Workflow architectures.

Three pipelines. Same structural pattern across different domains. The live demos that run on these architectures are in development. The architectures are not.

Applied Scenario / Insurance Product Deployment

Six agents, three data stores, four orchestration points. Any approved insurance filing is the input. The system identifies the product type, builds the rule documents, builds the rate tables, generates the application form, and produces a working electronic application. The architecture is product-agnostic. The same engine handles new product types without code changes.

INGESTION STRUCTURED STORAGE AI INTELLIGENCE SYNTHESIS + OUTPUT Product Filing Document Classification LOCAL O classified inventory Filing Diagnostician LOCAL O policy forms rate tables applications Rule Extraction API Rate Table Builder LOCAL Form Generator API Rule Documents Rate Tables Application Schema CROSS-TIER VALIDATION: Spot-check extractions against source PDFs. Fail → re-extract. O rules + rates + fields Application Assembly FRONTIER O Working eApp tested, production-ready application Next Product construction intelligence feeds forward PATTERNS COMPOUND EACH BUILD FASTER Agent Store Quality Orchestrator Local API Frontier

Applied Scenario / Managed Services Lifecycle

Seven agents, five data stores, six orchestration points, one human decision point. Every agent output is validated. Every handoff is governed. The system learns from its own operation.

INGESTION STRUCTURED STORAGE + ANALYSIS AI INTELLIGENCE SYNTHESIS + OUTPUT RFP Document Document Intelligence LOCAL O profile + shape requirements vendor signals Engagement Analysis API Requirements Decomposition LOCAL Competitive Intelligence API Engagement History Requirements Taxonomy Competitive Intel O Knowledge Retrieval API Prior Responses O all streams + provenance Intelligence Synthesis FRONTIER O intelligence + reasoning Response Drafting API O drafts + flags Human Decision Edit History classified edits O Response Package + captured intelligence INTELLIGENCE FEEDS BACK OUTPUT BECOMES INPUT Agent Store Decision Quality Orchestrator Local API Frontier

Applied Scenario / Project Lifecycle

Six agents, five data stores, five orchestration points, one human decision point. The system ingests everything a project already generates. The project update is the proof that the system understands.

INGESTION STRUCTURED STORAGE AI INTELLIGENCE SYNTHESIS + OUTPUT Tickets Commits + PRs Email + Slack Meetings + Any Ticket Intelligence LOCAL Commit Intelligence LOCAL Communication Intelligence LOCAL Meeting Intelligence LOCAL CROSS-TIER VALIDATION: API samples local extractions. Fail → reprocess. O Project History Deliverable Tracker Team Intelligence Decision Log Scope Registry SOW / Contract obligations, milestones O all stores + live signals Project Synthesis FRONTIER O intelligence + reasoning chains Update Assembly API O update + risks + flags Human Decision O Project Update status, risks, intelligence Proactive Actions alerts, scheduling, tickets Next Cycle everything learned feeds forward INTELLIGENCE FEEDS BACK OUTPUT BECOMES INPUT Agent Store Decision Quality Orchestrator Local API Frontier

Describe your operation.

Describe what you are dealing with. I will ask questions until I understand it, then we share ideas and talk about solutions.