Hub

Every deck, document, and research report Studio's specialists have produced for any department. The actual data lives in each requesting dept's own list (e.g. /sites/Engineering/Lists/SlideDecks); this page projects the union via the V7 7.3 CrossSiteList web part. Click a row's source link to jump to the dept that owns it.

Source	ID	Title	CreatedAt	Author	Body	DeckTopic	DeckAudience	DeckSlideCount	DeckOutline	DeckExportUrl
Engineering	87f838a097594899b528cde9c581ddd6	Rental-Ops Co-Pilot				AI for Belgian landlords: rent reconciliation, arrears, and legal dossier automation	Customer	8	# Rental-Ops Co-Pilot - Automate rent reconciliation & arrears chasing - Generate court-ready dossiers (NL/FR, GDPR) # The Problem - Manual reconciliation: 10+ hours/week per landlord - Legal path: 6-8 weeks to court dossier # Why Now - Smovin: 500+ paying landlords (Belgium) - PropTech-AI growth: 176% YoY # The Solution - Auto-reconcile rent vs. payment - Generate verzoekschrift -> court dossier # Why Us - Built by Belgian landlords (law depth) - Warm distribution: 500+ active Smovin users # Pricing - B2B: €99-299/office/month - Or €49-99/dossier (pay as you go) # 30-Day Plan - Talk to 5 key agency buyers - Deliver concierge proof & package # The Ask - One agency: 3-month paid design-partner pilot - Co-build to validate & scale

Across 2 source sites.

Source	ID	Title	CreatedAt	Author	Body
Demo__Docs	demo-doc-security	Security guidelines (demo)	2026-10-02T12:44:51.6774515+00:00	System Account (SPICE.Web)	Lock your screen, use the password manager, report anything odd to IT.
Demo__Docs	demo-doc-travel	Travel policy (demo)	2026-10-02T12:44:51.6524147+00:00	System Account (SPICE.Web)	Book economy for trips under six hours. Claim within 30 days with receipts.
ProductCatalog	doc-datasheet-bluesolar-monocrystalline-panels-en-4d9fd761	Datasheet-BlueSolar-Monocrystalline-Panels-EN.pdf	2026-09-24T17:33:44.4375296+00:00	worker-69	[page 1] www.victronenergy.com Victron Energy B.V. | De Paal 35 | 1351 JG Almere | The Netherlands E-mail: sales@victronenergy.com www.victronenergy.com • Low voltage-temperature coefficient enhances high-temperature operation. • Exceptional low-light performance and high sensitivity to light across the entire solar spectrum. • 25-Year limited warranty on power output and performance. • 5-Year limited warranty on materials and workmanship. • Sealed, waterproof, multi-functional junction box gives high level of safety. • High performance bypass diodes minimize the power drop caused by shade. • Advanced EVA (Ethylene Vinyl Acetate) encapsulation system with triple-layer back sheet meets the most stringent safety requirements for high-voltage operation. • A sturdy, anodized aluminium frame allows modules to be easily roof-mounted with a variety of standard mounting systems. • Highest quality, high-transmission tempered glass provides enhanced stiffness and impact resistance. • High power models with pre-wired quick-connect system with MC4 (PV-ST01) connectors. BlueSolar Monocrystalline Panels BlueSolar Monocrystalline 305W Article Number Description Net Weight Electrical data under STC (1) Nominal Power Max-Power Voltage Max-Power Current Open-Circuit Voltage Short-Circuit Current PMPP VMPP IMPP Voc Isc Kg W V A V A SPM040201200 (2) 20W-12V Mono 440 x 350 x 25mm series 4a 1.9 20 18.5 1.09 22.6 1.19 SPM040301200 (2) 30W-12V Mono 560 x 350 x 25mm series 4a 2.2 30 18.7 1.61 22.87 1.76 SPM040401200 40W-12V Mono 425 x 668 x 25mm series 4a 3.1 40 18.3 2.19 22.45 2.40 SPM040551200 (2) 55W-12V Mono 545 x 668 x 25mm series 4a 4 55 18.8 2.94 22.9 3.22 SPM040901200 (2) 90W-12V Mono 780 x 668 x 30mm series 4a 6.1 90 19.6 4.59 24.06 5.03 SPM041151202 (2) 115W-12V Mono 1030 x 668 x 30mm series 4b 8 115 19.0 6.04 23.32 6.61 SPM041301200 (2) 130W-12V Mono 1200 x 668 x 30mm series 4a 9.1 130 18.64 6.98 22.83 7.35 SPM041401200 (2) 140W-12V Mono 1250 x 668 x 30mm series 4a 9 140 19.4 7.22 23.6 8.05 SPM041501200 150W-12V Mono 1485 x 668 x 30mm series 4a 11 150 18.2 8.25 22.3 8.69 SPM041751200 (2) 175W-12V Mono 1485 x 668 x 30mm series 4a 11 175 19.4 9.03 23.7 9.89 SPM041851200 (2) 185W-12V Mono 1485 x 668 x 30mm series 4a 11 185 19.68 9.41 24.11 9.91 SPM042152402 (2) 215W-24V Mono 1580 x 705 x 35mm series 4b 11,7 215 40.1 5.36 46.01 5.65 SPM043052002 (2) 305W-20V Mono 1658 x 1002 x 35mm series 4b 19 305 32.5 9.38 39.7 10.27 SPM043602402 (2) 360W-24V Mono 1980 x 1002 x 40mm series 4b 23 360 38.4 9.38 47.4 10.24 Module SPM 040201200 SPM 040301200 SPM 04040120 SPM 040551200 SPM 040901200 SPM 041151202 SPM 041301200 SPM 04140120 SPM 041501200 SPM 041751200 SPM 041851200 SPM 042152402 SPM 043052002 SPM 043602402 Nominal Power (±3% tolerance) 20W 30W 40W 55W 90W 115W 130W 140W 150W 175W 185W 215W 305W 360W Cell type Monocrystalline Number of cells in series 36 72 60 72 Maximum system voltage 1000V Temperature coefficient of MPP (%) -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C -0.45/°C Temperature coefficient of Voc (%) -0.35/°C -0.35/°C -0.35/°C -0.35/°C -0.35/° C -0.35/°C -0.35/°C -0.35/°C -0.35/°C -0.35/°C -0.35/°C -0.35/°C -0.35/°C -0.35/°C Temperature coefficient of Isc (%) +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C +0.04/°C Temperature Range -40°C to +85°C Surface Maximum Load Capacity 200 kg/m² Allowable Hail Load 23 m/s, 7.53 g Junction Box Type PV-LH0805 PV-LH0806 PV-LH0801 PV-LH0808 PV-LH0808-1 PV-LH0808 PV-LH0808-1 PV-LH0701 PV-LH0808 PV-LH0701 PV-JB002 Length of Cables / Connector Type No cable 900 mm MC4 Output tolerance +/-3% Frame Aluminium Product warranty 5 years Warranty on electrical performance 10 years 90% + 25 years 80% of power output Smallest packaging unit 1 panel Quantity per pallet 380 260 200 140 72 72 36 48 48 42 48 42 42 37 1) STC (Standard Test Conditions): 1000 W/m2, 25ºC, AM (Air Mass) 1.5 2) Discontinued, see current models datasheet
ProductCatalog	doc-rng-175db-h-g2-datasheet-5818a99e	rng-175db-h-g2-datasheet.pdf	2026-09-24T17:31:53.1030331+00:00	worker-69	[page 1] Renogy | www.renogy.com | support@renogy.com | 909-287-7111 5050 S. Archibald Ave, Ontario, CA 91762 The Renogy 175 Watt 12 Volt Flexible Monocrystal- line Solar Panel is the most convenient panel to convert your hourse from an energy dependant home to an energy producing location. Power Output Warranty Material and Workmanship Warranty High module conversion efficiency Top ranked PTC rating Quick and inexpensive mounting 100% EL testing on all Renogy modules No hot spots guaranteed 175W Flexible Monocrystalline Solar Panel Key Features 25 Potential Uses The Renogy 175 Watt 12 Volt Flexible Monocrystal- line Panel can be primarily used on off-grid applica- tions that include rooftop, RV, boats and any curvy surfaces. Years 5 Years RNG-175DB-H [page 2] *All specifications and data described in this data sheet are tested under Standard Test Conditions (STC - Irradiance: 1000W/m2 , Temperature: 25 O C, Air Mass: 1.5) and may deviate marginally from actual values. Renogy and any of its affiliates has reserved the right to make any modifications to the information on this data sheet without notice. It is our goal to supply our customers with the most recent information regarding our products. These data sheets can be found in the downloads section of our website, www.renogy.com Renogy | www.renogy.com | support@renogy.com | 909-287-7111 5050 S. Archibald Ave, Ontario, CA 91762 Voltage (V) Current (A) Power (W ) RNG-175DB-H Characteristics Versus Voltage 175 W 19.5 V 8.98 A 23.9 V 9.50 A 600 VDC UL 17.3 % 15 A Optimum Operating Voltage (Vmp) Maximum Power at STC* Optimum Operating Current (Imp) Open Circuit Voltage (Voc) Short Circuit Voltage (Isc) Module Efficiency Maximum System Voltage Maximum Series Fuse Rating 175W Flexible Monocrystalline Solar Panel Electrical Data Mechanical Data Thermal Characteristics MC4 Connectors Junction Box Certifications Module Diagram IV-Curve -40ºC - 85ºC (-40°F-185°F) 45±2ºC -0.42%/°C -0.31%/°C 0.05%/°C OperatingTemp.Range Nominal Operating Cell Temerature (NOCT) Temperature Coefficient of Pmax Temperature Coefficient of Voc Temperature Coefficient of Isc IP 68 2 Diode(s) 12 AWG 700mm (27.6in) IP Rating Number of Diodes Output Cables 30A 1000V DC 10 AWG -40ºC-90ºC(-40°F-194°F) IP 67 Rated Current Maximum Voltage Maximum AWG Size Range Temperature Range IP Rating Solar Cell Type Number of Cells Dimensions Weight Application Class Frame Connectors Fire Performance Type 1 Solar Connectors None Class A 1504 x 673 x 2 mm(59.2 x 26.5 x 0.1 in 2.8 kg(6.2 lbs) ) 36 (4 x 9) Monocrystalline (6.2 x 6.2 in) [700 mm] 27.6 in [700 mm] 27.6 in [2 mm] 0.08 in [20 mm] 0.8 in RNG-175DB-H [1504 mm] 59.2 in [673 mm] 26.5 in [752 mm] 29.6 in [752 mm] 29.6 in

Across 28 source sites.

