AGNTCon + MCPCon Japan 2026

AI-powered incident response sounds like the perfect use case for autonomous agents, but giving them unrestricted access to raw alerts and logs can quickly turn automation into hallucination. Whether you're debugging production outages or investigating failed deployments, engineers still spend hours manually correlating signals across disconnected observability tools.

In this talk, we'll break down the anatomy of a reliable agentic incident investigation system and show why structured incident context outperforms raw telemetry, how evidence enrichment improves reasoning, and why bounded context is critical for trustworthy AI agents.

We'll walk through production-ready patterns for building dependable operational agents:
1. Structured evidence gathering from Datadog, New Relic, Splunk, and Prometheus
2. Context engineering through alert correlation and deployment metadata
3. Long-term memory using historical incidents stored in a vector database
4. Human-agent collaboration that validates AI-generated RCAs and continuously improves future investigations.

Thus helping you reduce manual triage, lower MTTR, and build reliable, production-ready AI-driven incident response systems.