AGNTCon + MCPCon Japan 2026

Every team building agentic systems hits the same wall: the gap between a demo and a system you'd trust in production. Most assume it's an engineering problem. It isn't. It's a governance problem and governance in a multi-agent system isn't a policy document. It's the full data path.

We demonstrate a production-style multi-agent workflow where customer feedback flows through agents, MCP tools, GitHub integrations, and multiple LLM providers before triggering automated actions.

We show how cloud-native primitives govern every interaction: OpenTelemetry for distributed tracing across agent hops, OPA for declarative policy enforcement, API gateways for prompt injection protection and model routing, and Kubernetes for workload isolation.

The hardest part of running agents in production isn't the AI. It's the same problems Kubernetes solved for microservices, observability, traffic management, policy, workload isolation applied to a new class of workload the ecosystem is still learning to instrument.