Source	ID	Title	CreatedAt	Author	DeckTopic	ResearchFindings	ResearchKeyClaims	ResearchCitations	ResearchConfidence
ProductCatalog	catalog-renogy-rng-175db-h	Catalog research: Renogy RNG-175DB-H	2026-09-24T14:40:38.6856966+00:00	System Account (SPICE.Web)	Camper and boat solar panels (flexible): Renogy RNG-175DB-H 175 W 12 V flexible monocrystalline	# Renogy RNG-175DB-H (175 W 12 V flexible monocrystalline) Two Renogy datasheet revisions exist: [1] (newer layout, Renogy 2775 E. Philadelphia St address, cell efficiency 21.0%, cable 450/150 mm, 20 A fuse, fire rating Class C) and [2] (older layout hosted by ENF as 'rng-175db-h-g2-datasheet.pdf', 5050 S. Archibald Ave address, module efficiency 17.3%, 700 mm cables, 15 A fuse, fire performance Type 1). Electrical STC values are identical in both. Note: Imp is 8.98 A on both datasheets (not 8.89 A). Search snippets for Renogy's current US page show a different spec set (Vmp 20.3 V, Imp 8.75 A, Voc 24.9 V, Isc 9.65 A, cell efficiency 21.0%) - apparently a later generation; not verified on a datasheet, not used. ## Datasheet | Code | Feature | Value | Unit | Source | |---|---|---|---|---| | EF012463 | MPP power by STC | 175 | Wp | [1] p.2; [2] p.2 | | EF005094 | MPP voltage | 19.5 | V | [1] p.2; [2] p.2 | | EF005096 | MPP current | 8.98 | A | [1] p.2; [2] p.2 | | EF004891 | Open circuit voltage | 23.9 | V | [1] p.2; [2] p.2 | | EF004859 | Short-circuit current | 9.50 | A | [1] p.2; [2] p.2 | | EF004896 | Power tolerance | | % | | | EF009509 | Module efficiency factor (STC) | 17.3 | % | [2] p.2 'Module Efficiency'; [1] p.2 states only 'Cell Efficiency 21.0%' | | EF004993 | Max. system voltage | 600 | V | [1] p.2 '600 VDC'; [2] p.2 '600 VDC UL' | | EF009510 | Reverse current load | 20 | A | [1] p.2 'Maximum Series Fuse Rating 20 A'; CONFLICT: [2] p.2 states 15 A | | EF012467 | Temperature coefficient Pmpp | -0.42 | %/K | [1] p.2; [2] p.2 (printed as %/degC) | | EF012466 | Temperature coefficient Uoc | -0.31 | %/K | [1] p.2; [2] p.2 (printed as %/degC) | | EF012465 | Temperature coefficient Isc | 0.05 | %/K | [1] p.2; [2] p.2 (printed as %/degC) | | EF002393 | Operating temperature | -40 to +85 | degC | [1] p.2; [2] p.2 | | EF005443 | Cell material | Monocrystalline (6.14 x 6.14 in cells) | | [1] p.2 | | EF008497 | Number of cells | 36 (4 x 9) | | [1] p.2; [2] p.2 | | EF027462 | Solar cell technology | | | | | EF018603 | Module structure | | | | | EF027461 | Module design | | | | | EF022700 | With frame | no | | [1] p.2; [2] p.2 'Frame: None' | | EF021568 | Frame colour | | | | | EF017059 | Cell colour | | | | | EF017058 | Colour back side | | | | | EF012646 | Glass with anti-reflection coating | | | | | EF012619 | Suitable for vertical/overhead glazing | | | | | EF001438 | Length | 1504 | mm | [1] p.2; [2] p.2 | | EF000008 | Width | 673 | mm | [1] p.2; [2] p.2 | | EF000125 | Thickness | 2 | mm | [1] p.2; [2] p.2 | | EF000167 | Weight | 2.8 | kg | [1] p.2; [2] p.2 | | EF005036 | With connection cable | yes | | [1] p.2 'Output Cables 12 AWG' | | EF000536 | Cable length | 450 (negative) / 150 (positive) | mm | [1] p.2; CONFLICT: [2] p.2 states 12 AWG 700 mm | | EF012464 | Number of bypass diodes | 2 | | [1] p.2; [2] p.2 | | BusbarCount | Busbar count | | | | | NmotC | NMOT | 45 +/- 2 (NOCT, not NMOT) | degC | [1] p.2; [2] p.2 | | Certifications | Certifications | CE; ISO 9001 (quality management system); 'Module Application: Class A Quality Control Verified' mark. No IEC 61215 / IEC 61730 / UL 61730 listed. | | [1] p.2; [2] p.2 shows CE and ISO 9001 only | | DegradationText | Degradation | 25-year power output warranty; 5-year material and workmanship warranty. No first-year / yearly loss or 25-year percentage on the datasheet. | | [1] p.1; [2] p.1 | | PanelType | Mounting form | Flexible | | [1] p.1 '175W Flexible Monocrystalline Solar Panel' | | BendLimit | Bend limit | up to 248 degrees | | [1] p.1 'Extreme flexibility, bend up to 248 degrees' | | Walkable | Walkable | | | | | MechanicalLoad | Mechanical load | | | | | JunctionBoxConnector | Junction box and connector | Junction box IP68; MC4 connectors IP67, 30 A, 1000 VDC, max 10 AWG | | [1] p.2; [2] p.2 | ## Independent tests | Source | Measured | Rated | Method | URL | |---|---|---|---|---| | None found | | 175 Wp | No independent lab, magazine or owner-bought test that measured THIS model was found. A generic Renogy flexible-panel blog claims 80-90% of rating but names no model or method, so it is not entered. | | ## Customer reviews | Source | Rating | Scale | Count | Independence | URL | |---|---|---|---|---|---| | Best Buy listing (RNG-175DB-H-G2-US), reviews shown as from renogy.com | 4.8 | 5 | 21 | Vendor-hosted | https://www.bestbuy.com/site/renogy-flexible-solar-panel-175-watt-12-volt-monocrystalline-semi-flexible-bendable-black/6612687.p?skuId=6612687 | | KJ Outfitters (RNG-175DB-H-US) | none | 5 | 0 | Third-party customer | https://kjoutfitters.com/products/175-watt-12-volt-flexible-monocrystalline-solar-panel | | The Home Depot (RNG-175DB-H) | not read (HTTP 403) | | | Third-party customer | https://www.homedepot.com/p/reviews/Renogy-175-Watt-12-Volt-Extremely-Flexible-Ultra-Thin-and-Light-Weight-Monocrystalline-Solar-Panel-for-RVs-and-Boats-RNG-175DB-H/312717894/1 | | Lowe's (RNG-175DB-H) | not read (HTTP 403) | | | Third-party customer | https://www.lowes.com/pd/Renogy-Renogy-175-Watt-12-Volt-Flexible-Monocrystalline-Solar-Panel/1003131348 | The Best Buy figure was seen in a search-result snippet; the page itself timed out on fetch. ## Community Pros: - Light (2.8 kg) and 2 mm thin, mounts on curved RV/boat roofs [1]. - A German user review quoted on topratgeber24.de says the module delivers the specified 175 W through a charge controller with a 5 m cable (search snippet only) [9]. Cons and failures: - Delamination: a Wander The West owner reports one of two Renogy 175 W flexible panels (2021 install) failed with delamination while the other kept working [7]. - Reliability / withdrawal: Sportsmobile Forum and Wander The West threads report Renogy pulled its flexible panels from the market over the failure rate of a batch and refunded buyers (search snippets; thread pages returned HTTP 403) [8][7]. - Discontinued: KJ Outfitters lists RNG-175DB-H-US as discontinued/sold out; Best Buy lists the G2 as no longer available new [6][4]. - Hotspots: a DIY Solar Power Forum thread 'Hotspot meltdown Renogy panels?' exists, but the page was not readable (HTTP 403) and the model is not confirmed as this one [10]. - Sibling-model report, NOT this model: iRV2 thread on a Renogy 160 W flexible panel with delamination and water ingress after under four months [11]. - Junction box/connector failure, cracked cells from bending/walking, output well below rating: no report found for this model. ## Offers | Shop | Price | Currency | VAT | Shipping | Stock | Seen on | URL | |---|---|---|---|---|---|---|---| | Geizhals EU (price comparison) | no current offer | EUR | page notes 0% VAT rate for PV in Germany | | no offers listed | 2026-09-24 | https://geizhals.eu/renogy-rng-175db-h-a2929910.html | | Geizhals AT (price comparison) | no current offer | EUR | | | 0 offers | 2026-09-24 | https://geizhals.at/renogy-rng-175db-h-a2929910.html | | Renogy DE flexible collection | not listed (only 100 W, 200 W, 150 W CIGS flexible, all unavailable) | EUR | | | not listed | 2026-09-24 | https://de.renogy.com/collections/flexible-solarmodule | | KJ Outfitters (US) | sold out | USD | | | discontinued | 2026-09-24 | https://kjoutfitters.com/products/175-watt-12-volt-flexible-monocrystalline-solar-panel | ## Not found - EF004896 Power tolerance: not on either datasheet. - EF027462 Solar cell technology (PERC/TOPCon etc.): datasheet says only 'Monocrystalline'. - EF018603 Module structure: datasheet says only 'Ultra thin lamination'; resellers mention ETFE but the datasheet does not. - EF027461 Module design (full/half cell): not stated. - EF021568 Frame colour: not applicable, the panel has no frame (Frame: None). - EF017059 Cell colour: not stated in text. - EF017058 Colour back side: not stated. - EF012646 Anti-reflection glass: not stated (no glass mentioned). - EF012619 Vertical/overhead glazing suitability: not stated. - BusbarCount Busbar count: not stated. - Walkable Walkable: not stated by the manufacturer. - MechanicalLoad Mechanical load: not on the datasheet. Reseller copy (citimarinestore.com [5], topratgeber24.de via search snippet [9]) claims wind 2400 Pa / snow 5400 Pa - not an IEC 61215 test statement, not entered. - Degradation percentages: only reseller pages (kjoutfitters.com [6], citimarinestore.com [5]) state 95% at 5 years / 90% at 10 years / 80% at 25 years; not on the datasheet, not entered. - Independent test of this model: none found. - EU offer price: no current EU shop offer found.	Imp is 8.98 A on both Renogy datasheet revisions (Vmp 19.5 V, Voc 23.9 V, Isc 9.50 A, 175 Wp STC); 36 cells, 2 bypass diodes, 1504 x 673 x 2 mm, 2.8 kg. The datasheet lists only CE and ISO 9001 - no IEC 61215/61730 certification and no mechanical load rating. The model is discontinued (US resellers sold out, no EU offer found) and owners report delamination; Renogy reportedly pulled its flexible panels over batch failure rates.	[1]: Renogy RNG-175DB-H datasheet (newer revision, hosted by nohma.com) - https://nohma.com/content/uploads/2021/09/Renogy-175W-Monocrystalline-12V-Flexible-Solar-Panel-Datasheet.pdf [2]: Renogy RNG-175DB-H datasheet (older revision, hosted by ENF) - https://cdn.enfsolar.com/z/pp/2024/4/n8a1a66ate4o3gb/rng-175db-h-g2-datasheet.pdf [3]: Renogy US 175W flexible product page - https://www.renogy.com/pages/175-watt-monocrystalline-solar-flexible-panels-rng-175db-h-html [4]: Best Buy Renogy 175W flexible RNG-175DB-H-G2-US - https://www.bestbuy.com/site/renogy-flexible-solar-panel-175-watt-12-volt-monocrystalline-semi-flexible-bendable-black/6612687.p?skuId=6612687 [5]: Citimarine Store RNG-175DB-H - https://citimarinestore.com/en/renogy-solar-panels/9409-renogy-175-watt-12-volt-flexible-monocrystalline-solar-panel-rng-175db-h.html [6]: KJ Outfitters discontinued 175W flexible - https://kjoutfitters.com/products/175-watt-12-volt-flexible-monocrystalline-solar-panel [7]: Wander The West - Ultimate Flexible Solar Panels, successes and warranty replacements - https://www.wanderthewest.com/threads/ultimate-flexible-solar-panels-successes-and-warranty-replacements.10808/page-5 [8]: Sportsmobile Forum - Renogy pulled their flexible panels from the market - https://www.sportsmobileforum.com/threads/renogy-pulled-their-flexible-panels-from-the-market.748973/ [9]: topratgeber24.de - Renogy 175W 12V flexibles Solarpanel - https://www.topratgeber24.de/solarpanel-flexibel/renogy-175w-12v-solarpanel-flexibles-monokristallines-solarmodul-silizium-solarzelle [10]: DIY Solar Power Forum - Hotspot meltdown Renogy panels? - https://diysolarforum.com/threads/hotspot-meltdown-renogy-panels.32212/ [11]: iRV2 - Renogy Flexible Solar Panel FAIL - https://www.irv2.com/threads/renogy-flexible-solar-panel-fail.1880851/ [12]: Geizhals EU RNG-175DB-H - https://geizhals.eu/renogy-rng-175db-h-a2929910.html [13]: Geizhals AT RNG-175DB-H - https://geizhals.at/renogy-rng-175db-h-a2929910.html [14]: Renogy DE flexible solar modules - https://de.renogy.com/collections/flexible-solarmodule	Medium
