What is automated root cause analysis?
Updated 7 October 2026
Automated root cause analysis is the use of software to find why a production system failed, by collecting evidence from logs, metrics, traces, code and data, and ranking the likely causes, instead of an engineer searching by hand.
This page is about software systems. Manufacturing and quality teams use the same phrase for methods such as the 5 Whys and fishbone diagrams, which is a different discipline.
How automated root cause analysis works
Most automated root cause analysis follows the same four steps an experienced engineer would.
- Collect evidence: the alert, error logs, traces, recent deploys, configuration and feature-flag changes, and database state.
- Build a timeline: what changed, and when, relative to the first symptom.
- Rank hypotheses: list the possible causes and score each against the evidence.
- Verify: test the leading cause, for example with a query on a read-only replica, before reporting it.
An example of automated root cause analysis
This is the example on our homepage. An alert fires: P95 latency on a checkout service has crossed 2.4 seconds against an 800 ms threshold, and support reports failed checkouts.
- Logs: hundreds of slow-query warnings on the orders table in the last 20 minutes.
- Database: an EXPLAIN on a read-only replica shows a full table scan on orders.account_id.
- Code: a migration merged earlier that day dropped the index on that column.
Together the three pieces of evidence give the root cause: the migration removed the index, so every checkout now scans the whole table. No single source shows it on its own, which is why automated root cause analysis has to read code and data, not only telemetry. Operate then drafts the fix as a patch for an engineer to review.
Rule-based vs machine learning vs agentic root cause analysis
| Approach | How it finds a cause | Where it struggles |
|---|---|---|
| Rule-based | Matches known patterns and runbooks | Anything nobody wrote a rule for |
| Machine learning | Spots anomalies and correlations in telemetry | Explaining why, and reading code or data |
| Agentic (AI agents) | Reads logs, code and data like an engineer and reasons about them | Confident but wrong answers, unless each answer is verified |
Why verification matters
An AI agent can produce a plausible root cause that is wrong. The fix is the same one aviation uses: a second, independent check before anyone acts. Operate has a separate agent on a different model verify each root cause, and rejects speculative conclusions before anyone sees them.
We cover this in why second opinions matter for root cause analysis and in aviation's rule for verifying AI diagnoses.
Automated root cause analysis without write access
Finding a cause doesn't require changing anything. Read-only access to logs, a database replica and the repository is enough to investigate, and it keeps the security review simple. We explain the trade-offs in AI root cause analysis without write access to production.
A buyer's checklist for automated root cause analysis
- Does every root cause come with evidence you can check?
- Is each answer verified before you see it?
- Does it read code and database state, or only telemetry?
- What access does it need, and can it write anywhere?
- Where does investigation data go, and can the model run in your network?
Our vendor-neutral guide to automated root cause analysis goes through each question, and the best AI SRE tools, compared applies them to six products.
Frequently Asked Questions
Can AI do root cause analysis?
Yes, for software systems. AI agents can read logs, traces, database state and recent code changes, rank the likely causes and show the evidence for each. The weak point is a confident answer that is wrong, so the answer should be verified independently before anyone acts. Operate has a second agent on a different model check every root cause.
Why does root cause analysis fail?
Usually because it stops at the first plausible cause, or because the evidence needed sits in a system nobody checked, such as the database or a configuration change. Missing timelines and blame-driven reviews make it worse. Automated root cause analysis helps by reading every connected source the same way each time.
How long does a root cause analysis take?
By hand it can take anywhere from minutes to days, depending on how many systems are involved and how quickly an engineer can get access to them. Automated root cause analysis runs as soon as the alert fires. In the example on our homepage, Operate reported the cause in 11 minutes, with the evidence attached.
Which AI tool is best for root cause analysis?
The best one is the tool whose access and data handling your security review will approve, and whose answers come with evidence you can check. Some run in your own infrastructure and stay read-only, like Operate. Others run inside an observability or incident platform. Test each on a few past incidents before you choose.
What are the 5 Whys for RCA?
The 5 Whys is a manual technique: you ask why a problem happened, then ask why again of each answer, about five times, until you reach a cause you can fix. It works for simple chains of events. Production software failures often have several contributing causes, so automated root cause analysis checks evidence across systems instead.