Meet the Workflows: Triage & Summarization

Jan 13, 2026

Wonderful to see you again! 🎩 So glad you’ve returned to Peli’s Agent Factory!

We’re the GitHub Next team, and we’ve been on quite a journey. Over the past months, we’ve built and operated a collection of automated agentic workflows. These aren’t just demos or proof-of-concepts - these are real agents doing actual work in our githubnext/gh-aw repository and its companion githubnext/agentics collection.

Think of this as your guided tour through our agent factory. We’re showcasing the workflows that caught our attention, taught us something new, or just made our lives easier. Every workflow links to its source Markdown file, so you can peek under the hood and see exactly how it works.

Starting Simple: Automated Issue Triage

To start the tour, let’s begin with one of the simple workflows that handles incoming activity - issue triage.

Issue triage now represents the “hello world” of automated agentic workflows: practical, immediately useful, relatively simple, and impactful. It’s used as the starter examples in other agentic automation technologies like Claude Code in GitHub Actions.

The purpose of automated issue triage is straightforward: when a new issue is opened, the agent analyzes its content, does research in the codebase and other issues, responds with a comment, and applies appropriate labels based on predefined categories. This helps maintainers quickly understand the nature of incoming issues without manual review.

Our Issue Triage Agent focuses on labels: it automatically labels and categorizes new issues the moment they’re opened. Let’s take a look at the full workflow:

---
timeout-minutes: 5

on:
  schedule: "0 14 * * 1-5"
  workflow_dispatch:

permissions:
  issues: read

tools:
  github:
    toolsets: [issues, labels]

safe-outputs:
  add-labels:
    allowed: [bug, feature, enhancement, documentation, question, help-wanted, good-first-issue]
  add-comment: {}
---

# Issue Triage Agent

List open issues in ${{ github.repository }} that have no labels. For each unlabeled issue, analyze the title and body, then add one of the allowed labels: `bug`, `feature`, `enhancement`, `documentation`, `question`, `help-wanted`, or `good-first-issue`.

Skip issues that:
- Already have any of these labels
- Have been assigned to any user (especially non-bot users)

After adding the label to an issue, mention the issue author in a comment explaining why the label was added.

Note how concise and readable this is - it’s almost like reading a to-do list for the agent. The workflow runs every weekday at 14:00 UTC, checks for unlabeled issues, and applies appropriate labels based on content analysis. It even leaves a friendly comment explaining the label choice.

In the frontmatter, we define permissions, tools, and safe outputs. This ensures the agent only has access to what it needs and can’t perform any unsafe actions. The natural language instructions in the body guide the agent’s behavior in a clear, human-readable way.

Automated Activity Summarization

To continue the tour, let’s look briefly at two automated summarization workflows that help us stay on top of repository activity. These agents digest large amounts of information and present it in a concise, readable format.

First, the Weekly Issue Summary creates digestible summaries complete with charts and trends (because who has time to read everything?)

Next, the Daily Repo Chronicle marrates the day’s activity like a storyteller - seriously, it’s kind of delightful.

Learnings

What surprised us most about this category?

First, the reduction of cognitive load. Having these agents handle triage and summarization freed up mental bandwidth for more important work. We no longer had to constantly monitor incoming issues or sift through activity logs - the agents did it for us, delivering only the essentials. This drastically reduced context switching and decision fatigue.

Second, the tone matters. When the Daily Repo Chronicle started writing summaries in a narrative, almost journalistic style, people actually wanted to read them. AI agents don’t have to be robotic - they can have personality while still being informative.

Third, customization is key. Triage differs in every repository. Team needs for activity summaries and actions that arise from them differ in every repository. Tailoring these workflows to our specific context made them far more effective. Generic agents are okay, but customized ones are game-changers.

Finally, these workflows became part of our routine. The Daily Repo Chronicle was a morning regular, giving us a quick overview of what happened overnight while we sipped. For teams that move fast using agents, these are key.

Next Up: Code Quality & Refactoring Workflows

Now that we’ve explored how triage and summarization workflows help us stay on top of incoming activity, let’s turn to the agents that continuously improve code quality.

Continue reading: Code Quality & Refactoring Workflows →

Learn More

GitHub Agentic Workflows - The technology behind the workflows
Quick Start - How to write and compile workflows

This is part 1 of a 16-part series exploring the workflows in Peli’s Agent Factory.