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Every agent has a defined Perceive → Reason → Act → Observe → Learn cycle. This is the loop that separates an agent from a function call. Without this defined per agent, you have a catalog, not a running system.
Skills are the critical middle layer between agents and tools. Agents decide WHAT. Skills know HOW. Tools do atomic work. Without skills, every agent re-implements the same logic. With skills: one fix improves all agents. Versioned in Git, tested in sandbox, audited on every run.
Every tool call from every agent flows through the MCP Gateway. No exceptions. Policy check before execution. Audit log per invocation. Deny by default — if a tool is not on the explicit allowlist for an agent, the call is rejected before any network request is made.
This is the exact sequence of events when an incident triggers. Every step maps to the architecture. Every arrow is a real API call. The Orchestrator never sleeps — it holds state in PostgreSQL so it survives crashes and restarts.
Memory is not an agent. It is a shared infrastructure service with four distinct layers. Every agent reads from it at start, writes to it at close. The Memory Fabric is what makes the platform a learning system rather than just an automation system.
Azure AI Foundry covers ~40% of this solution natively. The remaining 60% maps to adjacent Azure services that sit alongside Foundry. One piece — the MCP policy gateway — requires a custom build on Azure infrastructure.
This is the definitive test of whether a system is truly full agentic. Every item is mapped to the architecture. Nothing is aspirational — each has a specific implementation location.
You cannot improve what you cannot measure. Observability is not a tab in the platform — it is a cross-cutting layer that every other component writes to. Reasoning traces, tool spans, RAG retrieval quality, cost per incident, model accuracy, and human approval audit all flow to Azure Monitor + App Insights.
This is the difference between an agentic system and a full agentic system. Every resolved incident makes the platform measurably better at the next one. The loop runs automatically after every incident close. No human intervention required.