Agentic GraphRAG

Part IV. Production: Creating a Self-Healing System

Part IV closes the gap between a working prototype and a system you can actually deploy. An agent that reasons well in a notebook is not the same as an agent that runs 24/7 at acceptable cost, under governance constraints, and improves over time. Part IV makes the architecture production-ready: it learns from its own mistakes, runs within real cost and latency budgets, and respects the access boundaries enterprises require.

By the end of Part IV, the autonomous DevOps agent will be complete. It will monitor complex infrastructure in real time, diagnose issues, predict future failures, and learn from every interaction at a cost and latency profile that holds up under production load. It will have fulfilled the mission set out in Chapter 2.

• Chapter 7 covers the execution graph, the multi-layered evaluation framework, semantic backpropagation for targeted node-level feedback, and the graduated validation protocol, giving the agent a learning loop that improves performance without human intervention.

• Chapter 8 covers selective intelligence (matching model capability to node requirements), subgraph-level access control and PII handling, schema evolution and lifecycle management, and GPU-accelerated graph operations, making the complete system efficient, governed, and fast enough for production.