Building in Public with AI Agents: What I Learned After 90 Days

Three months ago, I started building an AI agent that helps developers debug deployment issues. Instead of working in silence, I decided to share the entire process publicly on Twitter and GitHub.

Here's what actually happened—and what I'd do differently next time.

Why Build AI Agents in Public?

Building in public means sharing your progress, challenges, and learnings openly as you develop your product. For AI agents specifically, this approach offers unique advantages:

• Real-time feedback on agent behavior: Users spot edge cases you miss
• Trust building: People see how your agent actually works, not just marketing claims
• Community-driven testing: Others try your agent in scenarios you haven't considered
• Learning in public: AI development moves fast—sharing knowledge helps everyone

The Reality: Week-by-Week Breakdown

Weeks 1-2: The Honeymoon Phase

I shared my initial concept: an agent that reads deployment logs, identifies common failure patterns, and suggests fixes. The response was encouraging—lots of "this would be useful" comments.

What worked:

• Simple, clear problem statement
• Visual mockups of the agent's workflow
• Honest admission that this was day one

What didn't:

• Overestimating how quickly I'd have something to demo
• Not setting clear expectations about timeline

Weeks 3-6: The Messy Middle

This is where building AI agents gets real. My agent could parse logs but kept hallucinating fixes for problems that didn't exist. I shared these failures openly.

Key challenges:

• Agent reliability varied wildly between different log formats
• Cost per query was higher than expected ($0.15-0.30)
• Users wanted instant responses, but quality required slower, multi-step reasoning

Valuable feedback from the community:

• "Can it handle Kubernetes logs?" (No, but now it does)
• "What about rate limiting?" (Hadn't considered this)
• "Show the agent's reasoning process" (Game-changer for trust)

Weeks 7-12: Finding Product-Market Fit

By month three, patterns emerged from public feedback. Users didn't just want automated fixes—they wanted to understand the debugging process.

I pivoted to an agent that:

1. Analyzes deployment logs
2. Shows its step-by-step reasoning
3. Suggests fixes with confidence levels
4. Explains why each fix might work

Concrete Example: The Kubernetes Debug Session

Here's a real interaction I shared publicly that changed how I thought about agent design:

User input: "My pod keeps crashing with exit code 137"

Agent v1 response: "Add more memory to your container"

Community feedback: "That's not always right. Show your work."

Agent v2 response:

Analyzing exit code 137...

1. Exit code 137 = 128 + 9 (SIGKILL)
2. SIGKILL usually means:
   - Out of memory (OOMKilled)
   - Manual termination
   - Resource limits exceeded

3. Checking your logs for OOM indicators...
   ✓ Found: "Killed process 1234 (java) total-vm:2048MB"

4. Recommendation (confidence: 85%):
   Increase memory limit from 1GB to 2GB
   
Why this should work:
- Your Java process is using 2GB but limited to 1GB
- OOMKiller is terminating the process
- 2GB limit provides reasonable headroom

This transparent approach built significantly more trust with users.

Practical Strategies That Worked

1. Share Failures Loudly

When my agent suggested deleting a production database to fix a connection issue, I tweeted about it immediately. The community helped me understand that my prompts were too aggressive about "fixing" problems.

2. Use Real Data (Anonymized)

I regularly shared anonymized examples of my agent working on real deployment issues. This showed both successes and limitations honestly.

3. Build Feedback Loops Into the Product

I added a "Was this helpful?" button with optional comments to every agent response. Users could flag problematic suggestions directly in the interface.

4. Document Decision-Making Process

I maintained a public changelog explaining why I made specific architectural choices:

• Why I chose GPT-4 over Claude for reasoning tasks
• How I structured prompts to reduce hallucinations
• When I decided to add human-in-the-loop confirmation for destructive actions

What I'd Do Differently

Start with Narrower Use Cases

I initially tried to handle all deployment failures. Better approach: Start with one specific failure type (like OOM issues) and expand gradually.

Set Clearer Boundaries Early

Users expected my agent to handle infrastructure provisioning, code debugging, and performance optimization. I should have defined scope upfront.

Invest More in Evaluation Framework

I was manually testing agent responses. Building automated evaluation early would have caught more issues before public release.

Tools and Platforms That Helped

• Twitter: Best for quick updates and getting fast feedback
• GitHub: Essential for technical discussions and issue tracking
• Loom: Video demos showed agent behavior better than text
• Linear: Public roadmap kept community aligned with priorities
• Discord: Real-time debugging sessions with power users

Measuring Success Beyond Downloads

Traditional metrics like user count matter, but for AI agents built in public, I tracked:

• Feedback quality: How specific and actionable were user suggestions?
• Community contributions: Did people submit prompts, test cases, or bug reports?
• Trust indicators: Were users sharing their real production issues?
• Retention with transparency: Did showing reasoning steps improve user retention?

The Bottom Line

Building AI agents in public is messier and slower than building in private. But the end product is significantly better.

My agent went from a glorified log parser to a debugging companion that users actually trust with production issues. This happened because hundreds of people saw it fail, pointed out edge cases, and suggested improvements.

The key is being genuinely transparent about limitations while maintaining momentum. Share the failures, celebrate the small wins, and let your community help you build something actually useful.

Getting Started

If you're considering building AI agents in public:

1. Start before you're ready: Share your concept and early prototypes
2. Define your feedback loop: How will users report issues and suggestions?
3. Pick your platforms: Choose 2-3 channels and commit to regular updates
4. Prepare for criticism: Not all feedback will be constructive, but most will be valuable
5. Document everything: Your future self will thank you for detailed notes

Building in public isn't just a marketing strategy—it's a product development methodology that works especially well for AI agents, where user trust and real-world testing are critical for success.