Building an AI Agent in Public: Lessons from Creating a Code Review Bot

Building in public means sharing your process, struggles, and wins as you create something. When that something is an AI agent, the journey becomes even more interesting—and educational.

I recently built ReviewBot, an AI agent that analyzes pull requests and provides code review feedback. Here's what I learned by sharing the entire process publicly.

Why Build in Public?

Building in public offers three key advantages for AI agent development:

• Faster feedback loops: Users spot issues and suggest improvements early
• Better problem validation: You discover real user needs before over-engineering
• Community accountability: Public commitment keeps you shipping regularly

The ReviewBot Journey

Week 1: MVP Definition

I started by sharing a simple problem statement on Twitter:

"Code reviews take forever. Building an AI agent that gives instant feedback on PRs. What would make this actually useful?"

The responses revealed three core requirements:

1. Catch obvious bugs (null checks, type errors)
2. Flag security issues (SQL injection, exposed secrets)
3. Suggest performance improvements

Week 2: Technical Architecture

I shared the initial architecture publicly:

GitHub Webhook → Queue → AI Agent → Comment API

The community immediately pointed out scaling issues:

• No rate limiting for the AI API
• Single point of failure
• No handling for large PRs

This feedback led to a more robust design with worker pools and chunked processing.

Week 3: First Working Version

I deployed a basic version that analyzed JavaScript files. The agent used GPT-4 with this prompt structure:

Analyze this code change for:
1. Potential bugs
2. Security issues
3. Performance concerns

Code:
[diff content]

Provide specific, actionable feedback.

Week 4: Real User Testing

Ten developers agreed to test ReviewBot on their repositories. The public feedback was brutal but helpful:

• Too verbose: Initial reviews were 3-4 paragraphs per issue
• False positives: Flagged intentional patterns as problems
• Missing context: Didn't understand project-specific conventions

Key Technical Decisions

Prompt Engineering

The biggest challenge was crafting prompts that produced consistent, useful output. Through public iteration, we refined the prompt to:

1. Be specific: "Check for SQL injection in database queries" vs. "Look for security issues"
2. Provide examples: Include code samples of good and bad patterns
3. Set boundaries: Explicitly state what NOT to flag

Context Management

Early versions analyzed files in isolation. User feedback showed this missed important context:

• Function calls to other files
• Configuration dependencies
• Test coverage expectations

We added a context-gathering phase that includes:

• Related file imports
• Configuration files
• Existing test patterns

Output Formatting

Users wanted actionable feedback, not essays. The final format became:

**Issue**: Brief description
**Line**: Specific line number
**Fix**: Concrete suggestion
**Priority**: High/Medium/Low

What Worked Well

Daily Progress Updates

Sharing daily progress (even small wins) kept momentum high and attracted ongoing feedback. Simple updates like "Fixed the rate limiting bug" or "Added support for TypeScript" generated valuable discussions.

Open Metrics Dashboard

I created a public dashboard showing:

• Number of PRs analyzed
• Average response time
• User satisfaction scores
• Common issue types found

This transparency built trust and helped identify improvement areas.

Community Feature Prioritization

Instead of guessing what to build next, I let the community vote on features. The top three requests were:

1. Support for more languages (Python, Go)
2. Integration with Slack notifications
3. Custom rule configuration

What I'd Do Differently

Start with Narrower Scope

Trying to handle all programming languages from day one was ambitious but unfocused. Starting with just JavaScript and expanding based on demand would have been smarter.

Set Clearer Expectations

Early users expected human-level code review quality. Better upfront communication about AI limitations would have prevented disappointment.

Build Feedback Loops Into the Product

I collected feedback manually through Twitter and email. Building rating/feedback mechanisms directly into ReviewBot would have scaled better.

Results After 6 Months

• 50+ active users across various open source projects
• 1,200+ PRs analyzed with 78% user satisfaction
• 15 languages supported (expanded from initial JavaScript)
• 3 spin-off projects by community members

Practical Takeaways

For AI Agent Development

1. Start with narrow, specific use cases: Broad agents are harder to evaluate and improve
2. Build feedback collection early: You need data on when the AI helps vs. hurts
3. Version your prompts: Track which prompt versions perform better
4. Plan for context limitations: AI models have token limits; design around them

For Building in Public

1. Share problems, not just solutions: People engage more with challenges than perfect demos
2. Document failures: What didn't work is often more valuable than what did
3. Create specific feedback requests: "What do you think?" gets worse responses than "Should I prioritize speed or accuracy?"
4. Set regular sharing schedules: Consistency matters more than perfection

Next Steps

The journey continues. Current focus areas based on community feedback:

• Smarter context selection: Only include relevant context to reduce token usage
• Learning from corrections: When users override suggestions, feed that back into the model
• Integration marketplace: Let users add custom analysis rules

Building ReviewBot in public transformed both the product and my development process. The constant feedback loop created something more useful than I could have built alone—and the shared journey helped others learn about AI agent development too.

The key is consistency: ship, share, learn, repeat.