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"Before You Buy an AI SRE: A Technical Evaluation Framework"

The Difference Between a Good Demo and a Working Tool

AI SRE evaluations often end without a clear answer. Teams run pilots in sandboxes, grade on first-try accuracy, skip baseline metrics, and walk away unsure whether the trial exposed a real limitation or masked genuine value. The pattern helps explain why more than half of GenAI projects get abandoned after proof of concept and why AI projects fail at roughly twice the rate of conventional IT work.

This guide explains what a rigorous AI SRE evaluation actually looks like. Inside:

• How to scope the pilot and set measurable goals before testing begins
• Why production conditions matter for an evaluation to generalize
• Which baseline metrics to capture so improvement can be quantified
• How to evaluate the quality of an agent's reasoning under feedback and iteration
• What a credible learning loop looks like when engineers correct the agent
• Which data surfaces an AI SRE needs to reason across a heterogeneous stack
• A procurement-ready checklist covering scope, learning, and measurement

The goal is an evaluation framework that produces a verdict engineering leaders can act on, closing the gap between an impressive demo and a tool that holds up under real incident load.

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