Last year your organization shipped one AI agent. This year there are twenty, built by five teams, using three frameworks — and nobody knows which agents depend on which. An "innocent" capability change in one agent cascades into silent failures across four others. This is microservice governance all over again, except agents evolve faster and fail more quietly.

This talk presents a practical governance model for multi-agent AI, built on the same contract-driven patterns that tamed API and schema sprawl. In a live demo, I'll walk through the full lifecycle: agents registering discoverable capability cards via the A2A Protocol, enforcing communication contracts with compatibility rules, and deprecating agent versions without breaking consumers. I'll also show how the Model Context Protocol (MCP) fits alongside A2A — standardizing tool integration where A2A handles agent-to-agent discovery.

Attendees will leave with:
- A working governance model for multi-agent AI: registration, discovery, versioning, and deprecation
- A clear mental model for when to use A2A vs. MCP — and how they compose
- Patterns for preventing cascading failures when agents evolve independently across teams