Building agentic AI systems means juggling API keys for LLMs and MCP-connected tools, plus the burden of configuring those services correctly. Add expensive inference calls and highly customized dev setups, and the inner loop quickly becomes inner hell.

This session will demonstrate a streamlined inner-loop experience for agentic AI. We will develop, test, and iterate on multi-agent systems on our laptop, then deploy to Kubernetes with zero code changes. We'll show how to auto-provision local LLM inference, build agents with typed tool contracts, compose workflows, and test interactions with hot reload. We'll also show how MCP and/or A2A enable portable integration and separation of concerns, and how teams can switch to production-grade inference through configuration alone, with Kubernetes-native health checks, observability, and deployment patterns.

You'll leave understanding how to build and ship AI systems with open-source tooling and cloud native patterns you already know.