Peli de Halleux

Time to get into data ana analytics at Peli’s Agent Factory!

In our previous post, we explored organization and cross-repo workflows that operate at enterprise scale - analyzing dozens of repositories together to find patterns and outliers that single-repo analysis would miss. We learned that perspective matters: what looks normal in isolation might signal drift at scale.

Beyond tracking basic metrics (run time, cost, success rate), we wanted deeper insights into how our agents actually behave and how developers interact with them. What patterns emerge from thousands of agent prompts? What makes some PR conversations more effective than others? How do usage patterns reveal improvement opportunities? This is where we brought out the big guns: machine learning, natural language processing, sentiment analysis, and clustering algorithms. Advanced analytics workflows don’t just count things - they understand them, finding patterns and insights that direct observation would never reveal.

Advanced Analytics & ML Workflows

These agents use sophisticated analysis techniques to extract insights:

Copilot Session Insights - Analyzes Copilot agent usage patterns and metrics
Copilot PR NLP Analysis - Natural language processing on PR conversations
Prompt Clustering Analysis - Clusters and categorizes agent prompts using ML
Copilot Agent Analysis - Deep analysis of agent behavior patterns

The Prompt Clustering Analysis uses machine learning to categorize thousands of agent prompts, revealing patterns we never noticed (“oh, 40% of our prompts are about error handling”).

The Copilot PR NLP Analysis does sentiment analysis and linguistic analysis on PR conversations - it found that PRs with questions in the title get faster review.

The Session Insights workflow analyzes how developers interact with Copilot agents, identifying common patterns and failure modes. What we learned: meta-analysis is powerful - using AI to analyze AI systems reveals insights that direct observation misses.

These workflows helped us understand not just what our agents do, but how they behave and how users interact with them.

Learn More

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

Next Up: Campaigns & Project Coordination Workflows

We’ve reached the final stop: coordinating multiple agents toward shared, complex goals across extended timelines.

Continue reading: Campaigns & Project Coordination Workflows →

This is part 15 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Organization & Cross-Repo

Jan 13, 2026

Let’s zoom out at Peli’s Agent Factory!

In our previous post, we explored multi-phase improver workflows - our most ambitious agents that tackle big projects over multiple days, maintaining state and making incremental progress. These workflows proved that AI agents can handle complex, long-running initiatives when given the right architecture.

But all that sophisticated functionality has focused on a single repository. What happens when you zoom out to organization scale? What insights emerge when you analyze dozens or hundreds of repositories together? What looks perfectly normal in one repo might be a red flag across an organization. Organization and cross-repo workflows operate at enterprise scale, requiring careful permission management, thoughtful rate limiting, and different analytical lenses. Let’s explore workflows that see the forest, not just the trees.

Organization & Cross-Repo Workflows

These agents work at organization scale, across multiple repositories:

Org Health Report - Organization-wide repository health metrics
Stale Repo Identifier - Identifies inactive repositories
Ubuntu Image Analyzer - Documents GitHub Actions runner environments

Scaling agents across an entire organization changes the game. The Org Health Report analyzes dozens of repositories at once, identifying patterns and outliers (“these three repos have no tests, these five haven’t been updated in months”). The Stale Repo Identifier helps with organizational hygiene - finding abandoned projects that should be archived or transferred. We learned that cross-repo insights are different - what looks fine in one repository might be an outlier across the organization. These workflows require careful permission management (reading across repos needs organization-level tokens) and thoughtful rate limiting (you can hit API limits fast when analyzing 50+ repos). The Ubuntu Image Analyzer is wonderfully meta - it documents the very environment that runs our agents.

Learn More

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

Next Up: Advanced Analytics & ML Workflows

Cross-repo insights reveal patterns, but we wanted to go even deeper - using machine learning to understand agent behavior.

Continue reading: Advanced Analytics & ML Workflows →

This is part 14 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Multi-Phase Improvers

Jan 13, 2026

Let’s continue our journey through Peli’s Agent Factory!

In our previous post, we explored infrastructure workflows - the meta-monitoring layer that validates MCP servers, checks tool configurations, and ensures the platform itself stays healthy. These workflows watch the watchers, providing visibility into the invisible plumbing.

