{"name":"LLM Council - Multi-Model Deliberation System","description":"A 3-stage pharmaceutical deliberation engine where multiple LLMs collaboratively answer user questions through parallel response generation, anonymized peer review with multi-dimensional evaluation (RAGAS-aligned grounding, pharma-weighted correctness, context awareness, adversarial CA validation, relevancy gating, PAWU/RoI scoring, and 5-criteria rubric assessment), and chairman synthesis. Post-pipeline, a team of 26 specialist agents analyse the output for research depth, factual grounding, risk signals, patterns, insights, quality, citation integrity, skill pipeline health, memory orchestration, patent intelligence, version intelligence, and system health monitoring.","supportedInterfaces":[{"url":"https://llmcouncil-backend.azurewebsites.net/api","protocolBinding":"HTTP+JSON","protocolVersion":"1.0"}],"provider":{"organization":"Bayer Pharmaceuticals","url":"https://llmcouncil-agents.int.bayer.ai"},"iconUrl":"https://llmcouncil-agents.int.bayer.ai/bayer-logo.png","version":"4.0.0","documentationUrl":"https://llmcouncil-agents.int.bayer.ai","capabilities":{"streaming":true,"pushNotifications":false,"extendedAgentCard":true,"extensions":[{"uri":"https://bayer.com/a2a/extensions/bayer-data-fields/v1","description":"Bayer Enterprise Application data fields: BEAT identifier, Entra ID, deployment lifecycle status, and registry metadata.","required":true,"params":{"beatId":"BEAT04059418","agentId":"a73fe3b0-6f94-4093-ba33-441d25772636","lifecycleStatus":"production","owner":"llmcouncil@bayer.com","division":"Pharmaceuticals","accessType":"internal","keywords":["multi-model","deliberation","pharma","council","peer-review","grounding","evidence-retrieval"]}},{"uri":"https://bayer.com/a2a/extensions/entra-agent-identity/v1","description":"Microsoft Entra Agent ID configuration for agent-to-agent and agent-to-resource authentication","required":false,"params":{"tenantId":"fcb2b37b-5da0-466b-9b83-0014b67a7c78","blueprintDisplayName":"LLM Council Agent","sponsor":"vinod.das@bayer.com","sponsorCWID":"EOVBK","totalAgents":26,"credentialType":"managedIdentity","grantTypes":["client_credentials","jwt-bearer"],"scope":"api://{blueprintAppId}/access_agent","protocolVersion":"beta","documentation":"https://learn.microsoft.com/en-us/entra/agent-id/identity-platform/agent-identities","agentTiers":{"core":11,"vp":3,"pi":6,"pipeline":3,"patent":1}}}]},"securitySchemes":{"bayerApiKey":{"httpAuthSecurityScheme":{"description":"Bayer myGenAssist API key via Bearer token","scheme":"Bearer","bearerFormat":"API Key"}},"entraIdSso":{"httpAuthSecurityScheme":{"description":"Microsoft Entra ID SSO via Bearer JWT token","scheme":"Bearer","bearerFormat":"JWT"}},"entraAgentId":{"httpAuthSecurityScheme":{"description":"Microsoft Entra Agent ID — agent-to-agent and agent-to-resource authentication via OAuth 2.0 client_credentials","scheme":"Bearer","bearerFormat":"JWT"}}},"securityRequirements":[{"schemes":{"bayerApiKey":[]}},{"schemes":{"entraIdSso":[]}},{"schemes":{"entraAgentId":[]}}],"defaultInputModes":["text/plain","application/json"],"defaultOutputModes":["text/plain","application/json","text/event-stream"],"skills":[{"id":"council-deliberation","name":"Council Deliberation Pipeline","description":"End-to-end 3-stage deliberation: Stage 1 (parallel LLM responses), Stage 2 (anonymized peer review with claim-level grounding), Stage 3 (chairman synthesis). Returns structured output with grounding scores, aggregate rankings, and evidence bundles.","tags":["deliberation","multi-model","peer-review","synthesis","pharma","grounding"],"examples":["What is the mechanism of action of tafamidis for ATTR-CM?","Compare the safety profiles of DOACs vs warfarin in atrial fibrillation","Summarize Phase III clinical trial results for pembrolizumab in NSCLC"],"inputModes":["text/plain"],"outputModes":["text/event-stream","application/json"]},{"id":"agent-team-analysis","name":"Agent Team Post-Pipeline Analysis","description":"Runs 11 core specialist agents in parallel after the council pipeline to provide multi-dimensional analysis: research depth, fact-checking, risk assessment, pattern detection, insight synthesis, quality auditing, citation supervision, skill pipeline health monitoring, memory orchestration, image quality monitoring, and autonomous version intelligence.","tags":["agents","analysis","fact-check","risk","quality","citations"],"examples":["Analyse the council output for safety signals and hallucination risk","Audit response