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Agents in production need a gateway, not a wrapper: the MCP + policy + observability stack for cloud-native AI

If your "AI agent" is just an LLM with a requests library and a service account, you don't have an agent — you have a future incident. Every team that has shipped agents to production learned the same lesson: the LLM is the easy 10%. The hard part is everything around it.

I'm going to walk through the stack I now consider non-negotiable: MCP for tools, a gateway for policy and routing, and OpenTelemetry for observability. I'll show configs, not concepts.

  • in AI, Cloud
  • 11 min read

The Cloud Engineer's Guide to AI Agents That Actually Do Things

There's a chasm right now between two worlds. AI builders ship agents that demo beautifully — "look, it booked my flight!" — but hand them real cloud access and they spin up 200 EC2 instances, leak IAM keys into logs, or get prompt-injected into deleting a production S3 bucket. Cloud engineers know how to build systems that don't fall over, but treat "AI agent" as a magic box: block it entirely, or hand it root keys and hope.

The middle ground is empty — and that's where the value is. This is a guide to building agents that mutate cloud infrastructure without burning it down: the architecture, the failure modes, and a concrete build you can copy.