How to implement agent routing and autonomy: intent dispatch at ingress, orchestration in the loop, versioned manifests with pinned tools, four autonomy patterns, shared contracts, and control points. Model selection lives in the LLM Blueprint. MCP is transport only.
How to build a robust evaluation framework across every AI system plane: data sources, offline and online modes, three scorers, specialized automation, and per-plane playbooks.
Two views, one G.A.I.N subject: estate operating model and PGAR on the request path.
Estate operating model for AI Governance: Should we, Must we, What can go wrong, How do we ensure it. One control system across the estate. Runtime enforcement lives in the Governance Runtime blueprint.
How to implement a Policy-Governed Agent Runtime. PEP/PDP enforcement, subject-action-resource-context contracts, audit replay, step-up, and boundary-specific playbooks.
How to implement model selection at the LLM gateway: capability matrix, task-aware routing, abstention, and eval-gated canary promotion. The model does not choose which model runs.
Reference design for G.A.I.N MCP as transport: client, gateway, server registry. Tool contracts (manifests) live in the Agents Blueprint.
Reference architecture for unified observability in regulated enterprises. Business, service, and infrastructure layers, the observability graph, capability model, maturity levels, and consumption patterns.
Reference design for G.A.I.N RAG: route-declared retrieval, PEP-gated search, context packs, and validation before synthesis.