Most workflows we’ve seen so far run once and complete: analyze this PR, triage that issue, test this deployment. They’re ephemeral - they execute, produce results, and disappear. But what about projects that are too big to tackle in a single run? What about initiatives that require research, setup, and incremental implementation? Traditional CI/CD is built for stateless execution, but we discovered something powerful: workflows that maintain state across days, working a little bit each day like a persistent team member who never takes breaks. Welcome to our most ambitious experiment - multi-phase improvers that prove AI agents can handle complex, long-running projects.

Multi-Phase Improver Workflows

These are some of our most ambitious agents - they tackle big projects over multiple days:

Daily Backlog Burner - Systematically works through issues and PRs, one day at a time
Daily Perf Improver - Three-phase performance optimization (research, setup, implement)
Daily Test Improver - Identifies coverage gaps and implements new tests incrementally
Daily QA - Continuous quality assurance that never sleeps
Daily Accessibility Review - WCAG compliance checking with Playwright
PR Fix - On-demand slash command to fix failing CI checks (super handy!)

This is where we got experimental with agent persistence and multi-day workflows. Traditional CI runs are ephemeral, but these workflows maintain state across days using repo-memory. The Daily Perf Improver runs in three phases - research (find bottlenecks), setup (create profiling infrastructure), implement (optimize). It’s like having a performance engineer who works a little bit each day. The Daily Backlog Burner systematically tackles our issue backlog - one issue per day, methodically working through technical debt. We learned that incremental progress beats heroic sprints - these agents never get tired, never get distracted, and never need coffee breaks. The PR Fix workflow is our emergency responder - when CI fails, invoke /pr-fix and it investigates and attempts repairs.

These workflows prove that AI agents can handle complex, long-running projects when given the right architecture.

Learn More

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

Next Up: Organization & Cross-Repo Workflows

Single-repository workflows are powerful, but what happens when you scale to an entire organization with dozens of repositories?

Continue reading: Organization & Cross-Repo Workflows →

This is part 13 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Testing & Validation

Jan 13, 2026

Let’s continue our tour of Peli’s Agent Factory!

In our previous post, we explored interactive and ChatOps workflows - agents that respond to slash commands and GitHub reactions, providing on-demand assistance with full context. We learned that context is king: the right agent at the right moment is far more valuable than scheduled runs.

But making code better is only half the battle. We also need to ensure it keeps working. As we refactor, optimize, and evolve our codebase, how do we know we haven’t broken something? How do we catch regressions before users do? That’s where testing and validation workflows come in - the skeptical guardians that continuously verify our systems still function as expected. We learned the hard way that AI infrastructure needs constant health checks, because what worked yesterday might silently fail today. These workflows embody trust but verify.

Testing & Validation Workflows

These agents keep everything running smoothly through continuous testing:

Smoke Tests - Validate that engines and firewall are working (running every 12 hours!)
Daily Multi-Device Docs Tester - Tests documentation across devices (mobile matters!)
CI Coach - Provides friendly guidance on CI/CD improvements

We learned the hard way that AI infrastructure needs constant health checks. The Smoke Tests run every 12 hours to validate that our core systems (engines, firewall, MCP servers) are actually working. It’s caught outages before users noticed them. The Multi-Device Docs Tester uses Playwright to test our documentation on different screen sizes - it found mobile rendering issues we never would have caught manually. The CI Coach analyzes our CI/CD pipeline and suggests optimizations (“you’re running tests sequentially when they could be parallel”).

These workflows embody the principle: trust but verify. Just because it worked yesterday doesn’t mean it works today.

Learn More

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

Next Up: Monitoring the Monitors

But what about the infrastructure itself? Who watches the watchers? Time to go meta.

Continue reading: Tool & Infrastructure Workflows →

This is part 11 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Interactive & ChatOps

Jan 13, 2026

Let’s keep exploring Peli’s Agent Factory!

In our previous post, we explored creative and culture workflows - agents that bring joy, build team culture, and create moments of delight. We discovered that AI agents don’t have to be all business; they can have personality while making work more enjoyable.

