A tool-using agent for infrastructure
A tool-using agent that proposes infrastructure changes behind a human approval gate — 40% lower cost and 60% lower MTTR.
Infrastructure decisions — scaling, right-sizing, incident response — leaned on whoever happened to be watching the dashboards.
Recommendations had to be grounded in real metrics, and the agent's capabilities had to be scoped so that a bad suggestion cost a review comment rather than an outage: an LLM guessing about infrastructure is a liability, not a tool.
I built a tool-using agent on LangChain whose tools read Prometheus metrics and logs. It queried the telemetry itself, analyzed the estate, proposed cost optimizations, predicted scaling needs, and generated the infrastructure-as-code to act on them — but its capabilities stopped at recommending. Applying anything required an engineer's approval.
Infrastructure costs came down 40% and MTTR dropped 60% — every change still passing through a human gate, with a growing share of issues prevented before they paged anyone.
// grounded in measured data, reviewed by humans
- — The agent proposed; engineers approved. Slower than auto-apply, but an unreviewed LLM change to production infrastructure was never on the table.
- — Grounding every recommendation in Prometheus data limited the agent to what was measured — blind spots in metrics became blind spots in advice.
Want something like this built, or want to talk about the work?