Building AI Agents in Public: A Developer's Documentation Journey

Last month, I decided to build an AI agent that could automatically categorize GitHub issues. Instead of working in isolation, I documented the entire process publicly—every failed prompt, every architectural pivot, every moment of confusion.

Here's what I learned about building AI agents in the open.

Why Build in Public?

Building in public means sharing your development process as it happens, not just the polished end result. For AI agents specifically, this approach offers unique benefits:

• Rapid feedback on prompts: Community members spot edge cases you missed
• Architecture validation: Other developers catch design flaws early
• Collective debugging: Multiple perspectives on why your agent behaves unexpectedly
• Documentation by default: Your public updates become your project documentation

The GitHub Issue Categorizer: A Case Study

My agent needed to read incoming GitHub issues and assign labels like "bug," "feature-request," or "documentation." Simple concept, complex execution.

Week 1: The Naive Approach

Public update: "Starting with a basic prompt: 'Categorize this GitHub issue.'"

The first version failed spectacularly. The agent would:

• Label feature requests as bugs
• Create inconsistent categories
• Miss obvious context clues

Community feedback: "Try few-shot prompting with examples from your actual repository."

Week 2: Adding Context

Public update: "Switching to few-shot prompting with 5 examples per category."

You are a GitHub issue categorizer. Here are examples:

Issue: "App crashes when clicking submit button"
Category: bug

Issue: "Add dark mode support"
Category: feature-request

[...]

Improvement: 60% accuracy to 85% accuracy.

Community insight: "Your examples are too clean. Real issues are messier."

Week 3: Handling Edge Cases

Public update: "Adding examples with typos, unclear descriptions, and mixed requests."

Someone in the community shared their own messy issue examples. Another developer suggested adding a "needs-clarification" category for ambiguous cases.

Final accuracy: 92%.

Practical Steps for Building AI Agents in Public

1. Choose Your Documentation Platform

• GitHub repository: Perfect for code and technical discussions
• Developer blog: Great for detailed explanations and lessons learned
• Twitter/X: Quick updates and community engagement
• Discord/Slack communities: Real-time feedback and debugging help

For my project, I used a combination: GitHub for code, a weekly blog post for detailed updates, and Twitter for quick wins and failures.

2. Document Your Prompt Evolution

Keep a log of every prompt iteration:

## Prompt v1 (Failed)
"Categorize this issue."
Problem: Too vague, inconsistent results

## Prompt v2 (Better)
"You are a GitHub issue categorizer..."
Improvement: Added role and examples
Accuracy: 85%

This becomes invaluable documentation and helps others understand your reasoning.

3. Share Failures Immediately

Don't wait for success to post updates. My most engaging posts were about failures:

• "Day 3: Agent thinks every issue is a bug 🤦‍♂️"
• "Prompt engineering is harder than I thought"
• "When your AI agent becomes too confident"

Failures generate more helpful feedback than successes.

4. Create Reproducible Examples

Share minimal examples that others can run:

# Minimal test case that demonstrates the problem
test_issue = "App slow when loading large datasets"
result = categorizer.classify(test_issue)
print(f"Expected: performance, Got: {result}")

5. Ask Specific Questions

Instead of "How do I make this better?" ask:

• "Should I handle typos in the preprocessing step or the prompt?"
• "Is 92% accuracy good enough for automated labeling?"
• "Would you trust this agent with your repository?"

What Building in Public Taught Me

AI Agent Development is Iterative

Every successful AI agent I've seen goes through multiple iterations. Building in public forces you to document these iterations, making the learning visible.

Community Knowledge Accelerates Development

My final prompt incorporated feedback from 12 different developers. The collective experience saved me weeks of trial and error.

Documentation Becomes Your Product

The documentation I created while building in public became as valuable as the agent itself. Other developers referenced my prompt evolution log when building their own agents.

Challenges to Expect

Managing Feedback Overload

As your project gains attention, you'll receive conflicting advice. Create a simple framework for evaluating suggestions:

• Does it solve a problem I'm actually experiencing?
• Is it technically feasible with my current setup?
• Does it align with my project goals?

Balancing Transparency and Focus

Sharing everything can become a distraction. I found weekly updates worked better than daily ones—enough transparency without constant interruption.

Handling Criticism

Not all feedback will be constructive. Focus on technical critiques and ignore personal attacks. The AI community is generally supportive, but prepare for occasional negativity.

Getting Started

1. Pick a small AI agent project (under 2 weeks of work)
2. Set up your documentation system (GitHub + blog/social media)
3. Share your initial approach before you start coding
4. Post weekly updates with concrete examples and specific questions
5. Document failures as thoroughly as successes

The Results

After four weeks of building in public:

• My GitHub issue categorizer achieved 92% accuracy
• The project gained 200+ GitHub stars
• I connected with 30+ AI developers
• The documentation helped 5 others build similar agents
• I learned prompt engineering techniques I never would have discovered alone

Building in public transformed a solo coding project into a community learning experience. The agent was just the excuse—the real value was the shared knowledge and connections.

The next time you build an AI agent, consider taking the journey in public. The community is waiting to learn alongside you.