But sometimes you need help right now, at the exact moment you’re stuck on a problem. You don’t want to wait for a scheduled run - you want to summon an expert agent with a command. That’s where interactive workflows and ChatOps come in. These agents respond to slash commands and GitHub reactions, providing on-demand assistance with full context of the current situation. We learned that context is king - the right agent at the right moment with the right information is far more valuable than a dozen agents running on cron schedules.

Interactive & ChatOps Workflows

These agents respond to commands, providing on-demand assistance whenever you need it:

Q - Workflow optimizer that investigates performance and creates PRs
Grumpy Reviewer - Performs critical code reviews with, well, personality
Workflow Generator - Creates new workflows from issue requests

Interactive workflows changed how we think about agent invocation. Instead of everything running on a schedule, these respond to slash commands and reactions - /q summons the workflow optimizer, a 🚀 reaction triggers analysis. Q (yes, named after the James Bond quartermaster) became our go-to troubleshooter - it investigates workflow performance issues and opens PRs with optimizations.

The Grumpy Reviewer gave us surprisingly valuable feedback with a side of sass (“This function is so nested it has its own ZIP code”). We learned that context is king - these agents work because they’re invoked at the right moment with the right context, not because they run on a schedule.

Learn More

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

Next Up: Testing & Validation Workflows

While ChatOps agents respond to commands, we also need workflows that continuously verify our systems still function as expected.

Continue reading: Testing & Validation Workflows →

This is part 10 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Creative & Culture

Jan 13, 2026

Let’s explore the fun side of Peli’s Agent Factory!

In our previous post, we explored security and compliance workflows - the essential guardrails that manage vulnerability campaigns, validate network security, and prevent credential exposure. These workflows let us sleep soundly knowing our agents operate within safe boundaries.

But here’s the thing: work doesn’t have to be all business. While we’ve built serious, production-critical workflows for quality, releases, and security, we also discovered something unexpected - AI agents can bring joy, build team culture, and create moments of delight. Not every workflow needs to solve a critical problem; some can simply make your day better. Let’s explore the playful side of our agent factory, where we learned that personality and fun drive engagement just as powerfully as utility.

Creative & Culture Workflows

Not everything needs to be serious! These agents remind us that work can be fun:

Poem Bot - Responds to /poem-bot commands with creative verses (yes, really)
Daily Team Status - Shares team mood and status updates
Daily News - Curates relevant news for the team

Okay, hear us out: the Poem Bot started as a joke. Someone said “wouldn’t it be funny if we had an agent that writes poems about our code?” and then we actually built it. And you know what? People love it. It became a team tradition to invoke /poem-bot after particularly gnarly PR merges. We learned that AI agents don’t have to be all business - they can build culture and create moments of joy.

The Daily News workflow curates relevant articles, but it also adds commentary and connects them to our work. These “fun” workflows have higher engagement than some of our “serious” ones, which tells you something about what makes people actually want to interact with automation.

Learn More

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

Next Up: Summon an Agent on Demand

Scheduled workflows are great, but sometimes you need help right now. Enter ChatOps and interactive workflows.

Continue reading: Interactive & ChatOps Workflows →

This is part 9 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Security & Compliance

Jan 13, 2026

Great to have you back at Peli’s Agent Factory!

In our previous post, we explored operations and release workflows that handle the critical process of shipping software - building, testing, generating release notes, and publishing. These workflows need to be rock-solid reliable because they represent the moment when our work reaches users.

But reliability alone isn’t enough - we also need security. When AI agents can access APIs, modify code, and interact with external services, security becomes paramount. How do we ensure agents only access authorized resources? How do we track vulnerabilities and enforce compliance deadlines? How do we prevent credential exposure? That’s where security and compliance workflows become our essential guardrails - the watchful guardians that let us sleep soundly at night.

Security & Compliance Workflows

These agents are our security guards, keeping watch and enforcing the rules:

Security Compliance - Runs vulnerability campaigns with deadline tracking
Firewall - Tests network security and validates rules
Daily Secrets Analysis - Scans for exposed credentials (yes, it happens)

Security workflows were where we got serious about trust boundaries. The Security Compliance agent manages entire vulnerability remediation campaigns with deadline tracking - perfect for those “audit in 3 weeks” panic moments. We learned that AI agents need guardrails just like humans need seat belts.

