Even the best run production systems eventually fail. What separates a controlled response from an SLA breach is how fast the team can move from alert to root cause, and that’s exactly where agentic AI is changing the math.
At Qubika’s Cloud, SRE & DevOps Studio, we’ve built a Triage Agent as part of our broader AccelerateAI framework. It takes an alert, or a free text description of a problem when there isn’t one, correlates context across infrastructure, observability, and CI/CD, and produces a structured incident report with a remediation runbook in seconds.
This is what we learned building it, and what it changes for the teams that use it.
Two ongoing demands
SRE work has two ongoing demands. One is prevention: hardening systems, tightening pipelines, reviewing IaC changes, and addressing the failure patterns from past incidents so they stop repeating. The other is response: moving fast when something does break, fast enough to keep the SLA intact.
Most of the Studio’s time goes into prevention, and for a mature practice that’s the right ratio. But response is what the SLA actually measures, and a single drawn out incident is enough to put the availability target at risk on its own. Cutting time to context in those moments is what the Triage Agent is built for.
What runbooks can’t capture
Runbooks are foundational. We invest in them, keep them current, and write a new one every time an incident teaches us something. They capture what incidents share in common: the recognizable failure modes, the standard recovery procedures, the checks worth running first. That’s most of the work.
But every real incident has a piece that no runbook can write in advance. The specific trigger this time, the exact sequence of events, the upstream cause that made a familiar symptom show up in an unfamiliar way. That piece is the one that takes time. You don’t look it up; you reconstruct it.
So triaging an incident means reading alerts, pulling logs, checking the latest deployments, scanning metrics across three or four tools, deciding which signal is the root cause and which is the downstream symptom, and figuring out which existing runbook (if any) applies to the situation in front of you.
That phase, before any remediation starts, routinely consumes the first 15 to 30 minutes of an incident. Those are the most expensive minutes on the SLA clock, and one long triage is enough to put the availability target at risk.
Why an agent fits this problem
The triage phase is well defined: gather context from a fixed set of sources, correlate it, identify the probable cause, and propose a remediation path. The inputs are structured (alerts, metrics, deploy history, IaC state). The output should be structured too: a report, a recommended action, a runbook.
This is exactly the shape of work an agent does well. It’s a deterministic flow through known data sources, with the LLM doing the parts humans were doing slowly: reading, correlating, summarizing.
A few principles guide how we build this kind of agent. First, slash commands as the interface: engineers invoke the agent from their IDE the same way they’d run any other developer command, with no new portal, no new login, no new place to learn. Second, structured graphs: each agent is a graph of nodes with a defined order, and determinism comes from the structure of the flow itself. Third, the engineer stays in the reviewer role: the agent gathers and proposes, the human decides what to act on, following the same operating model as the rest of AccelerateAI.
Where Triage fits in AccelerateAI
AccelerateAI is Qubika’s framework for AI execution across engineering workflows, built on Anthropic’s Claude Code, structured around a loop where the agent reads context, formalizes the work, executes, and self reviews before handing it back. The engineer operates as reviewer, not executor.
The SRE Studio applies that same model to cloud engineering work: ticket intake, plan, implement, review, and deploy infrastructure changes. That’s the proactive side.
Triage sits on the reactive side of the loop, invoked the moment something is wrong. /troubleshoot is the lightweight version: a quick correlation across alerts, recent changes, and current state, useful when a developer notices something off and wants a second pair of eyes before opening an incident. /triage-incident is the heavyweight version, producing a full structured analysis as a markdown report ready to attach to a postmortem. Both commands are fired by the engineer from their IDE. The agent does the gathering; the engineer keeps decision authority.
How the Triage Agent actually works
The agent is a graph of deterministic nodes. Each node has a single responsibility, and each step builds on the context produced by the previous one.
Inputs
The agent starts with whatever the engineer can give it: a link to the alert (the most common entry point in practice) or a free text description of the symptom, when the human spotted it before any alert fired. If alerts are available, the agent parses them directly. Otherwise it interprets the human description and decides which signals to query first.
Correlation across MCP servers
The agent then pulls context through MCP servers we’ve already standardized across the SRE practice: APM and observability MCPs for traces, logs, and metrics across services; CI/CD MCPs for the last N deploys and their diffs; infrastructure MCPs for current resource state, scaling events, and configuration drift; and our internal Platform MCP for service ownership, dependency graphs, and runbook metadata.
The correlation step is where the actual time savings come from. What used to be four browser tabs and 20 minutes is now one structured pull, ordered by the same logic an experienced SRE would apply.
Output: a report engineers actually use
The agent produces a markdown report with a fixed structure, same sections every time in the same order: Summary (one paragraph on what happened and what’s affected), Root Cause (with the evidence trail that supports it), Affected Resources (services, environments, customers), Timeline (reconstructed from alerts, deploys, and metric inflection points), Runbook to Remediate (step by step and executable), Permanent Solution (what needs to change so this doesn’t recur), Prevention (guardrails, tests, or alerts to add upstream), and What to Watch After Resolution (the signals that confirm we’re actually out of the woods).
The fixed structure does more than format the output. Every incident produces a consistent artifact for postmortems, every engineer learns to think about incidents in the same shape, and every report is parseable downstream: by knowledge bases, by search, and by the next generation of agents we build on top.
What changes for the team
The most visible change is time to context. Triage that used to take 20 to 30 minutes now produces a draft report in well under a minute. The engineer reviews, corrects, and acts, starting from a structured picture with the evidence already on the table.
The less visible change is consistency. Every incident, regardless of who’s on call or how tired they are, produces the same artifact in the same shape. Postmortems get easier. Pattern recognition across incidents gets easier. The next runbook practically writes itself.
And then there’s the cultural shift. When the triage phase stops being painful, engineers stop avoiding it. Issues get investigated earlier. Postmortems get written more often. The proactive side of SRE work gets back the time it deserves.
Closing
Reliability comes down to time. The teams that protect their SLAs are the ones who spend the first ten minutes of an incident fixing, and getting there means closing the gap between the alert firing and the engineer having a complete picture in front of them.
Agentic AI removes the part of triage that was consuming the most expensive minutes on the clock. It hands the engineer a structured starting point with the evidence already on the table, so the work that actually requires human judgment (what to do, when to escalate, how to communicate) can start sooner.
Multiply that across a year of incidents, and the team gets back time that didn’t exist before.
From Alert to Root Cause
Qubika's Cloud, SRE and DevOps Studio builds the systems and agentic workflows that help engineering teams respond faster, fail less, and protect every SLA. See how we work.
