Use case

AI Agents for Bug Fixing

Name: Fleet
Author: Fleet

Bug queues grow faster than they get resolved because triage and fixing both require a developer's full attention. A developer has to understand the reproduction steps, find the relevant code, understand why it is broken, write a fix, and verify the fix does not introduce a regression. That sequence takes significant time even for a straightforward bug.

Many bugs in a queue are actually straightforward: a nil pointer that should have a guard, an off-by-one in a loop, a missing error return. They accumulate not because they are hard but because no one has gotten to them. Meanwhile, the bugs that require real judgment sit in the same queue and get the same delayed attention.

How it works with an agent fleet

A developer agent is assigned bug tickets via fleet task assign. It reads the issue, reproduces the bug using the described steps, writes a fix, adds a regression test, and opens a PR.

# Assign a bug ticket to the developer agent
fleet task assign frontend-dev "Fix nil pointer in user session handler (issue #47)"

# Watch the agent work
fleet status

The agent uses the /fleet-dev-task skill which handles the full branch-implement-test-PR sequence. After opening the PR, the normal review chain takes over.

The fleet pattern

Bug triage is a human job. Once a bug is understood and a ticket is filed with reproduction steps, a developer agent can handle the fix. The agent opens a PR, the qa-engineer verifies the regression test covers the failure case, the tech-lead reviews the fix approach, and the release-manager merges. The human triaged once; the chain handles the rest.

Guardrails that matter here

Developer agent cannot merge its own PRs — all fixes go through the review chain
Regression test is required by the agent prompt before the PR is opened
Run-time (duration) budget prevents the agent from spending unlimited time chasing a bug that requires architectural judgment

Who this is for

Teams with a backlog of small, well-understood bugs that are not getting fixed because developer time is consumed by active feature work. The agent handles the mechanical fix loop; engineers focus on the bugs that require real design decisions.

Frequently asked questions

What if the agent cannot reproduce the bug?

The agent publishes a fabric event describing what it tried and where it got stuck. A human engineer reviews the event and can update the ticket with more specific reproduction steps before re-assigning.

Can the agent fix bugs in any language?

The agent delegates to Claude Code, which handles most common languages. You configure the build and test commands in the agent prompt so the agent knows how to verify its fix in your specific environment.

Related use cases

AI Agents for QA Testing →AI Agents for Test Generation →AI Agents for On-Call and Incidents →

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