Buyer's Guide
How to Evaluate an AI SRE
Most AI proofs of concept fail — not because the product doesn't work, but because the evaluation was designed wrong. This guide covers what to measure, what to avoid, and how to run a PoC that gives you a conclusive answer.
- 50%
- AI projects abandoned after PoC (Gartner)
- 80%
- AI projects fail overall (RAND)
- 3X
- PoC success rate with tight scope (Sapphire Ventures)
What's in the Guide
- Know What You're Improving Start with clear, measurable goals. Triage speed? Investigation time? Fewer repeat incidents? Scope to one or two variables, not everything at once.
- Test in Production, Not a Sandbox A clean demo environment tells you nothing about how the tool performs against real alerts, real telemetry, and real cross-stack complexity.
- Define Your Baseline Estimate MTTD, MTTR, investigation time, and escalation frequency before the pilot starts. Without a baseline, you're running a vibe check.
- Grade Convergence, Not First-Try Accuracy Evaluate whether the agent forms grounded hypotheses, shows its reasoning, accepts feedback, and improves — not whether it's perfect on day one.
- Demand a Visible Learning Loop If engineer feedback requires retraining cycles or manual intervention to take effect, that's a limitation worth knowing before you buy.
- PoC Checklist A practical checklist covering scope and data access, learning and collaboration behavior, measurement, and adoption readiness.
"When incidents pile up, roadmap progress stops and people burn out."
— Director of SRE, Enterprise CX Company
Trusted by engineering teams at leading companies