The Firewall workflow validates that our agents can’t access unauthorized resources, because an AI agent with unrestricted network access is… let’s just say we sleep better with these safeguards. These workflows prove that automation and security aren’t at odds - when done right, automated security is more consistent than manual reviews.

Learn More

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

Next Up: Creative & Culture Workflows

After all this serious infrastructure talk, let’s explore the fun side: agents that bring joy and build team culture.

Continue reading: Creative & Culture Workflows →

This is part 8 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Operations & Release

Jan 13, 2026

Welcome to another stop in Peli’s Agent Factory!

In our previous post, we explored metrics and analytics workflows - the agents that monitor other agents, turning raw activity data into actionable insights. We built our central nervous system for the agent ecosystem, learning that you can’t optimize what you don’t measure.

Now comes the moment of truth: actually shipping software to users. All the quality checks, metrics tracking, and iterative improvements culminate in one critical process - the release. Operations and release workflows handle the orchestration of building, testing, generating release notes, and publishing. These workflows can’t afford to be experimental; they need to be rock-solid reliable, well-tested, and yes, even a bit boring. Let’s explore how automation makes shipping predictable and stress-free.

Operations & Release Workflows

The agents that help us actually ship software:

Release - Orchestrates builds, tests, and release note generation
Daily Workflow Updater - Keeps actions and dependencies current (because dependency updates never stop)

Shipping software is stressful enough without worrying about whether you formatted your release notes correctly. The Release workflow handles the entire orchestration - building, testing, generating coherent release notes from commits, and publishing. What’s interesting here is the reliability requirement: these workflows can’t afford to be creative or experimental. They need to be deterministic, well-tested, and boring (in a good way).

The Daily Workflow Updater taught us that maintenance is a perfect use case for agents - it’s repetitive, necessary, and nobody enjoys doing it manually. These workflows handle the toil so we can focus on the interesting problems.

Learn More

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

Next Up: Security & Compliance Workflows

After all this focus on shipping, we need to talk about the guardrails: how do we ensure these powerful agents operate safely?

Continue reading: Security & Compliance Workflows →

This is part 7 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Metrics & Analytics

Jan 13, 2026

Let’s dive deeper into Peli’s Agent Factory!

In our previous post, we explored quality and hygiene workflows - the vigilant caretakers that investigate failed CI runs, detect schema drift, and catch breaking changes before users do. These workflows maintain codebase health by spotting problems before they escalate.

But here’s a question: when you’re running dozens of AI agents, how do you know if they’re actually working well? How do you spot performance issues, cost problems, or quality degradation? That’s where metrics and analytics workflows come in - they’re the agents that monitor other agents, turning raw activity data into actionable insights. This is where we got meta and built our central nervous system.

Metrics & Analytics Workflows

Data nerds, rejoice! These agents turn raw repository activity into actual insights:

Metrics Collector - Tracks daily performance across the entire agent ecosystem
Portfolio Analyst - Identifies cost reduction opportunities (because AI isn’t free!)
Audit Workflows - A meta-agent that audits all the other agents’ runs - very Inception

Here’s where things got meta: we built agents to monitor agents. The Metrics Collector became our central nervous system, gathering performance data that feeds into higher-level orchestrators. What we learned: you can’t optimize what you don’t measure. The Portfolio Analyst was eye-opening - it identified workflows that were costing us money unnecessarily (turns out some agents were way too chatty with their LLM calls).

These workflows taught us that observability isn’t optional when you’re running dozens of AI agents - it’s the difference between a well-oiled machine and an expensive black box.

Learn More

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

Next Up: Operations & Release Workflows

Now that we can measure and optimize our agent ecosystem, let’s talk about the moment of truth: actually shipping software to users.

Continue reading: Operations & Release Workflows →

This is part 6 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Quality & Hygiene

Jan 13, 2026

Welcome back to Peli’s Agent Factory!

In our previous post, we explored issue and PR management workflows - agents that enhance GitHub’s collaboration features by removing tedious ceremony like linking related issues, merging main branches, and optimizing templates. These workflows make GitHub more pleasant to use by eliminating small papercuts that add up to significant friction.

