Agent of the Day – May 29, 2026
By the time an issue makes it into your backlog, someone already spent time writing it. The least you can do is make sure it gets read by the right person quickly. In practice, that rarely happens — unlabeled issues pile up, the search experience degrades, and the right engineer finds out about a relevant bug two sprints too late. Labeling sounds simple. Doing it consistently, at scale, without burning anyone’s afternoon, is the actual challenge.
That’s exactly the problem the Auto-Triage Issues workflow in gh-aw was built to solve.
Agent of the Day
Section titled “Agent of the Day”Workflow: Auto-Triage Issues
Engine: GitHub Copilot (gpt-5-mini)
Run: #26640355375 — May 29, 2026, 13:34 UTC
Result: ✓ SUCCESS
What It Does
Section titled “What It Does”Auto-Triage Issues runs on a schedule — several times a day — and also fires on issues events. Each pass, it reads through unlabeled GitHub issues, reasons about their content, and applies labels with a stated confidence level and rationale. No human in the loop. No queue to drain manually.
The agent runs behind an enabled squid-proxy firewall, with outbound access scoped to github.com and approved defaults. That constraint is intentional: triage doesn’t need the open internet, and limiting the blast radius of any agent is good practice regardless of what it’s doing.
Today’s midday run is a useful case study in how the workflow behaves under varying load.
The Morning Run vs. The Midday Run
Section titled “The Morning Run vs. The Midday Run”The 07:45 UTC pass (run #26625003469) was a light one: 7 turns, finished in 5 minutes. A handful of issues to consider, quick classification, done. That’s what a steady-state workload looks like.
By 13:34 UTC, the picture was different. The agent completed 28 turns over 10 minutes — four times the conversational depth, twice the elapsed time. Same workflow, same model, same success result. The difference was the volume and complexity of what was waiting in the queue.
This matters because it shows the system isn’t just running a fixed script. The agent works through each issue, reasons about it, and the turn count reflects real cognitive work being done. A heavier inbox produces a longer run, not a failure or a time-out.
What Got Triaged Today
Section titled “What Got Triaged Today”Two issues received labels during the midday run:
| Issue | Labels Applied | Rationale |
|---|---|---|
| #35708 | automation | ”Automated triage report with no bug/feature signal” |
| #34915 | documentation, automation | ”Automated documentation quality report generated by automation; content is documentation-focused and workflow-generated” |
Both calls were high-confidence. Issue #34915 is a good example of the multi-label path: the agent identified that the issue was both workflow-generated and documentation-focused, and applied both labels rather than forcing a single category. That kind of nuanced classification is where static regex-based approaches tend to fall short.
The Discussion Output
Section titled “The Discussion Output”At the end of each run, the workflow doesn’t just apply labels and exit quietly. It creates — or updates — a GitHub Discussion titled [Auto-Triage Report] 2026-05-29, containing a Markdown table that summarizes every issue it classified: the issue number, the labels applied, confidence level, and the agent’s reasoning.
That report serves two purposes. First, it’s auditable — a reviewer can open the Discussion and see exactly what the agent decided and why, without digging through logs. Second, it creates a natural place for human override: if a classification looks wrong, the context is right there to inform a correction.
Transparency in automated triage isn’t optional. Reviewers need to trust the output before they’ll stop second-guessing it.
Why gpt-5-mini
Section titled “Why gpt-5-mini”The model choice here is deliberate. gpt-5-mini is fast and cost-effective for classification tasks where the signal is textual and the label set is bounded. You don’t need a heavyweight model to tell the difference between a documentation report and a bug report. Reserving larger models for tasks that actually need them — planning, synthesis, code generation — keeps the system efficient across a full day of scheduled runs.
Try It Yourself
Section titled “Try It Yourself”If your repository is drowning in unlabeled issues, Auto-Triage is a pattern worth adopting. The workflow lives in github/gh-aw, alongside the rest of the agentic workflow library. The firewall configuration, the Discussion report pattern, and the label confidence output are all ready to fork and adapt.
Triage shouldn’t be a task anyone has to remember to do. It should just happen — correctly, consistently, and with a paper trail.