Building in Public with AI Agents: A Developer's Journey from Idea to Implementation

Building in public has become a popular approach for software development, but adding AI agents to the mix introduces new challenges and opportunities. Here's what I learned building an AI-powered code review assistant while sharing the entire process publicly.

The Project: CodeReview Agent

I set out to build an AI agent that automatically reviews pull requests, focusing on code quality, security issues, and maintainability. Instead of working in stealth mode, I documented everything on Twitter and GitHub from day one.

Initial Architecture

The agent follows a simple pipeline:

1. Webhook Listener: Receives GitHub pull request events
2. Code Analyzer: Extracts changed files and diffs
3. AI Processor: Uses OpenAI's API to analyze code patterns
4. Comment Generator: Posts structured feedback as PR comments
5. Learning Loop: Incorporates user reactions to improve suggestions

Week 1: The Honeymoon Phase

I started with enthusiasm, posting daily updates about progress. The initial response was encouraging—developers were curious about the technical approach and offered suggestions.

Key decisions made public:

• Chose LangChain for agent orchestration
• Decided on GitHub Actions for deployment
• Selected PostgreSQL for storing review history

The transparency helped validate these choices early. Several experienced developers pointed out potential scaling issues with my initial webhook approach, leading me to switch to a queue-based system using Redis.

Week 3: The Reality Check

Public building exposed problems I might have ignored privately:

False Positive Problem

Early versions generated too many irrelevant comments. Users complained publicly, which initially felt embarrassing but proved valuable. The feedback helped me realize that:

• Generic prompts produced generic results
• Context about the codebase was crucial
• Confidence scoring was necessary to filter suggestions

Solution: Context-Aware Analysis

# Before: Generic analysis
review_prompt = f"Review this code: {diff}"

# After: Context-aware analysis
review_prompt = f"""
Review this {file_type} code change in {repo_context}.
Focus on: {review_priorities}
Previous feedback sentiment: {user_feedback_score}
Code: {diff}
"""

Week 6: Unexpected Benefits

Community Contributions

Building publicly attracted contributors I never would have found otherwise:

• A security researcher helped identify prompt injection vulnerabilities
• A DevOps engineer contributed deployment scripts
• Multiple developers shared test cases from their own repositories

User-Driven Features

Public feedback shaped the product roadmap:

1. Custom Rule Sets: Users wanted company-specific coding standards
2. Integration Options: Requests for Slack notifications and email summaries
3. Performance Metrics: Demand for review accuracy tracking

Technical Lessons Learned

Agent Reliability

AI agents in production face different challenges than demo applications:

• Rate limiting: OpenAI's API limits required implementing exponential backoff
• Timeouts: Large pull requests needed chunking strategies
• Error handling: Graceful degradation when AI services are unavailable

Prompt Engineering at Scale

What works for one repository often fails for another:

# Effective prompt structure:
1. Clear role definition
2. Specific output format
3. Context about codebase patterns
4. Examples of good vs. bad feedback
5. Confidence indicators

Data Privacy Concerns

Public building exposed privacy considerations I hadn't fully thought through:

• Code snippets in training data
• API key management
• User consent for learning from feedback

Metrics That Mattered

Engagement Metrics

• Review acceptance rate: 67% of suggestions were marked as helpful
• False positive rate: Dropped from 45% to 12% over 8 weeks
• User retention: 78% of teams continued using after 30 days

Development Velocity

• Feature requests: Averaged 3 per week from public feedback
• Bug reports: 85% came from public users vs. private testing
• Code quality: Public scrutiny led to better documentation and testing

Challenges of Public AI Development

Managing Expectations

AI agents often produce inconsistent results, which becomes more apparent when development is public. I learned to:

• Set clear expectations about agent capabilities
• Share failure cases alongside successes
• Explain the iterative nature of AI improvement

Handling Criticism

Public building means public failures. When the agent posted obviously wrong suggestions, the response was swift and sometimes harsh. Strategies that helped:

• Acknowledge mistakes quickly
• Explain the root cause when possible
• Show concrete steps for improvement

Practical Recommendations

For AI Agent Builders

1. Start with narrow use cases: Broad agents perform poorly across contexts
2. Implement feedback loops early: User reactions are crucial training data
3. Version your prompts: Track which versions perform better over time
4. Plan for failures: AI agents will make mistakes—handle them gracefully

for Public Building

1. Share process, not just results: People want to understand your thinking
2. Be vulnerable about failures: Honest posts often get the best engagement
3. Respond to feedback quickly: Public conversations move fast
4. Document decisions: Your reasoning helps others facing similar challenges

The Outcome

After 12 weeks of public development, the CodeReview Agent is used by 50+ development teams. More importantly, the public process led to:

• A better product shaped by real user needs
• A community of contributors and advocates
• Valuable connections with other AI builders
• Greater confidence in my technical decisions

Key Takeaways

Building AI agents in public amplifies both the challenges and benefits of transparent development. The feedback loops are faster, the pressure is higher, but the results are more aligned with actual user needs.

The combination of AI's unpredictability and public accountability creates a unique environment for learning and improvement. If you're building AI tools, consider sharing your journey—the community will help you build something better than you could alone.

---

Want to follow along with similar projects? The CodeReview Agent is open source, and I continue sharing development updates and lessons learned.