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Machines on-call for humans
Machines on-call for humans
Automated root cause analysis is an investigation method using software that generates hypotheses, chooses the evidence that separates them, and eliminates alternatives until the issue is traced from a trigger to what caused it.
Automated RCA runs that method on production issues, from full outages to performance degradations. It correlates logs, metrics, traces, deploys, and config changes, and produces evidence-backed root-cause conclusions in real time, while the alert investigation is live rather than after the fact. It runs on the telemetry you already have, as a layer over your observability stack rather than a replacement for it. And the autonomy is scoped to the investigation. The agent investigates on its own, and the fix waits for a human.
Say an alert fires at 2 am. The root cause is buried somewhere across logs, metrics, traces, recent deploys, and five dashboards, and the on-call engineer has to find it.
That search is where incident time goes. Most of MTTR is spent finding the cause. Once the cause is known, the fix is often quick, a rollback or a config change.
Automated RCA can bring that number down, but only if it holds up on real production incidents. Pointing an LLM at your logs works in a demo and breaks down once investigations get hard. Production needs models trained for troubleshooting, context about your systems, reasoning that tests hypotheses, guardrails on actions, a learning loop, and evals that measure investigation quality.
The timing separates it from traditional RCA. Five Whys and fishbone diagrams are post-incident rituals. They're useful for documenting what happened and recommending preventive actions, but they begin after the incident is resolved and depend on whoever's in the room remembering events accurately. Automated RCA takes a data-driven approach while the incident is still running, when finding the cause changes the outcome.
AIOps platforms like ServiceNow apply machine learning and workflow automation to IT operations. They handle anomaly detection and event correlation, compressing thousands of alerts into a few clusters and flagging that something unusual is happening. That narrows where to look, but a human still does the investigating.
Agents built on large language models (LLMs) changed what AI root cause analysis can do. They query observability tools, form hypotheses, and test them against the evidence, the way an engineer would.
| What gets automated | What that looks like | What the engineer still does | What you get |
|---|---|---|---|
| Nothing | Unassisted investigation across dashboards, logs, and code | The whole method | A cause, at the speed of one person holding one theory at a time |
| Evidence retrieval | Log search, natural language query over telemetry, observability copilots, alert and event correlation | Decides what to look for, holds the candidates, interprets each result, decides when to stop | Faster lookups and a narrower place to look |
| Evidence retrieval + response process | Incident orchestration: paging, channels, status pages, timelines | All of the investigation | A managed response, not an answer |
| Evidence retrieval + response process+ hypothesis loop | Automated RCA | Verifies the chain and decides the action | A chain from trigger to symptom with evidence at each link |
A team with a fast query interface over every production system is still doing manual root cause analysis. They have compressed the retrieval step, which was never the hard part. The engineer still generates the candidates, still picks the evidence that separates them, and still runs the loop one thread at a time at human speed.
Incident orchestration tools sit alongside automated RCA rather than competing with it. One runs the response, the other finds the cause.
Break an incident into its parts and the time goes to one place. Once the cause is known, the remedy is often a rollback, a restart, or a config change. The long middle is finding out what's actually wrong. Most of the mean time to resolution is spent understanding the problem, and downtime costs scale with every extra hour that takes.
That understanding got harder to reach. A decade ago, an engineer could hold the whole system in their head: one codebase, a few dashboards, deploys every couple of weeks. Now there are hundreds of services calling each other, infrastructure that autoscales by the hour, deploys landing daily, and telemetry measured in terabytes. Sometimes, the answer to "what broke" exists in the data, spread across more sources than a person can check in the time available. Other times, it’s not even written down. The bottleneck shifted from collecting the data to reasoning across it.
Investigation skill doesn't spread evenly either. It's built on years of accumulated knowledge about the system, and it concentrates in the hands of a handful of senior engineers. When an incident gets hard, it escalates to them no matter whose rotation it is. They become the constraint on every difficult resolution, and the toil lands hardest on the people the team can least afford to burn out. Meanwhile the knowledge never spreads, because the engineers who could teach it are busy firefighting.