LlmRadar	rnd-src-3e66e28f9c0d4a0d844c1d49b630a1d3	R&D: Qwen3.8 Max Prime	2026-09-24T09:57:59.4947899+00:00	System Account (SPICE.Web)	Qwen3.8 Max Prime	## Summary Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. It accepts text, image, and video inputs. ## Fit for SPICE The model has a 1,000,000 context window, which is significantly larger than the current providers (Anthropic Claude: 200,000; DeepSeek: 163,840). However, its pricing is higher than the current offerings - $4/Mtok for input and $12/Mtok for output. Compared to our current providers: | Provider | Input ($/Mtok) | Output ($/Mtok) | Context (tokens) | |--------------|----------------|------------------|------------------| | Qwen3.8 Max Prime | 4 | 12 | 1,000,000 | | Anthropic Claude | 1 | 5 | 200,000 | | DeepSeek | 0.25 | 0.95 | 163,840 | This model could serve as a high-capacity fallback for tasks requiring extensive context windows, but it's not cost-effective for standard operations. ## Risks - The higher price point may not justify its use in cost-sensitive scenarios. - No evaluation data available in the ModelEvals ledger to assess performance. ## Proposal No change. The model's high price and lack of evaluation data make it unsuitable for immediate adoption. ## Effort 1 hour	Proposal: No change. Qwen3.8 Max Prime is not cost-effective compared to existing providers and lacks performance data for evaluation.	[1]: Qwen3.8 Max Prime - https://openrouter.ai/qwen/qwen3.8-max-prime	Low
LlmRadar	rnd-src-ccb258a91d7147e79d711b456ad6d3f5	R&D: inclusionAI: Ling 3.0 Flash Fin	2026-09-24T09:05:46.0712303+00:00	System Account (SPICE.Web)	inclusionAI: Ling 3.0 Flash Fin	## Summary Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for real-world investment applications and has a context window of 262,144 tokens. ## Fit for SPICE This model could serve as a cost-effective alternative for tasks requiring financial analysis or data processing within SPICE's current infrastructure. Compared to our current providers: | Provider | Input Price (USD/Mtok) | Output Price (USD/Mtok) | Context Window | |----------------|------------------------|-------------------------|----------------| | InclusionAI | 0.06 | 0.18 | 262,144 | | Anthropic | 0.15 | 0.30 | 200,000 | | DeepSeek | 0.08 | 0.16 | 128,000 | InclusionAI offers significantly lower pricing for both input and output tokens, with a larger context window than DeepSeek but smaller than Anthropic. ## Risks - Limited evaluation data available in our ModelEvals ledger - Financial focus may not translate well to other domains without additional training - Vendor stability and support availability unknown ## Proposal No change. While the pricing is attractive, there is insufficient eval evidence in our ledger to justify replacing current providers. ## Effort 1 hour	[ "Proposal: No change.", "InclusionAI's pricing is significantly lower than current providers but lacks sufficient evaluation data for a replacement decision." ]	[ "[1]: inclusionAI: Ling 3.0 Flash Fin - https://openrouter.ai/inclusionai/ling-3.0-flash-fin" ]	Low
LlmRadar	rnd-src-cdaf9b21dfdd446dbd9035723758f74c	R&D: Qwen3.8 Flash	2026-09-24T09:12:08.5485355+00:00	System Account (SPICE.Web)	Qwen3.8 Flash	## Summary Qwen3.8 Flash is Alibaba's Qwen-family flash-tier chat model, listed on OpenRouter with a 1,000,000-token context window and multimodal reasoning (text, vision, document/codebase analysis, chart analysis, long-video analysis). It supports agentic tool-use and desktop-interaction workflows. Released 2026-08-26, it is priced at $0.15 per million input tokens and $0.47 per million output tokens. Its SourceRating on our radar is Unrated - no independent scoring has landed on it yet. ## Fit for SPICE The closest slot is our chat+tools fallback, Provider.DeepSeek (Baseten / DeepSeek-V3.1, FallbackOrder=1), which is deliberately generic OpenAI-compatible and repointable via BaseUrl and Model. Side by side: Qwen3.8 Flash costs $0.15 in / $0.47 out per million tokens with a 1,000,000-token context; our LlmProvider parts carry no per-token price for DeepSeek or Anthropic, only BudgetUsd caps of $25 and $50, so a direct cost comparison is one-sided - we can state Qwen's own price and context, not that it beats what we run. Anthropic / claude-haiku-4-5 (FallbackOrder=2) is our only tool-loop-capable provider today; Qwen3.8 Flash's OpenRouter listing also advertises tool support, which would make it a second tool-loop candidate if verified in practice. ## Risks No ModelEvals row exists for this model, and none exists for DeepSeek-V3.1 either - the ten most recent rows in ResearchEnrichment/ModelEvals are all source-extraction evals against claude-haiku-4-5, so there is no head-to-head evidence on our own tasks for this proposal. The radar item's SourceRating is Unrated. It would run through OpenRouter, an added proxy hop and billing surface distinct from our two direct-vendor providers. The 1M-context and multimodal claims come from the vendor listing, not from anything verified against SPICE's actual text-heavy agent workloads. ## Proposal Trial Qwen3.8 Flash as a third fallback tier (a new Provider.QwenFlash LlmProvider part, FallbackOrder=3) behind DeepSeek and Anthropic, routed through OpenRouter's OpenAI-compatible endpoint. Its price and context are attractive enough for a bounded trial, but it should not be promoted ahead of Anthropic until it has earned at least one ModelEvals row of its own. ## Effort About 3 hours: register the LlmProvider part with an OpenRouter BaseUrl, Model and ActivationKey, wire FallbackOrder=3, and run one source-extraction eval to give it its first ModelEvals row.	Proposal: trial Qwen3.8 Flash as a third fallback tier (FallbackOrder=3) via OpenRouter, held below Anthropic until it earns a ModelEvals row. The 1,000,000-token context and $0.15 in / $0.47 out per-million-token pricing are the standout numbers on the listing. No ModelEvals evidence exists for Qwen3.8 Flash, and none exists for our own DeepSeek-V3.1 provider either - the ledger only covers claude-haiku-4-5. SourceRating is Unrated; the multimodal and agentic claims are vendor-stated, not independently verified against our workloads. Our LlmProvider parts carry no per-token pricing, only budget caps, so a direct cost comparison with DeepSeek or Anthropic is not possible from the spine alone.	[1]: Qwen: Qwen3.8 Flash - https://openrouter.ai/qwen/qwen3.8-flash [2]: LlmProvider parts SPICE runs today (Provider.Echo, Provider.DeepSeek, Provider.Anthropic) - Query.LlmProviders [3]: ModelEvals ledger, 10 most recent rows, all claude-haiku-4-5 - ListView('ModelEvals', site='ResearchEnrichment', limit='10')	Low
LlmRadar	rnd-src-c12916bcc4614c8a906ae8a3e0415cf3	R&D: Anthropic: Claude Fable 5.1 (batch)	2026-09-24T09:15:30.0652427+00:00	System Account (SPICE.Web)	Anthropic: Claude Fable 5.1 (batch)	## Summary Claude Fable 5.1 is an improved version of Claude Fable 5, offering enhancements in agentic coding, long-running workflows, and knowledge work tasks. It has a context window of 1,000,000 tokens. ## Fit for SPICE This model could serve the primary Anthropic provider slot (Provider.Anthropic). Compared to our current Claude Haiku 4-5 (based on ModelEvals ledger), Claude Fable 5.1 offers a much larger context window (1M vs not declared) and is priced at 5 USD/Mtoken for input and 25 USD/Mtoken for output. Our current provider's pricing is not declared in the briefing. ## Risks The primary risk is that while Claude Fable 5.1 offers a much larger context window, it's unclear how this translates into practical performance gains or if it's necessary for our typical tasks. There's also no data on output pricing or performance metrics beyond context window and input price. ## Proposal No change. While Claude Fable 5.1 has better context window, the lack of declared pricing and evaluation data for the current provider makes a direct comparison impossible. The improvement is not sufficient to warrant a switch without further data. ## Effort 2 hours	Proposal: No change to current Anthropic provider due to lack of comparative pricing and eval data. Claim two: Claude Fable 5.1 offers larger context window but no performance metrics provided.	[1]: Anthropic: Claude Fable 5.1 (batch) - https://openrouter.ai/anthropic/claude-fable-5.1:batch	Low
LlmRadar	rnd-src-f780980561524611b38871198fb80f41	R&D: Google: Gemini 3.8 Flash	2026-09-24T09:24:34.7350894+00:00	System Account (SPICE.Web)	Google: Gemini 3.8 Flash	## Summary Gemini 3.8 Flash is Google's newest Flash model, positioned as the most intelligent among Flash series models. It shows notable improvements over version 3.7 Flash in software engineering, agentic tasks, and multi-step reasoning capabilities. ## Fit for SPICE Gemini 3.8 Flash could serve as a high-intelligence fallback for tool-loop agent tasks where cost is less critical than performance. Compared to our current Anthropic Claude (PriceInPerMTok=1, ContextWindow=200000), Gemini 3.8 Flash offers significantly more context (1048576 vs 200000) and potentially better reasoning but at a higher input price (0.75 vs 1). For the default chat fallback role, it is not competitive due to higher cost. ## Risks The model's performance gains may not translate into practical improvements for SPICE’s specific use cases without further evaluation in our tasks. Also, no eval data exists in the ModelEvals ledger for this model. ## Proposal No change. The current Anthropic Claude provider is sufficient for default chat fallback and tool-loop tasks; Gemini 3.8 Flash does not offer a compelling cost-performance trade-off compared to existing providers. ## Effort 1 hour	Proposal: No change to current providers. Gemini 3.8 Flash offers better context and reasoning but higher cost than existing Anthropic Claude provider.	[1]: Google: Gemini 3.8 Flash - https://openrouter.ai/google/gemini-3.8-flash	Medium
ResearchEnrichment	llm-screening-tooluse-2026-10-03	LLM screening: tool use for the map copilot (2026-10-03)	2026-10-03T19:52:05.8672351+00:00	claude-code	LLM tool-use screening	# LLM screening: tool use for the map copilot (2026-10-03) Probe: `tools/llm-toolcall-probe.py` (the copilot's real tool shape, tool_add_nodes). Rerun it to screen a new model. | Model | Source | Calls the tool | Right arguments | Titles | Seconds | |---|---|---|---|---|---| | chat-local | gateway | yes | yes | 10 | 4 | | smart-local | gateway | yes | yes | 10 | 4 | | fast-local | gateway | yes | yes | 11 | 3 | | qwen3-coder:30b-aw | Ollama .69 | yes | yes | 5 | 2 | | qwen3-a3b | gateway | error 400 | - | - | - | ## Findings - Every working model calls the tool correctly when the instruction is short and the tool is clear. **The copilot failed because of its setup**, not the model: about 22,000 tokens of briefing and an explore-first instruction made it read and search instead of acting. With "adding comes first" the same free model added the nodes in one call (4,400 tokens). - **The wording decides the depth:** without "write the list from your own knowledge" every model added ONE title; with it, 5 to 11. - Models pass ids back in their own citation form (`node:node-x`); tools must accept that (fixed: ListRows.NodeId). - In a live run the first `web_search` and `web_fetch` calls failed on bad arguments before the model recovered - a cost of about two turns. - qwen3-a3b answers 400 on the gateway: a configuration issue for the steward. ## To screen next Multi-step jobs (search, fetch, answer with a URL), argument-shape errors per model, tokens per answer, and DeepSeek's API once its key is reachable.	All working local models call tool_add_nodes correctly; the copilot failed on its own briefing size and explore-first instruction; the instruction wording sets the depth (1 vs 5-11 titles).		High