Now let’s shift from collaboration ceremony to codebase maintenance. While issue workflows help us handle what comes in, quality and hygiene workflows act as vigilant caretakers - spotting problems before they escalate and keeping our codebase healthy. These are the agents that investigate failed CI runs, detect schema drift, and catch breaking changes before users do.

Quality & Hygiene Workflows

These are our diligent caretakers - the agents that spot problems before they become, well, bigger problems:

CI Doctor - Investigates failed workflows and opens diagnostic issues (it’s like having a DevOps specialist on call 24/7)
Schema Consistency Checker - Detects when schemas, code, and docs drift apart
Breaking Change Checker - Watches for changes that might break things for users

The CI Doctor was a revelation. Instead of drowning in CI failure notifications, we now get timely, investigated failures with actual diagnostic insights. The agent doesn’t just tell us something broke - it analyzes logs, identifies patterns, searches for similar past issues, and even suggests fixes. We learned that agents excel at the tedious investigation work that humans find draining.

The Schema Consistency Checker caught drift that would have taken us days to notice manually.

These “hygiene” workflows became our first line of defense, catching issues before they reached users.

Learn More

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

Next Up: Metrics & Analytics Workflows

With quality and hygiene workflows maintaining our codebase health, we needed a way to understand whether they were actually working. How do you know if your agents are performing well? That’s where metrics and analytics come in.

Continue reading: Metrics & Analytics Workflows →

This is part 5 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Documentation & Content

Jan 13, 2026

Welcome to the documentation corner of Peli’s Agent Factory!

In our previous post, we explored code quality and refactoring workflows - agents that continuously push our codebase toward better design, finding patterns and improvements that humans often miss. These workflows never take a day off, quietly working to make our code cleaner and more maintainable.

Now let’s address one of software development’s eternal challenges: keeping documentation accurate and up-to-date. Code evolves rapidly; docs… not so much. Terminology drifts, API examples become outdated, slide decks grow stale, and blog posts reference deprecated features. The question isn’t “can AI agents write good documentation?” but rather “can they maintain it as code changes?” Documentation and content workflows challenge conventional wisdom about AI-generated technical content. Spoiler: the answer involves human review, but it’s way better than the alternative (no docs at all).

Documentation & Content Workflows

These agents maintain high-quality documentation and content:

Glossary Maintainer - Keeps glossary synchronized with codebase
Technical Doc Writer - Generates and updates technical documentation
Slide Deck Maintainer - Maintains presentation slide decks
Multi-device Docs Tester - Tests documentation site across mobile, tablet, and desktop devices

Documentation is where we challenged conventional wisdom. Can AI agents write good documentation?

The Technical Doc Writer generates API docs from code, but more importantly, it maintains them - updating docs when code changes. The Glossary Maintainer caught terminology drift (“we’re using three different terms for the same concept”).

The Slide Deck Maintainer keeps our presentation materials current without manual updates.

The Multi-device Docs Tester uses Playwright to verify our documentation site works across phones, tablets, and desktops - testing responsive layouts, accessibility, and interactive elements. It catches visual regressions and layout issues that only appear on specific screen sizes.

The Blog Auditor ensures our blog posts stay accurate as the codebase evolves - it flags outdated code examples and broken links.

AI-generated docs need human review, but they’re dramatically better than no docs (which is often the alternative). Validation can be automated to a large extent, freeing writers to focus on content shaping, topic, clarity, tone, and accuracy.

Learn More

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

Next Up: Issue & PR Management Workflows

Beyond writing code and docs, we need to manage the flow of issues and pull requests. How do we keep collaboration smooth and efficient?

Continue reading: Issue & PR Management Workflows →

This is part 3 of a 16-part series exploring the workflows in Peli’s Agent Factory.

Meet the Workflows: Triage & Summarization

Jan 13, 2026

Hi there! 👋 Welcome back 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. This represents the “hello world” of automated agentic workflows: practical, immediately useful, relatively simple, and impactful.

Our Issue Triage Agent 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.

Welcome to Peli's Agent Factory

Jan 12, 2026