The Model Context Protocol promises a clean way to expose enterprise tools to AI agents. In practice, shipping MCP into production reveals the harder problems: multi-tenant authorization that survives audit, observability that explains tool-call failures, and platform-boundary design that keeps the surface area governable.

This talk walks through the patterns used to put an MCP server into production behind real OAuth, real Redis/DynamoDB-backed authorization, and real Datadog observability — with the three biggest mistakes made and how other enterprise teams can avoid them.
Attendees will leave with a concrete reference architecture and a checklist for evaluating MCP rollouts in their own organizations.
Takeaways:
(1) An MCP authorization model that fits enterprise audit.
(2) Observability patterns that catch tool-call regressions.
(3) Platform-boundary heuristics for what to expose vs. keep internal.

Preferred length: 45 min (also available as 30 min breakout).
Audience: AI engineers, engineering managers, platform architects.
Level: Intermediate to advanced.
First public delivery: 2026.
Format: Conference talk, breakout, panel, or podcast.