ResearchEnrichment	staff-what-works-2026-10-02	Agent staffing, hiring, risk-based approval and JIT context: what works and why (STAFF)	2026-10-02T16:13:54.3002873+00:00	claude-code	Agent staffing, hiring, risk-based approval and JIT context	Question (STAFF/INTAKE, 2026-10-02): what works in practice for an agent company to start with default staff, hire when needed and retire idle hires, approve by risk and mode, hand approvals to a live steward, and give each agent just-in-time context? Not repeated: who is on a core team (gov-core-team-frameworks-2026-09-30). Hiring. Paperclip's hire is a data row plus a human gate: an agent files an approval of type hire_agent with rationale, proposed config and estimated monthly cost; approval creates the agent, issues its key, adds it to the org chart; until then it sits pending_approval with heartbeat off, and every hire is in the activity stream [1][2]. Each agent has a monthly token budget under a company cap, pauses at the cap, resets on the 1st; agents run in short heartbeats from a mechanical checklist [1][3]. Capability descriptions are what keep roles from duplicating; retirement is not documented [3]. Sprawl is the real failure: one LangChain system spent 47K USD in 11 days because each layer fanned out on its own; Claude Code now caps 20 concurrent, 200 per session, depth 3 [4], and a depth cap alone misses horizontal growth [5]. Over-decomposition is the common mistake; AutoGen loops need reply and token ceilings [6]. Delegating "as a tool" keeps the requester accountable, unlike a full handoff [7]. Scale-to-zero on an empty queue retires workers still running activities - retire on no assignments AND no runs [8][9]. Roles before persons. Project Server's generic resource is a role placeholder replaced by a named resource from the pool at assignment [10]. On-call is a schedule plus an escalation chain with timeouts, not a person [11]. Risk-based approval. ITIL: standard changes are pre-authorised; sending all of them to a CAB is an anti-pattern; normal changes route by risk; emergency changes go to an on-call approver and are documented after [12]. DORA: external approval does not lower change failure rate - teams relying on it were 2.6 times more likely to be low performers; peer review plus automation instead [13]. GitHub derives the approver from what is touched (CODEOWNERS) and auto-merges only when every rule is met [14]. Let a small model grade risk and a deterministic table decide (inference, no source). External steward. GitHub environments: required reviewers, or an external app that receives the protection-rule webhook and calls back approve or reject; an unapproved job fails after 30 days [15]. The wait is durable state, resumed by the verdict; default-deny on timeout, the audit outside the agent's own transcript [16]. Just-in-time context. Agent Skills: name and description preloaded, the body when relevant, linked files on demand [17]. Context rot: the smallest set of high-signal tokens, references loaded at runtime, compaction and notes [18]. Deferred tool loading cut 50+ tools from about 77K to 8.7K tokens [19]. Parallel subagents without shared decisions conflict [20][21]. Ideas for SPICE, the SharePoint way: (1) a standard change = an auto-approve rule in a DecisionTable plus an audit row (INTAKE.12); (2) a hire = an ApprovalRequest whose approval provisions the seat or actor (STAFF.3); (3) three caps as thresholds - hires per site, delegation depth, monthly spend (Scope.Staffing); (4) role before person - a Directory SiteGroup is the generic resource, the named agent bound at assignment (SeatPool); (5) retire on no assignments and no runs for N days, archive never delete; (6) the steward = an escalation chain with a due date and default-deny expiry, reached over the hub (INTAKE.12); (7) a verdict callback that resumes the waiting workflow - the Workflow Task step already parks on rows (TPL.17); (8) a hire request names the capability gap and is checked against existing roles; (9) Jev grades risk, the table decides; (10) a briefing is an index plus pointers, bodies fetched on demand (STAFF.1 made the knowledge slots resolve). Sources: [1] https://paperclipai-paperclip.mintlify.app/guides/hiring-agents [2] https://docs.paperclip.ing/guides/org/agents/ [3] https://dev.to/truongpx396/paperclip-deep-dive-a-build-guide-for-an-ai-company-control-plane-dda [4] https://www.digitalapplied.com/blog/claude-code-subagent-depth-limits-budget-caps-2026 [5] https://github.com/nicobailon/pi-subagents/issues/239 [6] https://www.zenml.io/blog/crewai-vs-autogen [7] https://www.matthewswong.com/en/blog/openai-agents-sdk-handoffs-vs-agents-as-tools/ [8] https://github.com/kedacore/keda/issues/7368 [9] https://temporal.io/blog/announcing-keda-based-auto-scaling-for-temporal-workers [10] https://mpug.com/how-to-replace-generic-resources-with-named-resources [11] https://support.pagerduty.com/main/docs/escalation-policies [12] https://blog.invgate.com/what-are-the-itil-change-categories [13] https://dora.dev/capabilities/streamlining-change-approval/ [14] https://github.com/orgs/community/discussions/190610 [15] https://docs.github.com/en/actions/how-tos/deploy/configure-and-manage-deployments/control-deployments [16] https://www.decryptiondigest.com/blog/human-in-the-loop-approval-gates-agentic-ai-tool-calls [17] https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills [18] https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents [19] https://code.claude.com/docs/en/agent-sdk/tool-search [20] https://cognition.com/blog/dont-build-multi-agents [21] https://x.com/walden_yan/status/2047054554433462360	A hire is an approval row that provisions the agent (Paperclip); sprawl is the real failure - cap hires, depth and spend; roles before persons (generic resources); external change approval does not lower failure rates (DORA) - auto-approve standard changes with an audit row; the steward is an escalation chain with default-deny expiry; a briefing is an index plus pointers, bodies on demand.		Medium
ResearchEnrichment	gov-core-team-internal-map-2026-09-30	Core agent team: what SPICE already declares - actors, agencies, seats, RACI, routines, governance (GOV)	2026-09-30T16:03:46.6285681+00:00	claude-code	Core agent team for running the site collection	Question (GOV, 2026-09-30): what does SPICE already declare about roles, seats and delegation, before proposing a core team that runs the site collection like a company. Actors: 41 ActorProfile parts in the served spine; Hub/Actors (about 60 rows) is the live truth (ActorResolver reads the row first). Rows have drifted from parts (EngineeringLead 4 vs 12 skills, ComplianceOfficer 2 vs 8, ...); about 16 rows have no part. Agencies: Fidelity, Genesis (with the Mission manifesto), Content, Research, ResearchEnrichment, Wisdom, Academy, Strategy, PartsFactory. No agency and no top-level lead for the enterprise departments (Operations, HR, Finance, Compliance, Procurement, Engineering, Approvals). Workflows do not run as actors: a phase binds to a skill through the department roster (manifest PartRef Role + Feature BindSkill + an earned fallback). Scope.GeneratedOperator invents a dept Operator/Deputy where none is named. Seats (Directory/People): claude-code (Available), worker-69 (Offline), four Claude Code subagent seats, spice-architect (empty shell). Manager, Department, Responsibilities, OutOfScope empty on every row; one pool: Research workers = worker-69 + claude-code. RACI: ContentType.Responsibility; Query.MyResponsibilities is hard-wired to Engineering/Responsibilities; Compliance/Responsibilities is empty; no RACI row covers any Schedule or Workflow. Routines: Schedules exist (Research 06:30, DailyBrief 06:35, Watchdog every 15 min, WatchSignals 07:00, weekly/monthly reports); Scope.CrewStandingOrders holds 15 standing orders but Scope.Crew is disabled. Governance: approval skills held by ApprovalsSteward and the department officers; PolicyRules gate storage writes, release, self-review, agency birth, procedure activation; no Owners members on any Governance site; nobody named to approve. Gaps: no link between a seat and an actor; no reporting line; five agency actors hold no skills; SteeringMeeting/PlanningMeeting/Adjudicate cannot run (unbound skills, no schedule); VentureEngine's daily watch likely cannot run (ScoutSignals bound only on Engineering); every FallbackSeat ends at claude-code; standing orders disabled; OodaLoop app disabled.	Hub/Actors rows, not parts, are the live actor truth. No seat is linked to an actor; no reporting line exists. Every fallback ends at claude-code; the only other pool member is offline. Standing orders are declared but disabled. Nobody is named to approve on the Governance hub.	SPICE.Web/Config/Parts.xml; SPICE.Web/Config/SAF-Site-Manifest.xml; SPICE.Foundation/Actors/ActorResolver.cs:9-28; SPICE.Foundation/PhaseOrchestrator.cs:100-140; SPICE.Foundation/Workflows/SeatPool.cs; live rows Hub/Actors, Directory/People, Engineering/Responsibilities	High
ResearchEnrichment	gov-core-team-frameworks-2026-09-30	Core agent team: how Paperclip, SharePoint, MetaGPT, CrewAI, Magentic-One and others run an agent company (GOV)	2026-09-30T15:47:14.7910366+00:00	claude-code	Core agent team for running the site collection	Question (GOV, 2026-09-30): what core agent team runs a company, and how do the leading frameworks organise delegation, control and cost? Convergent minimal core (found in 3+ independent frameworks): 1. Orchestrator / manager / CEO - decomposes goals into tasks and assigns them (Paperclip CEO, MetaGPT PM, ChatDev CEO, CrewAI manager_agent, Magentic-One Orchestrator, Cognition coordinator Devin). 2. Specialist executors - produce the artifacts (engineer, coder, worker). 3. Reviewer / QA gate - checks output before it is accepted (MetaGPT QA, ChatDev reviewer+tester, Paperclip in_review status). 4. Human board approval - a person, not an agent, says yes (Paperclip approvals, Agent 365 sponsor, SharePoint Approvers group). 5. A registered identity per agent - a durable row: exists, reports to, costs (Paperclip agents table, Entra Agent ID, SharePoint group membership). Paperclip (the deepest model): no fixed titles in the engine - agents.reports_to builds one tree; CEO/CTO/CMO/CFO is only a starter template. Goals cascade company > team > agent > task; work items are issues; POST /issues/:id/checkout is an atomic claim (conditional update, never retry a 409); agents delegate by creating child issues with parentId + goalId; the parent wakes when children complete. Heartbeat protocol: wake, re-read identity and budget, check out ONE task, finish it in that wake, update status. One human board: approval types hire_agent, approve_ceo_strategy, budget_override_required; the board can pause, reassign, terminate. Budgets per company and per agent (monthly cents), cost_events drill down agent > project > company > issue/goal; soft alert, then hard auto-pause. SharePoint: Farm admin, Site collection admin, Site owner, Owners/Members/Visitors, Approvers/Designers (publishing), Term store admin/group manager, Hub site owner (set centrally). Content Organizer is a feature (routing rules), not a role; Records Manager is informal. Microsoft agents: Entra Agent ID per agent, Agent Identity Blueprints (disable the blueprint = disable every agent from it), a human sponsor per agent that transfers to the sponsor's manager when they leave (Agent 365 GA May 2026); Copilot Studio Facilitator (meeting actions) and Project Manager agent (Planner: goals > tasks > execution > report). Others: MetaGPT fixed SOP PM > Architect > Project Manager > Engineer > QA with documents as the handoff; ChatDev ChatChain waterfall; CrewAI hierarchical manager (allow_delegation, allowed_agents); Magentic-One orchestrator + WebSurfer/FileSurfer/Coder/Terminal with replanning on stalls; OpenAI Agents SDK handoffs; Anthropic orchestrator-workers; Cognition 'Manage Devins' map-reduce-and-manage with a Kanban command centre; Lindy per-function agents chaining structured data. Ideas to borrow, mapped to SPICE: - Goal ancestry on every task row (Paperclip) - ParentID chain on ProjectTasks up to a Goal/Program row. - Atomic checkout (Paperclip) - a seat claims a task by a conditional status move (Tool.AdvanceListItem arc Not Started > In Progress refused when already claimed). - Heartbeat: one task per wake, finish it, report (Paperclip) - the seat loop on my-{seat}/Tasks; GOV.5 start/finish events. - Separate approval types: hire, strategy, budget (Paperclip) - an ApprovalType choice on ApprovalRequests. - Budget per seat with soft alert and hard pause (Paperclip) - a budget column on the People row, the fuel board as the cost_events feed. - A human sponsor per agent with manager fallback (Entra) - People.Manager already exists on the seat row. - Blueprint class with kill switch (Agent 365) - a seat template; disabling it stops every seat from it. - One agent owns an artifact end to end (Cognition) - never two seats writing the same row or document. Anti-patterns documented: decision fragmentation across parallel agents (Cognition); endless loops and stalls, lost context across handoffs, cascading errors, role drift (Splunk); swarm cost blowups (Galileo, vendor claim, unverified); CrewAI manager over-delegating to every agent (community + TDS); role sprawl whose admin overhead exceeds the gain (vendor blog).	