Defining How AI Agents Operate at Cloud Scale
An agent that troubleshoots Kubernetes, shows its evidence, and leaves the fix to the engineer.
Problem
Diagnosing a cluster failure meant hopping across logs, dashboards, and commands.
Approach
An agent that investigates on its own, shows every step, and waits to be told to act.
Impact
In testing, everyone reached a safe next action — with the engineer still in control.
Speed was easy. Trust was the design problem.
Troubleshooting was scattered across five tools.
Logs in one place, dashboards in another, and the fix in none of them.
Six manual steps, or one reviewed action. The same failure, two very different routes.
The agent investigates. The engineer decides.
Evidence-gathering runs on its own; a consequential change never does.
"An agent you can't read is an agent you can't trust."
Automatic.
The agent collects evidence and keeps its progress visible.
Separated.
Findings stay distinct from the actions it proposes.
Confirmed.
A risky change waits for the engineer's review.
Meet the operator where the problem already is.
Not a generic button — one tied to the resource on screen.
Everyone read these as things to act on. Recommendations, surfaced in place.
A correct answer isn't automatically a usable one.
Every response resolves to diagnosis, solution, and a safe next step.
Diagnosis.
What is wrong, and which resource it affects.
Solution.
The change that would resolve it.
Next steps.
How to proceed safely from here.
Evidence builds trust only if you can find it.
Every command, resource, and signal — surfaced, not buried.
Everyone needed help finding the manifest. Hidden evidence can't earn trust.
Pick the visual the decision needs.
A snapshot, a map, a timeline — matched to the question.
One failing path, end to end. Where it breaks, before the text does.
One agent, one scenario. Then a pattern for many.
Trust is designed, not declared.
Show what the agent inspected and why — then leave the decision to the human.
Azure OpenAI for Developers
Helping developers get started with AI by doing, not just reading.
