Building AI SRE Agents: From Your Laptop to a Production Fleet

An agent that diagnoses an OOM you caused on purpose has told you very little about how it will handle a live incident at 2am, reading logs that outside parties can influence, with credentials within reach and nobody watching it think.

Getting from one to the other is a sequence of stages, and most of the work has little to do with the model. It’s in the intake, the isolation, the evals and the feedback loops that tell a team which agent can be trusted with what.

This hands-on guide walks SRE and platform teams through each stage, with code, configuration and pitfalls.

Download the eBook to find out:

• How to build and grade an agent on a throwaway cluster, using failures you inject so you know the ground truth.
• How a four-rung trust ladder takes an agent from read-only shadow mode to approved remediation, each rung gated by numbers set in advance.
• When an action class is ready for autonomy: 50+ real incidents, a right-cause rate above 90% in shadow mode, and a tested rollback.
• Why a model upgrade can quietly break one incident class, and the drift rule that catches it.

What This eBook Covers

The eBook presents the journey in four parts, each building on the last: a local sandbox with zero production access, shadow mode on real clusters, autonomous response in the cloud, and the five problems that only appear once a fleet of agents runs in production.

Some of the practitioner advice inside:

• Graduate on evidence, not on the demo. Turn every injected failure, then every resolved incident, into a golden set. Track two numbers: did the agent name the right cause, and would you have approved its fix?
• Make guardrails structural, not instructional. A longer prompt can override a rule in a prompt. Nothing can call a tool that isn’t mounted.
• Keep an adversarial set next to the golden set. One checks that agents find the right cause; the other checks that they refuse the wrong action, like a log line telling them to scale to zero.
• Tier autonomy by blast radius. Restart a crashed pod and notify. Scale a deployment after async approval. Roll back a release only with the service owner’s sign-off.