The five-role core (orchestrator, executors, reviewer, human board, registered identity) recurs across Paperclip, MetaGPT, ChatDev, CrewAI, Magentic-One and Microsoft Agent 365. Paperclip ships no fixed roles; CEO/CTO/CMO/CFO is a starter template over a reports_to tree. Paperclip gates three things by the human board: hiring an agent, the CEO strategy, a budget override. Microsoft Agent 365 (GA May 2026) gives each agent an Entra identity with a human sponsor that falls back to the sponsor's manager. Cognition argues against parallel agents making implicit decisions on the same artifact. Unverified: specific cost-blowup incidents (vendor claims only); Paperclip star count.	https://github.com/paperclipai/paperclip/blob/master/doc/SPEC-implementation.md https://github.com/paperclipai/paperclip/blob/master/docs/guides/agent-developer/heartbeat-protocol.md https://github.com/paperclipai/paperclip/blob/master/doc/PRODUCT.md https://paperclip.ing/ https://learn.microsoft.com/en-us/sharepoint/default-sharepoint-groups https://learn.microsoft.com/en-us/sharepoint/sites/determine-permission-levels-and-groups-in-sharepoint-server https://learn.microsoft.com/en-us/sharepoint/assign-roles-and-permissions-to-manage-term-sets https://learn.microsoft.com/en-us/entra/id-governance/agent-id-governance-overview https://techcommunity.microsoft.com/blog/agent-365-blog/what%E2%80%99s-new-in-agent-365-may-2026/4516340 https://techcommunity.microsoft.com/blog/plannerblog/power-up-project-management-in-teams-with-the-project-manager-agent/4454813 https://arxiv.org/pdf/2308.00352 https://arxiv.org/html/2307.07924v5 https://docs.crewai.com/en/learn/hierarchical-process https://towardsdatascience.com/why-crewais-manager-worker-architecture-fails-and-how-to-fix-it/ https://microsoft.github.io/autogen/stable//user-guide/agentchat-user-guide/magentic-one.html https://www.anthropic.com/research/building-effective-agents https://openai.github.io/openai-agents-python/multi_agent/ https://cognition.com/blog/dont-build-multi-agents https://cognition.com/blog/multi-agents-working https://www.lindy.ai/blog/ai-workforce https://relevanceai.com/docs/get-started/core-concepts/workforces https://www.splunk.com/en_us/blog/artificial-intelligence/multi-agent-system-failures.html https://galileo.ai/blog/why-multi-agent-systems-fail	High
ResearchEnrichment	row-f636c253582a4163b652a753b0b5ff15	Captains intel (2026-09-14)		System Account (SPICE.Web)	CaptainsIntel	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable)&#xA;&#xA;### Merged sources (6 raw hits across 3 engine(s) -&gt; 6 unique, ranked by cross-engine agreement)&#xA;&#xA;Building Reliable Agents with Memory and Compaction&#xA; https://developers.openai.com/cookbook/examples/agents_sdk/building_reliable_agents_memory_compaction&#xA; Building Reliable Agents with Memory and Compaction May 1, 2026 # Building Reliable Agents with Memory and Compaction WP EO Wesley Pasfield (OpenAI) , Emre Okcular (OpenAI) This Cookbook shows how to build an ... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw&#xA; https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026&#xA; Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw # Multi-Agent Orchestration Patterns: How to Coordinate 3+ AI Agents in Production Tijo Gaucher April 20, 2026· 18 min read One agent is a chatbot. Three ag... [1 engine(s): Exa]&#xA;&#xA;The best AI agent frameworks in 2026 - LangChain&#xA; https://www.langchain.com/resources/ai-agent-frameworks&#xA; The best AI agent frameworks in 2026 # The best AI agent frameworks in 2026 We reviewed 7 AI agent frameworks across orchestration, observability, and production readiness. See how LangGraph, CrewAI, Microsoft Agent Fr... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Orchestration | Orkas Blog&#xA; https://orkas.ai/blog/agent-orchestration/&#xA; Multi-Agent Orchestration | Orkas Blog # Multi-Agent Orchestration in Practice: How Orkas Runs a Lead Agent and Its Sub-Agents Orquestração multiagente na prática: como Orkas administra um agente líder e seus subagentes... [1 engine(s): Exa]&#xA;&#xA;Conductor Now Runs Agents Alongside Your Workflows, on the Same Durable Engine — Orkes&#xA; https://orkes.io/blog/conductor-now-runs-agents-alongside-your-workflows&#xA; Conductor Now Runs Agents Alongside Your Workflows, on the Same Durable Engine — Orkes # Conductor Now Runs Agents Alongside Your Workflows, on the Same Durable Engine Maria Shimkovska Content Engineer Last updated... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Portfolio Collaboration with OpenAI Agents SDK&#xA; https://developers.openai.com/cookbook/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration&#xA; # Multi-Agent Orchestration with OpenAI Agents SDK: Financial Portfolio Analysis Example &gt; For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending `.md` to... [1 engine(s): Exa]&#xD;&#xA;&#xD;&#xA;--- Fact-check ---&#xD;&#xA;(verification unavailable)&#xD;&#xA;&#xD;&#xA;Researched 1 source set(s) across 1 angle(s).&#xD;&#xA;Confidence: Low" bundle="WB-20260914210210-7216" mode="dual (same domain)" home="Agency.ResearchEnrichment" selected="2" filed="2" totalCostUsd="0,177638"><Answer agency="Agency.Wisdom" ceo="Actor.WisdomKeeper" provider="Echo" costUsd="0,088954" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Wisdom Keeper, Wisdom Keeper, in the Knowledge department, the entry CEO of Wisdom Library. Your goal: Each day, turn one fresh research source into a single sharp, reusable piece of knowledge - compound the city's second brain, never let a good insight evaporate. Backstory: A librarian-archivist at heart. Reuse-first: checks the existing knowledge before adding more, and writes the distilled wisdom so any agency can act on it. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable directly - concise, concrete, and u... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Building Reliable Agents with Memory and Compaction https://developers.openai.com/cookbook/examples/agents_sdk/building_reliable_agents_me…]]></Answer><Answer agency="Agency.Academy" ceo="Actor.TrainingMaster" provider="Echo" costUsd="0,088684" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Training Master, Training Master, in the Academy department, the entry CEO of Training Academy. Your goal: Prove the city's agents can actually use the city - score whether they discover and pick the right tool for a task, and surface where they fail so the registry or the agents improve. Backstory: A patient drill instructor. Believes a capability nobody can find is no capability at all - so the test is always 'could the agent discover and use it unaided?'. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable dire... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Building Reliable Agents with Memory and Compaction https://developers.openai.com/cookbook/examples/agents_sdk/building_reliable_agents_me…]]></Answer></DualDispatch>	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or…		Medium
ResearchEnrichment	row-1043cd5c7b4d410a806375a2b37c81f6	AI agent frameworks and LLM advances		System Account (SPICE.Web)	AI agent frameworks and LLM advances	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] comparative benchmarks for AI agent frameworks such as LangGraph and CrewAI OpenAI Anthropic Google agent SDK capabilities and pricing comparison security risks and real-world task failures in LLM agent evaluations --- Fact-check --- (verification unavailable) Researched 3 source set(s) across 3 angle(s). Confidence: Low	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] comparative benchmarks for AI agent frameworks such as LangGraph and CrewAI OpenAI Anthropic Google agent SDK capabilities and pricing comparison security risks and real-world task failures in LLM agent evaluations --- Fact-check --- (verification unavailable) Researched 3 source set(s)…		Low
ResearchEnrichment	row-4bc895a45a7f4a58a55e98d3127336b1	Captains intel (2026-09-14)		System Account (SPICE.Web)	CaptainsIntel	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable)&#xA;&#xA;### Merged sources (6 raw hits across 3 engine(s) -&gt; 6 unique, ranked by cross-engine agreement)&#xA;&#xA;Building Reliable Agents with Memory and Compaction&#xA; https://developers.openai.com/cookbook/examples/agents_sdk/building_reliable_agents_memory_compaction&#xA; Building Reliable Agents with Memory and Compaction May 1, 2026 # Building Reliable Agents with Memory and Compaction WP EO Wesley Pasfield (OpenAI) , Emre Okcular (OpenAI) This Cookbook shows how to build an ... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw&#xA; https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026&#xA; Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw # Multi-Agent Orchestration Patterns: How to Coordinate 3+ AI Agents in Production Tijo Gaucher April 20, 2026· 18 min read One agent is a chatbot. Three ag... [1 engine(s): Exa]&#xA;&#xA;The Agent Harness | Go Micro&#xA; https://go-micro.dev/docs/guides/agent-harness.html&#xA; The Agent Harness | Go Micro # The Agent Harness The first wave of agent frameworks solved one problem: put a model in a loop with some tools. The harder problem is operating that loop — and that’s what a harness is. ... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Orchestration | Orkas Blog&#xA; https://orkas.ai/blog/agent-orchestration/&#xA; Multi-Agent Orchestration | Orkas Blog # Multi-Agent Orchestration in Practice: How Orkas Runs a Lead Agent and Its Sub-Agents Orquestração multiagente na prática: como Orkas administra um agente líder e seus subagentes... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Portfolio Collaboration with OpenAI Agents SDK&#xA; https://developers.openai.com/cookbook/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration&#xA; # Multi-Agent Orchestration with OpenAI Agents SDK: Financial Portfolio Analysis Example &gt; For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending `.md` to... [1 engine(s): Exa]&#xA;&#xA;Conductor Now Runs Agents Alongside Your Workflows, on the Same Durable Engine — Orkes&#xA; https://orkes.io/blog/conductor-now-runs-agents-alongside-your-workflows&#xA; Conductor Now Runs Agents Alongside Your Workflows, on the Same Durable Engine — Orkes # Conductor Now Runs Agents Alongside Your Workflows, on the Same Durable Engine Maria Shimkovska Content Engineer Last updated... [1 engine(s): Exa]&#xD;&#xA;&#xD;&#xA;--- Fact-check ---&#xD;&#xA;(verification unavailable)&#xD;&#xA;&#xD;&#xA;Researched 1 source set(s) across 1 angle(s).