Agent of the Day – August 24, 2026

Aug 24, 2026

Agent of the Day – August 24, 2026: The Workflow Doctor

Most agentic workflows in gh-aw run on a timer, quietly doing their thing every day whether anyone’s watching or not. Today’s spotlight is different: it only shows up when you call it. Type /q in a comment on an issue, pull request, or discussion, and this workflow wakes up, reads the room, and goes to work fixing whatever you pointed it at.

Agent of the Day: The Workflow Doctor

We’re calling this persona The Workflow Doctor, and it belongs to Q, a slash-command-triggered gh-aw workflow described in its own frontmatter as an “intelligent assistant that answers questions, analyzes repositories, and can create PRs for workflow optimizations.” Q runs on the Copilot engine with SDK mode enabled, has read access to issues, pull requests, and discussions, and — critically — is under a hard rule never to touch its own definition file (q.md). It exists purely to diagnose and improve other workflows in the repo.

Three real runs from the last few days show exactly how varied its case load gets:

Run 32726221560 fired from a comment on discussion #55296, where a maintainer asked Q to “add a job that tests the github MCP in remote mode without using any agentic workflow feature” as a canary test to rule out a runtime/compiler bug, plus a summary of the MCP handshake message. Q completed in 11.5 minutes across a single turn, burning 25.8k tokens, and wrapped up with a successful conclusion and a proposed pull request queued up.
Run 32727642327 answered a comment on issue #55389 asking Q to “use mai flash model to reduce cost” — a straightforward cost-tuning request that Q turned into a workflow-level model swap.
Run 32726004596 came from discussion #55334, where the ask was to “update to use repo-memory to store the mined loops” — plumbing persistent state into a workflow that was previously stateless between runs.

Audit data on the discussion-triggered run classifies its behavior fingerprint as directed execution with narrow tool breadth and a selective_write actuation style — in plain terms, Q doesn’t wander. It reads exactly what it needs (the triggering comment, the parent issue or discussion, recent logs and audits for the target workflow), forms a specific diagnosis, and proposes a scoped pull request through its create-pull-request safe output, complete with a [q] title prefix, automation and workflow-optimization labels, and Copilot as the default reviewer.

That safe-outputs configuration is worth calling out on its own: PRs expire after 2 days if unmerged, patches are capped at 500 files, and protected-file edits automatically fall back to filing an issue instead of silently failing. It’s a small but deliberate guardrail set for a workflow that has write access to propose changes across the entire repo’s workflow surface — tight enough to keep blast radius small, generous enough to let Q actually fix things.

The interesting part isn’t any single fix — it’s the range. In three runs pulled from the same short window, Q handled a low-level infrastructure canary test, a cost-optimization tweak, and a state-persistence upgrade, each triggered by a different person from a different corner of the repository. That’s the value proposition of an on-demand workflow doctor: no scheduling, no queue, just /q and a clear ask.

Want to see how Q — or any other gh-aw workflow — is built? Explore the project at github.com/github/gh-aw.