Each of these was manageable on its own. Together they compound, because investigation load keeps growing while the pool of people who can absorb it stays flat. Automating the investigation itself only recently became possible, which is why the category is emerging now.
Teams that build their own investigation agent tend to see the same arc. It works in single-player mode, one engineer running simple investigations, and then collapses on real production incidents. Six reasons come up repeatedly.
Six pillars, and each one exists because a specific part of the method breaks when you try to run it without a person.
Models post-trained on production troubleshooting, and orchestrated alongside frontier models. Post-training closes the domain gap, so symptoms map to real production failure modes instead of the nearest familiar pattern.
A context graph of your systems, fluency with your observability tools, and context engineering that controls what enters the model's window at each step. The agent starts every investigation already knowing your services, dependencies, and recent code changes, and its queries stay scoped. That fixes both scattered knowledge and token explosion.
Causal reasoning and multi-agent coordination, with hypotheses tested in parallel. Instead of one thread committing to its first answer, the system generates alternatives, gathers evidence for and against each, and ranks what survives.
Guardrails and scoped autonomy. The agent investigates freely and reads widely, but acts only within explicit bounds, with an audit trail on everything it touches. That's what makes the security and compliance story workable.
Explicit feedback from engineers plus implicit signals from every investigation, folded back into what the system knows. Tribal knowledge becomes institutional memory, and each incident improves the next investigation.
A domain-specialized eval framework calibrated to how engineers actually investigate. LLM evals measure whether changes improve the quality of investigations, detects regressions, and allows the system to safely absorb new model releases.
Here's how a Resolve AI automated RCA investigation works through an alert investigation, step by step.
The causal timeline is the trust mechanism. Every finding is inspectable, engineers and agents work from the same live evidence, and the on-call engineer verifies the conclusion in minutes instead of hours. This investigation loop is also the core of what an AI SRE does.
The outcomes Resolve AI customers report follow from that. DoorDash Ads reduced time to root cause by up to 87% with Resolve AI, and Salesforce reduced it by roughly 60%. Fewer incidents escalate to senior engineers, and postmortems start pre-documented, because the investigation record already exists.
Building retrieval automation is genuinely fast. A capable engineer with tool access and a frontier model gets there in days, and for a single-service setup it can be enough. What that build does not give you is the loop. Candidate generation, discriminating evidence, elimination, and a chain that holds up all sit behind knowledge capture, evals, guardrails, and a compliant security layer, and each of those is its own workstream.
The cost sits in everything around the agent. Knowledge capture, evals, guardrails, and a compliant security layer each become their own workstream, and together they add up to an internal product that requires a full-time team. The real question is whether you want to build production AI infrastructure or spend that engineering time on your own product.
The fastest way to evaluate this category is watching an agent investigate your incidents. Book a demo to see Resolve AI's agents on your systems, or start with the AI for Production Systems ebook for the full architecture behind them.
Automated RCA is an investigation method run by software. It generates hypotheses, chooses the evidence that separates them, and eliminates alternatives until the issue is traced from a trigger to its cause. It runs on your existing telemetry during live alert investigations, using LLM agents that query tools and test hypotheses the way an engineer would.
AIOps tools, AI for IT operations, use machine learning for anomaly detection and event correlation. Their output is the starting point for a manual investigation, a narrowed set of correlated events that an engineer still has to work through by hand. Agentic automated RCA runs the investigation itself. It generates hypotheses, queries systems to test them, and returns ranked causes with evidence.
An AI agent can find the root cause of a production incident, provided it's built as a system rather than a bare model pointed at logs. A general model over your logs finds correlations and stops there. A root cause analysis AI with context about your architecture, parallel hypothesis testing, and verification can trace the actual cause, and the difference shows in the output: a causal timeline with citations instead of an unexplained guess.
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