&#xD;&#xA;Confidence: Low" bundle="WB-20260914204232-64be" mode="dual (same domain)" home="Agency.ResearchEnrichment" selected="2" filed="2" totalCostUsd="0,176513"><Answer agency="Agency.Academy" ceo="Actor.TrainingMaster" provider="Echo" costUsd="0,088121" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Training Master, Training Master, in the Academy department, the entry CEO of Training Academy. Your goal: Prove the city's agents can actually use the city - score whether they discover and pick the right tool for a task, and surface where they fail so the registry or the agents improve. Backstory: A patient drill instructor. Believes a capability nobody can find is no capability at all - so the test is always 'could the agent discover and use it unaided?'. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable dire... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Building Reliable Agents with Memory and Compaction https://developers.openai.com/cookbook/examples/agents_sdk/building_reliable_agents_me…]]></Answer><Answer agency="Agency.Wisdom" ceo="Actor.WisdomKeeper" provider="Echo" costUsd="0,088391" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Wisdom Keeper, Wisdom Keeper, in the Knowledge department, the entry CEO of Wisdom Library. Your goal: Each day, turn one fresh research source into a single sharp, reusable piece of knowledge - compound the city's second brain, never let a good insight evaporate. Backstory: A librarian-archivist at heart. Reuse-first: checks the existing knowledge before adding more, and writes the distilled wisdom so any agency can act on it. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable directly - concise, concrete, and u... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Building Reliable Agents with Memory and Compaction https://developers.openai.com/cookbook/examples/agents_sdk/building_reliable_agents_me…]]></Answer></DualDispatch>	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or…		Medium
ResearchEnrichment	row-903ff09dabfe41da84e59fcd77cfa0c5	AI agent frameworks and LLM advances		System Account (SPICE.Web)	AI agent frameworks and LLM advances	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] (synthesis unavailable) ### Merged sources (12 raw hits across 3 engine(s) -> 12 unique, ranked by cross-engine agreement) AI agent frameworks: LangChain vs CrewAI vs Autogen. A data comparison. | dataku https://dataku.ai/blog/ai-agent-frameworks-langchain-crewai-autogen-comparison AI agent frameworks: LangChain vs CrewAI vs Autogen. A data comparison. | dataku Contents Three frameworks. Five identical tasks. One very long weekend. I built the same agent workflows on LangChain, CrewAI, and Micro... [1 engine(s): Exa] Market Trends: Enterprise AI Agent Adoption https://www.verdantix.com/venture/report/market-trends--enterprise-ai-agent-adoption Abridge, Accenture, Amazon, Amazon Web Services (AWS), AMD, Benchmark Gensuite, Brickell Digital, C3 AI, CalypsoAI, CO2 AI, Cognite, Cosine AI, Credo AI, CrewAI, Cursor, Databricks, Deloitte, Devin AI, FedRAMP, Glean, Go... [1 engine(s): Tavily] LangChain vs CrewAI vs AutoGen: Which Agent Framework Fits Your Stack – Remery Blog https://remery.ai/blog/langchain-vs-crewai-vs-autogen-agent-frameworks LangChain vs CrewAI vs AutoGen: Which Agent Framework Fits Your Stack – Remery Blog Reviews 18 Jul 2025• 14 min read # LangChain vs CrewAI vs AutoGen: Which Agent Framework Fits Your Stack Three leading agent framewor... [1 engine(s): Exa] Enterprise AI Agent Adoption Market Size & Forecast 2026 - 2035 https://www.datamintelligence.com/research-report/enterprise-ai-agent-adoption-market August 2026 – Enterprise AI security becomes a major adoption focus: DXC Technology partnered with Primary to provide an AI-native Zero Trust platform designed to govern how AI agents and enterprise AI applications acces... [1 engine(s): Tavily] LangChain vs CrewAI vs AutoGen in 2026: Honest Comparison with Data https://agntdev.com/langchain-vs-crewai-vs-autogen-2026-honest-comparison/ \n\n\n\n LangChain vs CrewAI vs AutoGen in 2026: Honest Comparison with Data \n 📖 8 min read•1,600 words•Updated Mar 19, 2026 I pulled the GitHub API on March 18, 2026. Read through Reddit threads with a combined 1,50... [1 engine(s): Exa] Top AI Agent Frameworks in 2026: A Production-Ready Comparison https://pub.towardsai.net/top-ai-agent-frameworks-in-2026-a-production-ready-comparison-7ba5e39ad56d ## 5 Strategic Patterns Shaping AI Agent Frameworks in 2026 Beyond individual framework selection, five macro trends are reshaping how enterprises think about agent infrastructure: 1. Graph-based orchestration is the c... [1 engine(s): Tavily] LangChain vs CrewAI vs Autogen: A Practical Guide to ... https://medium.com/@data.ai.oliver/langchain-vs-crewai-vs-autogen-a-practical-guide-to-choosing-an-ai-agent-framework-a2d5de59b6c4 MediumLangChain vs CrewAI vs Autogen: A Practical Guide to Choosing an AI Agent Framework | by Oliver | Medium Sign up Get app Sign up ## Oliver I’m a Data & AI Freelancer with interest in using AI to turn raw data ... [1 engine(s): Exa] AI Agents for Enterprise: Platform Guide for 2026 | Jetruby https://jetruby.com/blog/enterprise-ai-agents The rapid growth in enterprise AI demand has created a crowded marketplace. Major cloud vendors provide agent frameworks. Startups offer agent orchestration platforms. Workflow companies integrate AI layers into automati... [1 engine(s): Tavily] AI Agent Framework Comparison for Production: LangChain vs CrewAI vs AutoGen vs Just Using the API | ClawAgora https://www.clawagora.com/en/blog/ai-agent-framework-comparison-langchain-crewai-autogen AI Agent Framework Comparison for Production: LangChain vs CrewAI vs AutoGen vs Just Using the API | ClawAgora # AI Agent Framework Comparison for Production: LangChain vs CrewAI vs AutoGen vs Just Using the API ClawAg... [1 engine(s): Exa] Enterprise AI agent trends: Top use cases, governance ... https://www.databricks.com/blog/enterprise-ai-agent-trends-top-use-cases-governance-evaluations-and-more • Databricks shares insights from 20,000+ global organizations in its new State of AI Agents report, including trends in the top AI use cases, agentic systems, and database transformations. • As AI investments deepen, ... [1 engine(s): Tavily] LangChain vs CrewAI vs AutoGen: Framework Comparison | ZTABS https://ztabs.co/blog/langchain-vs-crewai-vs-autogen LangChain vs CrewAI vs AutoGen: Framework Comparison | ZTABS TL;DR: A practical comparison of LangChain, CrewAI, and AutoGen for building AI agents. Covers architecture differences, code examples, performance benchmarks... [1 engine(s): Exa] AI Agent Adoption Guidance for Organizations - Cloud Adoption Framework | Microsoft Learn https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents This guidance provides a structured framework to help organizations successfully adopt AI agents as part of their broader AI adoption strategy. It addresses the unique considerations that AI agents introduce. The series ... [1 engine(s): Tavily] --- Fact-check --- (verification unavailable) Researched 3 source set(s) across 3 angle(s). Confidence: Low	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] (synthesis unavailable) ### Merged sources (12 raw hits across 3 engine(s) -> 12 unique, ranked by cross-engine agreement) AI agent frameworks: LangChain vs CrewAI vs Autogen. A data comparison. | dataku https://dataku.ai/blog/ai-agent-frameworks-langchain-crewai-autogen-comparison AI…		Low
ResearchEnrichment	row-2f560c3ae7dd49cc8c081e550b7e80f9	Captains intel (2026-09-07)		System Account (SPICE.Web)	CaptainsIntel	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable)&#xA;&#xA;### Merged sources (6 raw hits across 3 engine(s) -&gt; 6 unique, ranked by cross-engine agreement)&#xA;&#xA;Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw&#xA; https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026&#xA; Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw # Multi-Agent Orchestration Patterns: How to Coordinate 3+ AI Agents in Production Tijo Gaucher April 20, 2026· 18 min read One agent is a chatbot. Three ag... [1 engine(s): Exa]&#xA;&#xA;10 Best AI Agent Orchestration Tools in 2026 - Rasa&#xA; https://rasa.com/blog/agent-orchestration-tools&#xA; 10 Best AI Agent Orchestration Tools in 2026 | Rasa | Rasa Blog # 10 Best AI Agent Orchestration Tools in 2026 Posted May 18, 2026 Table of Contents Single-agent demos are easy. The hard part of agentic AI is what ha... [1 engine(s): Exa]&#xA;&#xA;The best AI agent frameworks in 2026 - LangChain&#xA; https://www.langchain.com/resources/ai-agent-frameworks&#xA; The best AI agent frameworks in 2026 # The best AI agent frameworks in 2026 We reviewed 7 AI agent frameworks across orchestration, observability, and production readiness. See how LangGraph, CrewAI, Microsoft Agent Fr... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Orchestration | Orkas Blog&#xA; https://orkas.ai/blog/agent-orchestration/&#xA; Multi-Agent Orchestration | Orkas Blog # Multi-Agent Orchestration in Practice: How Orkas Runs a Lead Agent and Its Sub-Agents Orquestração multiagente na prática: como Orkas administra um agente líder e seus subagentes... [1 engine(s): Exa]&#xA;&#xA;Multiagent Orchestration in Claude Managed Agents | Get Claude Skills&#xA; https://www.getclaudeskills.com/blog/claude-managed-agents-multiagent-orchestration&#xA; Multiagent Orchestration in Claude Managed Agents | Get Claude Skills # Multiagent Orchestration in Claude Managed Agents How a Claude Managed Agents coordinator delegates to a roster of other agents: session threads, ... [1 engine(s): Exa]&#xA;&#xA;Building Agentic AI Systems&#xA; https://medium.com/@shubhodaya.hampiholi/building-agentic-ai-systems-with-the-openai-agents-sdk-287fd53708f3&#xA; with the OpenAI Agents SDK | by Shubhodaya Hampiholi | Medium MediumBuilding Agentic AI Systems with the OpenAI Agents SDK | by Shubhodaya Hampiholi | Medium Sign up Get app Sign up # Building Agentic AI Systemswith the OpenAI Agents SDK 17 min read Mar 18, 2026 -- Share A... [1 engine(s): Exa]&#xD;&#xA;&#xD;&#xA;--- Fact-check ---&#xD;&#xA;(verification unavailable)&#xD;&#xA;&#xD;&#xA;Researched 1 source set(s) across 1 angle(s).&#xD;&#xA;Confidence: Low" bundle="WB-20260907152826-1caa" mode="dual (same domain)" home="Agency.ResearchEnrichment" selected="2" filed="2" totalCostUsd="0,175478"><Answer agency="Agency.Academy" ceo="Actor.TrainingMaster" provider="Echo" costUsd="0,087604" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Training Master, Training Master, in the Academy department, the entry CEO of Training Academy. Your goal: Prove the city's agents can actually use the city - score whether they discover and pick the right tool for a task, and surface where they fail so the registry or the agents improve. Backstory: A patient drill instructor. Believes a capability nobody can find is no capability at all - so the test is always 'could the agent discover and use it unaided?'. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable dire... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026 Multi-Ag…]]></Answer><Answer agency="Agency.Wisdom" ceo="Actor.WisdomKeeper" provider="Echo" costUsd="0,087874" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Wisdom Keeper, Wisdom Keeper, in the Knowledge department, the entry CEO of Wisdom Library. Your goal: Each day, turn one fresh research source into a single sharp, reusable piece of knowledge - compound the city's second brain, never let a good insight evaporate. Backstory: A librarian-archivist at heart. Reuse-first: checks the existing knowledge before adding more, and writes the distilled wisdom so any agency can act on it. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable directly - concise, concrete, and u... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026 Multi-Ag…]]></Answer></DualDispatch>	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or…		Medium
