Everyone is building AI agents. Almost no one is thinking about the infrastructure layer underneath them.

This talk reframes AI agents as cloud native workloads and shows how to build a production-grade multi-agent platform on Kubernetes. Topics covered: modeling agents as K8s custom resources, durable execution and retry semantics with Dapr, event-driven inter-agent messaging with NATS, and end-to-end observability using OpenTelemetry traces across agent hops.

We go beyond LangGraph demos to tackle the hard problems: failure recovery, agent scheduling, concurrency limits, and audit logging — the same concerns we solved for microservices, now applied to agents.