AI for DevOps & SRE: From Impressive Demos to Operational Trust

AI for DevOps & SRE: From Impressive Demos to Operational Trust

What would make you trust an AI agent with operational changes?

An agent can generate a valid command and still misunderstand the objective. It can mistake missing telemetry for a healthy service, or choose an action outside the scope you authorized. How do we recognize these failures before they affect real systems?

Join Bucharest XDevOps Meetup for a practical, vendor-neutral session exploring the risks of AI-assisted operations and the controls that help manage them.

We’ll work through three synthetic scenarios in isolated environments:

1. The command is valid, but the objective is wrong.

2. The query returns no data, but the agent draws a confident conclusion.

3. The agent selects a tool outside the operator’s explicit boundaries.

For each scenario, we’ll examine the evidence, identify where the workflow breaks, and revisit it with appropriate controls: scoped permissions, validation before execution, meaningful human approval, audit trails, and independent verification of the outcome.

We’ll also discuss what these controls cannot guarantee. A permitted action can still be a poor decision. Human approval needs context. A successful tool call does not prove that the problem is resolved.

Who it’s for

DevOps engineers, SREs, platform engineers, developers, and technical leaders exploring AI-assisted operations. Familiarity with infrastructure operations is helpful; experience building AI agents is not required.

What you’ll take away

A practical checklist for evaluating an AI-assisted workflow: what it is allowed to do, what evidence supports its decisions, when it should stop or ask for approval, and how to verify what actually happened.

Proposed agenda — 90 minutes

• 0–10: Introduction — what does operational trust require?

• 10–30: Three failure scenarios — objectives, evidence, and action boundaries.

• 30–55: Practical controls — permissions, validation, approvals, audit, and outcome verification.

• 55–75: Revisit the scenarios — what the controls catch and what remains uncertain.

• 75–90: Discussion and Q&A.

Bring your questions and experiences with AI in incident response, observability, infrastructure, or delivery pipelines.