Governing AI Agent Decisions with MCP: How I Built Dead Letter Oracle

Dead Letter Oracle turns failed events into governed replay decisions. The Problem Nobody Solves A failed message hits the DLQ. The fix looks obvious. The replay still breaks production. In event-driven systems, messages fail silently. They land in a dead-letter queue with a vague error and an angry on-call engineer staring at them. The diagnosis is manual. The fix is a guess. The replay decision, whether to reprocess the message, is made without confidence scoring, without governance, and without an audit trail. Most AI agent demos show you the happy path: the agent gets it right on the first try. Dead Letter Oracle is not that demo. The Closed Loop Dead Letter Oracle turns failed events into governed replay decisions. It does not just diagnose. It reasons through a fix, tests it, revises when confidence is too low , makes a governed ALLOW/WARN/BLOCK decision, and shows every step of its reasoning. The full loop: Read the failed DLQ message via dlq_read_message Validate the payload vi