AI Agents That Operate Infrastructure - Lessons from a Tier 1 bank deployment

Modern “AIOps” mostly means better alerts and nicer dashboards. The hard part, actually operating infrastructure, still relies on humans writing integrations, stitching together tools, and executing runbooks under pressure.

In this session I’ll show a different pattern: AI agents as operators, where the agent can plan and execute real changes safely across your existing systems. The key is a “universal executor” approach: instead of building and maintaining bespoke integrations, we give the agent the API documentation (or an OpenAPI spec), plus a small set of operational guardrails, and it generates the required calls, validates the outcome, and self-heals when the real world pushes back (rate limits, inconsistent schemas, partial failures, missing permissions, stale state, etc.).

I’ll walk through what we built at NetOrca (our self‑service infrastructure orchestration platform) and what we learned running it in production at NatWest Bank — at enterprise scale, with real constraints and real consequences. You’ll see how we handle the messy parts people skip in demos: idempotency, retries, rollback strategies, state-machine concurrency, noisy or contradictory API docs, safe prompting, and how we prevent an agent from “doing the wrong thing” when it’s confident but incorrect.

We’ll cover:
The architecture: planning → execution → validation → repair loop (and why “tool calling” alone isn’t enough)
How to make “docs → execution” reliable in the face of bad specs and unpredictable APIs
Guardrails that work in practice (scopes, allowlists, policy checks, change windows, blast-radius limits)
Observability: how you debug agent actions like you debug humans (audit trails, structured step logs, reproducible runs)
What enterprise adoption actually looks like: trust-building, controlled rollout, and the line between automation and autonomy

If you’re building agentic systems for infrastructure, DevOps, network/security ops, or internal platforms, you’ll leave with a concrete blueprint for moving beyond copilots and into agentic operations that ship real changes plus the production lessons that only show up when you run this stuff for real.