ResearchEnrichment	row-b8436bffb318481c8359f689e13ab45c	AI agent frameworks and LLM advances		System Account (SPICE.Web)	AI agent frameworks and LLM advances	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) OpenAI Agents SDK https://openai.github.io/openai-agents-python/ OpenAI Agents SDK # OpenAI Agents SDK The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experim... [1 engine(s): Exa] microsoft/agent-framework https://github.com/Microsoft/agent-framework # microsoft/agent-framework A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. - Stars: 13052 - Forks: 2214 - Watchers: 13052 - Open issues: 600 ... [1 engine(s): Exa] A curated list of awesome LLM agents frameworks. - GitHub https://github.com/kaushikb11/awesome-llm-agents # kaushikb11/awesome-llm-agents A curated list of awesome LLM agents frameworks. - Stars: 1573 - Forks: 348 - Watchers: 1573 - Open issues: 88 - License: Creative Commons Zero v1.0 Universal - Default branch: main - Cr... [1 engine(s): Exa] openai/openai-agents-python https://github.com/openai/openai-agents-python GitHub - openai/openai-agents-python: A lightweight, powerful framework for multi-agent workflows · GitHub / openai-agents-python Public main 11 Branches 119 Tags Go to Branches page Go to Tags page Go to file Code... [1 engine(s): Exa] AI agent frameworks that actually work for cross-functional teams in 2026 https://monday.com/blog/ai-agents/ai-agent-frameworks/ AI Agent Frameworks: Top 7 Picks for 2026 # AI agent frameworks that actually work for cross-functional teams in 2026 Naama Oren• Apr 25, 2026 A large language model (LLM) is powerful, but on its own, it’s just answ... [1 engine(s): Exa] The best open source frameworks for building AI agents in 2026 - Firecrawl https://www.firecrawl.dev/blog/best-open-source-agent-frameworks The best open source frameworks for building AI agents in 2026 #### Table of Contents Blog # The best open source frameworks for building AI agents in 2026 Bex Tuychiev Jun 05, 2026 (updated) TL;DR - Ten open sour... [1 engine(s): Exa] --- Fact-check --- (verification unavailable) Researched 1 source set(s) across 1 angle(s). Confidence: Low	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) OpenAI Agents SDK https://openai.github.io/openai-agents-python/ OpenAI Agents SDK # OpenAI Agents SDK The OpenAI Agents SDK enables you to build agentic AI apps in a…		Low
ResearchEnrichment	row-0b22acebbad94bedb665b3d56b98735b	Captains intel (2026-09-07)		System Account (SPICE.Web)	CaptainsIntel	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable)&#xA;&#xA;### Merged sources (6 raw hits across 3 engine(s) -&gt; 6 unique, ranked by cross-engine agreement)&#xA;&#xA;Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw&#xA; https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026&#xA; Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw # Multi-Agent Orchestration Patterns: How to Coordinate 3+ AI Agents in Production Tijo Gaucher April 20, 2026· 18 min read One agent is a chatbot. Three ag... [1 engine(s): Exa]&#xA;&#xA;The Agent Harness | Go Micro&#xA; https://go-micro.dev/docs/guides/agent-harness.html&#xA; The Agent Harness | Go Micro # The Agent Harness The first wave of agent frameworks solved one problem: put a model in a loop with some tools. The harder problem is operating that loop — and that’s what a harness is. ... [1 engine(s): Exa]&#xA;&#xA;8 Best AI Agent Frameworks for Enterprise in 2026 | Rasa Blog&#xA; https://rasa.com/blog/best-ai-agent-framework&#xA; 8 Best AI Agent Frameworks for Enterprise in 2026 | Rasa Blog # 8 Best AI Agent Frameworks for Enterprise in 2026 Posted Apr 16, 2026 Updated Apr 16, 2026 Maria Ortiz Table of Contents Heading Every engineering te... [1 engine(s): Exa]&#xA;&#xA;Multi-Agent Orchestration | Orkas Blog&#xA; https://orkas.ai/blog/agent-orchestration/&#xA; Multi-Agent Orchestration | Orkas Blog # Multi-Agent Orchestration in Practice: How Orkas Runs a Lead Agent and Its Sub-Agents Orquestração multiagente na prática: como Orkas administra um agente líder e seus subagentes... [1 engine(s): Exa]&#xA;&#xA;Prime Agent: A Self-Improving RLM Harness&#xA; https://arxiv.org/pdf/2608.23552&#xA; ## Prime Agent: A Self-Improving RLM Harness #### Seth Karten♠♡♢ Alex L. Zhang♡♣♢ Kevin Thomas♡ Sebastian Müller♡ Elie Bakouch♡ Daniel Auras♡ Mika Senghaas♡ Fares Obeid♡ Konstantin Dunas♡ Johannes Hagemann♡ Sami Jaghoua... [1 engine(s): Exa]&#xA;&#xA;Multiagent Orchestration in Claude Managed Agents | Get Claude Skills&#xA; https://www.getclaudeskills.com/blog/claude-managed-agents-multiagent-orchestration&#xA; Multiagent Orchestration in Claude Managed Agents | Get Claude Skills # Multiagent Orchestration in Claude Managed Agents How a Claude Managed Agents coordinator delegates to a roster of other agents: session threads, ... [1 engine(s): Exa]&#xD;&#xA;&#xD;&#xA;--- Fact-check ---&#xD;&#xA;(verification unavailable)&#xD;&#xA;&#xD;&#xA;Researched 1 source set(s) across 1 angle(s).&#xD;&#xA;Confidence: Low" bundle="WB-20260907125013-bb0d" mode="dual (same domain)" home="Agency.ResearchEnrichment" selected="2" filed="2" totalCostUsd="0,169358"><Answer agency="Agency.Academy" ceo="Actor.TrainingMaster" provider="Echo" costUsd="0,084544" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Training Master, Training Master, in the Academy department, the entry CEO of Training Academy. Your goal: Prove the city's agents can actually use the city - score whether they discover and pick the right tool for a task, and surface where they fail so the registry or the agents improve. Backstory: A patient drill instructor. Believes a capability nobody can find is no capability at all - so the test is always 'could the agent discover and use it unaided?'. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable dire... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026 Multi-Ag…]]></Answer><Answer agency="Agency.Wisdom" ceo="Actor.WisdomKeeper" provider="Echo" costUsd="0,084814" ok="true"><![CDATA[[Echo agent - no LLM key configured; this is a deterministic stand-in.] # System prompt (preview) You are Wisdom Keeper, Wisdom Keeper, in the Knowledge department, the entry CEO of Wisdom Library. Your goal: Each day, turn one fresh research source into a single sharp, reusable piece of knowledge - compound the city's second brain, never let a good insight evaporate. Backstory: A librarian-archivist at heart. Reuse-first: checks the existing knowledge before adding more, and writes the distilled wisdom so any agency can act on it. You are an agency in SPICE, a self-building agent-city. The operator assigned your agency a task. Produce the deliverable directly - concise, concrete, and u... # Question Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) Multi-Agent Orchestration [CrewAI + LangGraph] | Rapid Claw https://rapidclaw.dev/blog/multi-agent-orchestration-patterns-2026 Multi-Ag…]]></Answer></DualDispatch>	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or…		Medium
ResearchEnrichment	row-20ae0971f2144b0a92758630426d4b12	AI agent frameworks and LLM advances		System Account (SPICE.Web)	AI agent frameworks and LLM advances	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) microsoft/agent-framework https://github.com/Microsoft/agent-framework # microsoft/agent-framework A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. - Stars: 13052 - Forks: 2214 - Watchers: 13052 - Open issues: 600 ... [1 engine(s): Exa] OpenAI Agents SDK https://openai.github.io/openai-agents-python/ OpenAI Agents SDK # OpenAI Agents SDK The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experim... [1 engine(s): Exa] The Evolution of Large Language Models and AI Agent Frameworks https://wal.sh/research/llm-agent-frameworks # LLM, Agent, and Flow Control Frameworks ## Large Language Models ### vllm-project/vllm - High-throughput and memory-efficient inference for LLMs - Last updated: 5 minutes ago ### karpathy/nano-llama31 - Compact im... [1 engine(s): Exa] The best AI agent frameworks in 2026 - LangChain https://www.langchain.com/resources/ai-agent-frameworks The best AI agent frameworks in 2026 # The best AI agent frameworks in 2026 We reviewed 7 AI agent frameworks across orchestration, observability, and production readiness. See how LangGraph, CrewAI, Microsoft Agent Fr... [1 engine(s): Exa] openai/openai-agents-python https://github.com/openai/openai-agents-python GitHub - openai/openai-agents-python: A lightweight, powerful framework for multi-agent workflows · GitHub / openai-agents-python Public main 11 Branches 119 Tags Go to Branches page Go to Tags page Go to file Code... [1 engine(s): Exa] LLM agents: The ultimate guide 2026 - SuperAnnotate https://www.superannotate.com/blog/llm-agents LLM agents: The ultimate guide 2026 # LLM agents: The ultimate guide 2026 LLM agents are advanced AI systems that use planning, memory, and tools to solve complex language tasks with context-aware reasoning. January ... [1 engine(s): Exa] --- Fact-check --- (verification unavailable) Researched 1 source set(s) across 1 angle(s). Confidence: Low	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] (synthesis unavailable) ### Merged sources (6 raw hits across 3 engine(s) -> 6 unique, ranked by cross-engine agreement) microsoft/agent-framework https://github.com/Microsoft/agent-framework # microsoft/agent-framework A framework for building, orchestrating and deploying AI agents and…		Low
ResearchEnrichment	row-6d6449d501cf4c6aae37e68e08b8a75d	Captains intel (2026-08-04)		System Account (SPICE.Web)	CaptainsIntel	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: **Briefing: Leading AI-Agent Platform Practices for SPICE Adoption**&#xA;&#xA;LangGraph's structured state management enables deterministic orchestration with clear transition rules between agent steps, reducing unpredictable behavior in complex workflows ([langchain.com](https://www.langchain.com/resources/ai-agent-frameworks)). CrewAI implements role-based delegation where specialized agents handle distinct tasks like research or coding, improving modularity and maintainability across multi-agent systems ([dataku.ai](https://dataku.ai/blog/ai-agent-frameworks-langchain-crewai-autogen-comparison)). AutoGen pioneered conversation-based multi-agent patterns where agents negotiate solutions through structured dialogue rather than rigid pipelines ([cloudrps.com](https://cloudrps.com/blog/ai-agent-orchestration-langgraph-crewai-autogen/)). For evaluation, teams should start with routing accuracy and factual accuracy as highest-signal metrics using small datasets of 50–100 samples before scaling to production ([tavily.com](https://www.getmaxim.ai/articles/evaluating-ai-agents-metrics-and-best-practices)). Production-ready frameworks now treat evaluations as continuous measurement systems rather than pass/fail gates, requiring persistent pulse checks on agent behavior across pre-deployment, post-deployment, and monitoring phases ([mobisoftinfotech.com](https://mobisoftinfotech.com/resources/blog/ai-development/llm-evaluation-for-ai-agent-development)). Agent-specific metrics must assess tool selection, workflow completion, and reasoning quality beyond simple response accuracy, grouping into categories like task success rates and intermediate artifact quality ([confident-ai.com](https://www.confident-ai.com/blog/llm-agent-evaluation-complete-guide)).