MCP Dev Summit North America

Most MCP integrations power chat: an agent responds to a prompt. But MCP can power continuous loops — agents that run on schedule, find issues, and ship fixes without human prompting.

This talk covers how to build MCP servers for autonomous operations. The core pattern: Detect → Analyze → Propose → Execute → Report. Each loop queries MCP resources, processes data, and takes action based on its trust level.

I'll show three examples from e-commerce: (1) a learnings database — optimization patterns exposed as MCP resources that agents query to diagnose codebases; (2) CDN observability — requests, bandwidth, cache rates as queryable resources for finding performance issues; (3) conversion analytics — pageview and conversion data that agents correlate to propose content changes.

The key design question: when can agents act autonomously vs. require human approval? I'll present a trust framework where loops graduate from report-only → PR with review → auto-merge based on accuracy over time.

Takeaways: how to structure domain expertise as MCP resources, architecture for connecting observability to agents, and patterns for safe autonomous execution.