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.
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.
Reference design for G.A.I.N MCP as transport: client, gateway, server registry. Tool contracts (manifests) live in the Agents Blueprint.
Reference design for G.A.I.N RAG: route-declared retrieval, PEP-gated search, context packs, and validation before synthesis.