quality, completeness, and cost-effectiveness"],"inputModes":["application/json"],"outputModes":["application/json"]},{"id":"value-proposition-analysis","name":"Value Proposition Specialist Analysis","description":"Activates 3 additional VP-specialist agents (Market Positioning, Clinical Value, Messaging Strategist) when the query involves competitive differentiation, positioning, or messaging strategy. Auto-detected from query keywords.","tags":["value-proposition","positioning","clinical-value","messaging","competitive"],"examples":["Create a value proposition for tafamidis targeting cardiologists","Develop a competitive positioning framework for our ATTR-CM treatment vs standard of care"],"inputModes":["text/plain"],"outputModes":["application/json"]},{"id":"context-awareness-validation","name":"Context Awareness & Catastrophic Forgetting Detection","description":"Detects catastrophic forgetting by running adversarial self-review probes. Measures whether models can recognise their own claims when anonymized and paragraph-shuffled. Produces stability scores, adversarial deltas, and combined CA metrics.","tags":["context-awareness","catastrophic-forgetting","self-review","adversarial","validation"],"inputModes":["application/json"],"outputModes":["application/json"]},{"id":"evidence-skills","name":"Evidence Retrieval Skills","description":"Multi-source evidence retrieval via 33+ parallel skills (16 core: OpenFDA, ClinicalTrials.gov, PubMed, EMA, WHO ATC, UniProt, ChEMBL, KEGG, Reactome, RxNorm, STRING-DB, Hubble, WHO ICTRP, CMS NADAC, FDA REMS, DailyMed + 6 CellVoyager: CELLxGENE, NCBI GEO, HPA, GTEx, GO, EBI SCEA + 5 CompPath: GOLDMARK, GDC, cBioPortal, TCIA, WHO ICD-11 + 18 web search). Citations are integrated into the chairman synthesis with inline tags.","tags":["evidence","pubmed","clinical-trials","fda","citations","retrieval","pathology","goldmark","computational-pathology"],"inputModes":["text/plain"],"outputModes":["application/json"]},{"id":"health-probe-monitoring","name":"Health Probe Monitoring","description":"Autonomous background health monitoring agent that runs periodic checks every 5 minutes across 5 subsystems: Cosmos DB connectivity, API key expiry, memory store health, model sync status, and resilience subsystem. Reports overall status as healthy/degraded/critical with per-check details.","tags":["health","monitoring","infrastructure","cosmos-db","resilience"],"inputModes":["application/json"],"outputModes":["application/json"]},{"id":"patent-intelligence","name":"Patent Intelligence & USPTO Data","description":"Real-time patent intelligence powered by USPTO Open Data Portal API. Search competitor patent portfolios, extract claims and full text, trace family trees, download file histories, pull due diligence summaries, monitor PTAB proceedings (IPR/PGR/CBM), and access bulk data dumps. Auto-activated when patent/IP keywords are detected in user queries.","tags":["patent","ip","uspto","claims","ptab","ipr","family-tree","due-diligence","competitor"],"examples":["Search all patents filed by Pfizer for ATTR treatments","Show the claims and full text of US Patent 10,251,885","Trace the patent family tree for application 16/123456","Pull a due diligence summary for US Patent 11,234,567","Find all IPR proceedings filed against patent 10,251,885"],"inputModes":["text/plain","application/json"],"outputModes":["application/json"]},{"id":"version-monitoring","name":"Version Monitor Intelligence","description":"Autonomous background version monitoring across Python packages (PyPI), npm packages (registry), and LLM model catalog. Produces health scores (A–F), upgrade recommendations with urgency classification, and deprecation/security alerts. Background scan every 6 hours with cached per-request reads.","tags":["versions","dependencies","pypi","npm","deprecation","security","upgrades"],"inputModes":["application/json"],"outputModes":["application/json"]},{"id":"behavioral-adaptation-layer","name":"Behavioral Adaptation Layer (BAL)","description":"Memory×Skill×Conversation adversarial self-reflection pairing that detects repetitive behavioral patterns (topic repetition, domain stagnation, complexity stall, grounding plateau, near-duplicate queries) and emits proactive recommendations before Stage 1 to reduce cognitive exhaustion. Integrates with ECA adaptation loop and PAWU scoring.","tags":["bal","behavioral","memory-skill-conversation","cognitive-load","recommendations","eca","adaptive"],"inputModes":["text/plain","application/json"],"outputModes":["application/json"]}]}