Sam Gutentag
TLDR: AI DevOps Agents can take over high-frequency, low-leverage DevOps tasks like build restarts, deploy diagnostics, and health monitoring, freeing developers to focus on building, not babysitting infrastructure.
You deploy. Something breaks. Slack pings start flying. Now you’re digging through logs or babysitting a CI job instead of heading into the rest of your day.
Sound familiar? These aren’t edge cases, they can be regular occurrences for most teams. They also eat into developers’ limited time for focused, creative work.
According to Microsoft’s 2025 Time Warp study, developers spend more time debugging and operational tasks than writing code. That means every moment lost to a flaky deploy or failing health check is eating into the most valuable part of a developer’s week.
In small and mid-sized teams, there’s often no dedicated SRE or platform engineer to own this work. So developers pick up the slack, restarting jobs, watching logs, and re-notifying teammates. The result? Context-switching, frustration, and lost momentum.
And it’s exactly the kind of work that AI Agents are built to handle.
What is DevOps toil, and why does it matter?
Google’s SRE Handbook defines toil as manual, repetitive, automatable work tied to production systems. It’s not glamorous. But it’s real.
Examples include:
Restarting failed jobs that aren’t actually broken
Re-running deployment scripts that flaked for no clear reason
Investigating infra alerts that turn out to be noise
Tracking down logs or metrics when something goes sideways
This work drains morale, slows feature development, and causes burnout. While each task might seem small, they add up, especially when they appear every week or, worse, every day.
There are plenty of things teams should do more often: updating health checks, syncing deploy status, closing the loop on flaky jobs. But they skip them. Why? Because the tasks are tedious. If these tasks were more manageable, teams would do them more. And that’s where AI comes in: not just faster execution, but broader execution.
Let’s stop treating operational toil as inevitable. With the right DevOps agent, it doesn’t have to be.
Let agents handle the hand-holding
A well-configured AI DevOps Agent can quietly manage the background noise. These aren’t one-off scripts. They’re persistent, responsive systems that adapt to how your team works.
Think of the DevOps Agent as your automated SRE. It integrates directly with your GitHub, Slack, and Linear accounts to watch over your CI pipeline, debug flaky tests, and manage job orchestration. Upload CI test results, and it will diagnose root causes using stack traces and error metadata. Configure alerts, and it routes them to the right place at the right time. Connect it to your repo, and it surfaces actionable insights when things go wrong, from identifying your slowest test job to annotating PRs with links to logs, summaries, and suggested fixes. No more manual retries, no more alert fatigue.
What can an agent do?
A lot more than you might think:
Auto-restart jobs that fail due to known transient issues
Generate human-readable summaries when a deployment fails and highlight the likely cause
Monitor logs and metrics for meaningful changes and anomalies
Update stakeholders when builds break or ship, via Slack, GitHub comments, or ticketing tools like Linear
Annotate pull requests with links to logs, root cause suggestions, and test context
These aren’t just automations, they’re actions taken with context. For example, a merged PR could automatically regenerate documentation, update API schemas, and notify downstream consumers. That’s the beauty of multi-agent workflows: each agent handles its domain, like skilled trades on a job site.
Because code is verifiable, agents shine here. Unit tests give them the guardrails to act with confidence. That means less time for second-guessing and more time for moving forward.
From firefighting to feature-building
Every hour your team doesn’t spend chasing CI flakes or triaging deploy failures is an hour you’re not building something new. AI helps simplify that tradeoff: let the agents handle the noise so that you can focus on impact.
That’s the real opportunity AI gives us: not a shortcut but a shift. From reacting to issues to proactively preventing them, from babysitting infrastructure to spending time on work that matters.
At Trunk, our DevOps Agent was built with this mindset. It responds to signals that usually land on a developer’s plate, CI failures, flaky test patterns, webhook events, scheduled checks, and acts automatically. You don’t have to babysit a dashboard or triage alerts. You just start seeing results.
Join the waitlist to experience Trunk’s AI DevOps Agent and put your CI pipeline on autopilot. 👉 https://trunk.io/agent
