Team Rebase
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Every vendor in the DevOps space now claims "AI-powered root cause analysis." The pitch is always the same: feed in your alerts, get root causes out. Faster MTTR. Fewer war rooms. Magic.
The problem: demos are designed to make everything look good. Vendors cherry-pick incidents they've already solved. The UI is polished. The confidence scores are high. Then you buy, deploy, and realize the tool only works on the exact scenarios it was trained to demo.
Before you evaluate, you need a framework. Here's how to actually assess engineering intelligence platforms and the questions that separate real capabilities from marketing.
What Engineering Intelligence Should Actually Do
First, define what you're buying. Engineering intelligence isn't faster alerting or prettier dashboards. It's the ability to:
Correlate across domains. Connect code changes, infrastructure state, deployment history, and operational signals into a unified picture.
Surface patterns humans miss. Not just "what happened" but "why this keeps happening."
Support decisions with evidence. Show the reasoning chain, not just confidence scores. Decision support, not autonomous black boxes.
Work with your existing stack. Integrate with what you have, not require ripping out tools or granting invasive access.
If a vendor can't do all four, you're buying a point solution dressed as a platform. What is Engineering Intelligence Platform?
The Evaluation Framework: 6 Criteria
Use this table when evaluating vendors. The "Good Answer" column shows what real capability sounds like. The "Red Flag" column shows what marketing dressed as capability sounds like.
Criteria | What to Ask | Good Answer | Red Flag |
|---|---|---|---|
Cross-Domain Correlation | Can it connect code changes to incidents? | "We correlate GitHub commits with Datadog alerts and PagerDuty incidents in real-time" | "We ingest telemetry from your observability stack" (telemetry only, no code) |
Code-Awareness | Does it understand code, not just metrics? | "We analyze PR diffs, identify N+1 query patterns, track which files correlate with incident history" | "We integrate with your APM traces" (traces ≠ code understanding) |
Depth of Context | Does it know your system or need you to explain it? | "We build a dependency graph from your actual traffic patterns and deployment topology" | "You'll configure service relationships in our UI" (manual setup = shallow context) |
Evidence & Reasoning | Does it show WHY, not just WHAT? | "Here's the causal chain: deploy → code path → connection pool exhaustion → latency spike" | "Root cause: deployment (87% confidence)" (confidence without explanation) |
Integration Flexibility | Read-only? API-first? Self-hosted option? | "Start with API access, expand to pipeline integration if needed, self-host option available" | "Install our agent on every node before we can begin" (invasive before value) |
Time to Value | How fast can you pilot? | "Initial insights within days, full deployment in weeks" | "4-6 week integration period before first results" (long setup = high risk) |
Most vendors will pass one or two criteria. The test is whether they pass all six.
We wrote more about what is Cross-Domain Correlation and why it is necessary in the age of AI. Read more about that here.
Questions That Expose Single-Domain Tools
These questions are designed to surface gaps. If a vendor hesitates or pivots to a different capability, you've found a limitation.
On cross-domain correlation:
"Walk me through how you'd identify a root cause that spans code AND infrastructure, not one or the other."
"What data sources do you actually correlate? Just telemetry, or also code history and deployment patterns?"
On code-awareness:
"Can you show me an incident where the root cause was a code pattern, not a config change or resource exhaustion?"
"Show me how you'd catch an N+1 query pattern before it causes an incident."
On integration:
"If I have 10 tools today, how do you connect them versus replace them?"
"What access do you need on day one? What do you need for full deployment?"
The N+1 query question is particularly revealing. Telemetry-only tools can't answer it. They see latency spikes and database load, but they don't understand the code pattern causing it. If a vendor can't explain how they'd catch code-level issues, they're an observability tool with an AI label.
The Accuracy Problem
Every vendor claims high accuracy. Few define what accuracy means.
Here's the problem: most vendors count "identified the right service" as a success. That's not root cause. That's topology. Knowing the payment service is affected isn't insight. Knowing why it's affected, what code change caused it, and how to fix it is root cause analysis.
When vendors report accuracy, ask:
How do you define a correct root cause? Is "right service" enough, or do you require the actual causal mechanism?
What percentage of RCAs required engineers to validate or correct? If it's over 30%, the tool is a triage assistant, not an RCA engine.
What percentage sent engineers down the wrong path? False confidence is worse than no answer.
Correlation is not causation. A deployment happening before an incident doesn't mean it caused the incident. Real accuracy requires tracing the mechanism: deployment → code change → specific code path → resource exhaustion → user impact.
Here's how we think about accuracy tiers:
Tier | What the AI Delivers |
|---|---|
Ideal | Identifies root cause + explains causal mechanism + shows evidence chain across code and infrastructure |
Good | Narrows blast radius to right team/system with clear reasoning |
Poor | Sends engineers down wrong path OR just confirms what they already suspected |
"87% confidence" means nothing without the evidence chain. Demand to see the reasoning, not just the score.
How to Structure Your Pilot
A good pilot proves value quickly without requiring you to bet your infrastructure on an unproven vendor.
Start with historical incidents. Share 5-10 significant past incidents with the vendor. Walk them through the data sources and tools involved. This tests whether they can integrate with your stack and whether their approach makes sense for your environment.
Backtest before you go live. Have the vendor analyze these historical incidents and show you what they would have surfaced. Calibrate together. If the backtest results don't match what your engineers actually found, that's a red flag.
Test on incidents the vendor hasn't seen. This is critical. If they only backtest on incidents they've already analyzed, you're seeing memorization, not intelligence. Real capability generalizes to new incidents.
Define success criteria upfront. Use the accuracy tiers above. Agree with the vendor on what "success" looks like before the pilot starts, not after.
Insist on read-only access first. Any vendor requiring invasive access before proving value hasn't earned that trust. Start with API integrations. Expand to deeper access only after the platform proves it can deliver.
Make Engineering Intelligence Work for You
Evaluating an engineering intelligence platform is about more than ticking boxes. Focus on cross-domain correlation, code-awareness, and evidence-based reasoning. Test vendors with real incidents they haven't seen. Prioritize platforms that adapt to your environment not the other way around.
To see how these criteria play out in practice, Book a Demo
For a deeper dive on why most AI-powered RCA falls short read our breakdown on Correlation vs Causation blog.
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