How Should You Evaluate an AI SRE Product?
Published June 27, 2025
The rise of AI-powered Site Reliability Engineering (SRE) tools is one of the most significant trends in enterprise IT. As organizations—from Fortune 100 giants to fast-growing startups—face ever more complex systems, more AI-generated code, and ever-increasing customer expectations, the need for robust, intelligent incident response is only going to grow. But with a crowded vendor landscape, how do you evaluate an AI SRE product and ensure it delivers value versus finding yourself in Gartner's trough of disillusionment?
How to Structure Your Evaluation
A successful evaluation starts with real-world relevance. Leading organizations structure their evaluations in the following four steps—and you should too if you want a clear, early signal on whether an AI SRE product will add value in your day-to-day operations:
1. Start with High-Impact Teams
Choose 1-2 teams that have been feeling the pain of incidents and are likely to benefit, which would justify high engagement with the vendor.
2. Select a Set of Representative Incidents
Share 10 historical incidents that were truly significant for these teams. Walk the vendor through all the data sources and tools involved in resolving those incidents, ensuring they can integrate with your stack. Data typically falls into three buckets:
Telemetry: e.g., MELT data from tools like Elastic, Datadog, Dynatrace
Triggers: e.g., ServiceNow incidents; PagerDuty, Incident.io, and Datadog alerts
Change Events: e.g., Deployments/PRs; internal change log databases; ServiceNow change tickets; Datadog events
3. Define Success Up Front
Calibrate with the vendor on these historical events and make sure you are happy with the answer provided and the UI/UX during this backtesting phase. Agree upon what accuracy must look like on live incidents with respect to the mutually agreed upon scorecard for the pilot to be a success. We note that evaluating the AI accuracy for RCA is inherently hard, because the right answer can vary by organization and by incident severity. Below we provide a sample scorecard that we have built with our customers on how to evaluate the response from an AI SRE product.
4. Evaluate in the Real World
Test on live incidents that the vendor has not seen before. The best evaluations happen in production, where your engineers are genuinely engaged. If production isn’t possible, some organizations use staging environments with synthetic incidents, but nothing beats live incidents in production for proving value.
Prerequisites Before Trial
Below we list some core requirements before you can even trial an AI-SRE product.
Read-only Access
Insist on read-only access to start. No organization wants extra overhead in their deployments and collectors, it’s just too invasive! If the product can’t work with just your existing data, it’s likely not the right fit. If you want to use an AI-SRE product to remediate, then we suggest limiting the AI to execute a pre-defined whitelisted set of scripts.
Deployment Speed
From initial data access you should get an initial version of the product in your hands in less than a week, with one more week to calibrate together with the vendor. Quick time-to-value is essential.
Security & Scalability
Ensure the solution fits your security model, whether cloud or on-prem. Ask how easily it can scale to other teams—can new teams onboard themselves, or does each expansion require significant hand-holding from the vendor?
How Do You Evaluate Accuracy
Once the AI SRE is up and running, its steady state value comes from its consistency in accurately identifying the root cause. Yet “accurate root cause” means different things to different teams, so we anchor it to three practical expectations we see across customers:
Ideal: Pinpoints the exact change that caused the incident.
Good: Provides enough context to narrow the blast radius and page the right team.
Poor: Sends engineers on a false trail; if that happens too often, the tool isn’t worth your time.
To illustrate these points, let’s use an example – your storage service in us-east-1 starts returning 502/526 errors after its load balancer’s TLS certificate expires. We would evaluate potential answers from an AI-SRE as follows:
“Expired TLS cert on us-east-1-lb; renew the cert.” → Ideal answer
“Degraded health on us-east-1-lb; investigate the load balancer.” → Good answer
“Latency spike in eu-west-2 database; check replication.” → Poor answer
At Traversal, we quantify the AI accuracy with a finer five-tier rubric for each RCA and share the scores with customers on a regular cadence, which we have listed below. Importantly, our accuracy metric is confidence-weighted: an answer earns full credit only when the AI is both correct and highly certain—reflecting the value of trustworthy, decisive guidance. The rubric also ties each accuracy tier to the reduction in engineering effort and mean-time-to-resolution (MTTR) you can expect—showing exactly how a higher-precision AI translates into faster, less painful incident recovery.
Points | Accuracy Tier | What the AI Delivers | Well-calibrated AI Confidence | Typical Engineer Effort / MTTR |
|---|---|---|---|---|
100% | Bulls-Eye RCA | Pinpoints the exact root cause and causal chain; explains why it happened with well-structured telemetry (metrics, logs, traces, configs) and what to fix. | High | Audit & act in minutes ⇒ 10–15 min MTTR |
75% | Directional RCA | Surfaces the right subsystem/service & key symptoms but stops one hop short of root cause (e.g., missing error log or deployment); makes its limitations explicit. | Medium to high | Engineers follow breadcrumbs quickly ⇒ ~30 min MTTR |
50% | Good Triage | Provides several relevant symptoms & timelines mixed with some noise; useful but the answer needs pruning. | Low to medium | Extra filtering & validation ⇒ 45–60 min MTTR |
25% | Partial Miss | Points to plausible but an off-target domain (right cluster, wrong service) or gives vague correlations; still better than random. | Uncertain to low | Engineers must re-triage; may save a little time ⇒ >60 min MTTR |
0% | Misleading RCA | Declares wrong root cause or irrelevant symptoms, sending the team down a blind alley. | Not relevant | Increases MTTR; may trigger new incident review |
Make AI SRE Work for You
Evaluating an AI SRE product is about more than just ticking boxes. Focus on the incidents that matter, test vendors with real data, and prioritize solutions that deliver actionable insights and scale with your organization. As the importance of AI in site reliability engineering grows, making the right choice now will set your teams up for faster, smarter incident response in the future.
To see how these ideas play out in practice, watch our demo. For a broader perspective on the market, you can also read our AI SRE landscape post.