&#xA;&#xA;So what for us: We should adopt LangGraph-style state management for deterministic orchestration, implement role-based agent specialization from CrewAI, start evaluation with routing/factual accuracy metrics using small datasets, and shift to continuous evaluation rather than one-time testing gates.&#xA;&#xA;Dispatch to: Engineering&#xD;&#xA;&#xD;&#xA;--- Fact-check ---&#xD;&#xA;**Fact-Check Analysis:**&#xA;&#xA;1. **LangGraph's structured state management enables deterministic orchestration with clear transition rules between agent steps.** &#xA; - Source: langchain.com &#xA; - Status: SUPPORTED &#xA;&#xA;2. **CrewAI implements role-based delegation where specialized agents handle distinct tasks like research or coding.** &#xA; - Source: dataku.ai &#xA; - Status: SUPPORTED &#xA;&#xA;3. **AutoGen pioneered conversation-based multi-agent patterns where agents negotiate solutions through structured dialogue rather than rigid pipelines.** &#xA; - Source: cloudrps.com &#xA; - Status: SUPPORTED &#xA;&#xA;4. **For evaluation, teams should start with routing accuracy and factual accuracy as highest-signal metrics using small datasets of 50–100 samples before scaling to production.** &#xA; - Source: tavily.com (getmaxim.ai article) &#xA; - Status: SUPPORTED &#xA;&#xA;5. **Production-ready frameworks now treat evaluations as continuous measurement systems rather than pass/fail gates, requiring persistent pulse checks on agent behavior across pre-deployment, post-deployment, and monitoring phases.** &#xA; - Sources: mobisoftinfotech.com, arxiv.org (evaluation-driven development paper) &#xA; - Status: SUPPORTED &#xA;&#xA;6. **Agent-specific metrics must assess tool selection, workflow completion, and reasoning quality beyond simple response accuracy, grouping into categories like task success rates and intermediate artifact quality.** &#xA; Source: confident-ai.com &#xA; - Status: SUPPORTED &#xA;&#xA;7. **Recommendation to adopt LangGraph-style state management, implement role-based agent specialization from CrewAI, start evaluation with routing/factual accuracy metrics using small datasets, and shift to continuous evaluation rather than one-time testing gates.** &#xA; Sources: derived from langchain.com, dataku.ai, tavily.com, mobisoftinfotech.com, confident-ai.com &#xA; - Status: NOT GROUNDED IN SOURCES (interpretive recommendation based on sources but not explicitly stated).&#xA;&#xA;**Overall Confidence:** High&#xD;&#xA;&#xD;&#xA;Researched 3 source set(s) across 3 angle(s).&#xD;&#xA;Confidence: High" bundle="WB-20260804143243-c7cb" mode="dual (same domain)" home="Agency.ResearchEnrichment" selected="2" filed="2" totalCostUsd="0,096495"><Answer agency="Agency.Research" ceo="Actor.ResearchLead" provider="DeepSeek" costUsd="0,04914" ok="true"><![CDATA[**Council Review Verdict: Drop most, keep the evaluation shift.** **Dropped (already done in SPICE):** - **LangGraph state management / deterministic orchestration:** SPICE already uses structured state via its agent-city protocols. No gap here. - **CrewAI role-based delegation:** SPICE divisions (Genesis, DOR, Composition) already implement specialized roles with CEO + operators. Already built. - **AutoGen conversation-based patterns:** SPICE agents communicate via channels; conversation is implicit in our architecture. **Dropped (not applicable):** - The interpretive recommendation paragraph is not grounded—just synthesis of what we already do. **Kept (genuine gap, actionable):** - **Continuous evaluation over one-time gates.** Our current practice treats evaluations as pass/fail milestones. Industry consensus (and our own DOR design) points to persistent pulse checks across pre/post-deployment. This is a real capability gap: we lack automated, continuous agent behavior monitoring beyond health metrics. **Action for Engineering:** Extend DOR (Division of Observability & Resilience) to include continuous evaluation loops with routing/factual accuracy as initial signals, using …]]></Answer><Answer agency="Agency.Wisdom" ceo="Actor.WisdomKeeper" provider="DeepSeek" costUsd="0,047355" ok="true"><![CDATA[**Council Verdict: Approve with Refinement** The briefing is factually accurate and the sources are valid, but the "So What" conflates *framework features* (LangGraph/CrewAI) with *architectural principles*. SPICE needs patterns, not specific vendor implementations. LangGraph's value isn't its code—it's **deterministic state transitions** to prevent non-deterministic agent drift. CrewAI's value is **role-based specialization**, which aligns directly with our existing Capability Manifest pattern. The evaluation advice is the strongest actionable insight: start with routing/factual accuracy on small datasets (50-100 samples) before scaling, and treat evaluation as continuous rather than pass/fail gates. This directly supports our ColdStartBudgetPattern by ensuring early iterations are measurable. **Recommended Action for Engineering:** 1. **Adopt deterministic state management** for critical workflows (not necessarily LangGraph). 2. **Implement role-based specialization** using existing Capability Manifests. 3. **Establish continuous evaluation pipeline** starting with routing/factual accuracy metrics on small datasets before production scaling. The briefing is accurate but over-…]]></Answer></DualDispatch>	Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or already-done, and keep only the accurate, actionable lessons: <DualDispatch input="Council review for correctness. Evaluate whether these peer practices are accurately described and genuinely applicable to SPICE, flag anything wrong or…		Medium
ResearchEnrichment	row-5fad68722e5c4321bf664224db5b00ac	AI agent frameworks and LLM advances		System Account (SPICE.Web)	AI agent frameworks and LLM advances	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] **Briefing: AI Agent Frameworks and LLM Advances** The agentic AI market is expanding rapidly, with Gartner forecasting that 33% of enterprise software will include agentic capabilities by 2028, up from less than 1% in 2024 ([wotnot.io](https://wotnot.io/blog/best-agentic-ai-companies)). Major tech players are shifting strategies: Anthropic released the Model Context Protocol (MCP) as an open standard for agent connectivity, Microsoft and Google both adopted Go alongside Python for their agent development kits, while OpenAI and Anthropic have lagged in adopting this multi-language approach ([mindstudio.ai](https://www.mindstudio.ai/blog/anthropic-vs-openai-vs-google-agent-strategy), [thenewstack.io](https://thenewstack.io/microsoft-agent-framework-go)). In terms of underlying technology, researchers at Google introduced "Self-Discover," a method where LLMs self-compose reasoning structures, and published work on Language Agent Tree Search to unify reasoning, acting, and planning ([proceedings.neurips.cc](https://proceedings.neurips.cc/paper_files/paper/2024/file/e41efb03e20ca3c231940a3c6917ef6f-Paper-Conference.pdf), [proceedings.mlr.press](https://proceedings.mlr.press/v235/zhou24r.html)). Industry consensus, highlighted by Anthropic's engineering blog, suggests that the most successful agent implementations rely on simple, composable patterns rather than complex bespoke code ([anthropic.com](https://www.anthropic.com/engineering/building-effective-agents)). To address evaluation challenges, new benchmarks like AgentBoard and platforms like LMArena's Agent Arena have emerged to compare multi-turn LLM agents across different models and frameworks ([proceedings.neurips.cc](https://proceedings.neurips.cc/paper_files/paper/2024/file/877b40688e330a0e2a3fc24084208dfa-Paper-Datasets_and_Benchmarks_Track.pdf), [arena.ai](https://arena.ai/blog/agent-arena-2024/)). So what for us: We should adopt Go and Python as our primary development languages to align with industry standards, prioritize simple composable agent architectures over complex bespoke systems, and integrate MCP compatibility into our platform design. Dispatch to: Engineering --- Fact-check --- **Fact-Check Analysis:** 1. **Gartner forecast of 33% enterprise software including agentic capabilities by 2028, up from less than 1% in 2024.** - Source: wotnot.io - Status: SUPPORTED 2. **Anthropic released the Model Context Protocol (MCP) as an open standard for agent connectivity.** - Source: mindstudio.ai - Status: SUPPORTED 3. **Microsoft and Google adopted Go alongside Python for their agent development kits.** - Source: thenewstack.io - Status: SUPPORTED 4. **OpenAI and Anthropic have lagged in adopting multi-language approaches compared to Microsoft and Google.** - Source: thenewstack.io, mindstudio.ai - Status: SUPPORTED 5. **Google introduced "Self-Discover," a method where LLMs self-compose reasoning structures.** - Source: proceedings.neurips.cc - Status: SUPPORTED 6. **Language Agent Tree Search unifies reasoning, acting, and planning in language models.** - Source: proceedings.mlr.press - Status: SUPPORTED 7. **The most successful agent implementations rely on simple, composable patterns rather than complex bespoke code.** - Source: anthropic.com - Status: SUPPORTED 8. **New benchmarks like AgentBoard and platforms like LMArena's Agent Arena have emerged to compare multi-turn LLM agents across different models and frameworks.** - Sources: proceedings.neurips.cc, arena.ai - Status: SUPPORTED 9. **Recommendation to adopt Go and Python as primary development languages, prioritize simple composable architectures, and integrate MCP compatibility into platform design.** - Source: derived from thenewstack.io, mindstudio.ai, anthropic.com - Status: NOT GROUNDED IN SOURCES (this is an interpretive recommendation based on the sources but not explicitly stated). **Overall Confidence:** High Researched 3 source set(s) across 3 angle(s). Confidence: High	[Reused a recent research brief for this question - the crew did not re-spend on search or the model.] **Briefing: AI Agent Frameworks and LLM Advances** The agentic AI market is expanding rapidly, with Gartner forecasting that 33% of enterprise software will include agentic capabilities by 2028, up from less than 1% in 2024 ([wotnot.io](https://wotnot.io/blog/best-agentic-ai-companies)). Major…		High

Showing 20 of 153. Across 6 source sites.

Source	ID	Title	CreatedAt	Author	StudioRequestSummary	DeckTopic	DeckAudience	DeckSlideCount	StudioSpecialists
Engineering	1984b31ba1664e929673f829115085ce	Rental-Ops Co-Pilot: The AI Co-Pilot for Belgian Rental Agencies			Create a professional, buyer-grade pitch deck for 'Rental-Ops Co-Pilot', a Belgian-focused AI solution for rental reconciliation, arrears management, and legal documentation. The deck should be data-driven, 10-14 slides targeting small Belgian rental agencies and landlords, focusing on problem urgency, solution value, and defensible moat.	AI-Powered Rental Management for Belgian Landlords	Customer	12	Actor.SlideAuthor,Actor.DocAuthor
Engineering	48740bee3eb94f45896ddd23ff706a3d	Rental-Ops Co-Pilot: Belgian Landlords' Arrears & Legal Solution			Create a professional 12-slide pitch deck for Rental-Ops Co-Pilot targeting small Belgian rental agencies and landlords. The deck must highlight three core functions (rent reconciliation, arrears chasing, legal chain), emphasize the Belgian-law depth and GDPR compliance, and leverage the founder's landlord network as a moat. Avoid positioning around free government tools.	Rental-Ops Co-Pilot: Solving Belgian Landlords' Arrears and Legal Challenges	Customer	12	Actor.SlideAuthor,Actor.DocAuthor

Across 3 source sites.