> Building Production AI Agents: Lessons Learned

After months of building AI agents for production use cases, here are the hard-won lessons I've learned along the way.

Lesson 1: Start with the Simplest Agent That Works

It's tempting to build a multi-model, multi-tool, memory-enhanced super-agent from day one. Don't.

Start with a single model, a single tool, and a clear objective. You'll learn more from deploying a simple agent than from architecting a complex one that never ships.

Lesson 2: Observability Is Non-Negotiable

When an agent makes a decision, you need to know why. Every agent call should log:

• The input context
• The model's reasoning
• Which tools were called and their results
• The final output

Without this, debugging agent failures is like debugging a program without stack traces.

Lesson 3: Guardrails > Prompts

Prompt engineering gets you 80% of the way. The last 20% — the part that matters in production — comes from structural guardrails:

• Input validation: Reject malformed or adversarial inputs before they hit the model
• Output parsing: Use structured outputs (JSON schemas, TypeScript types) instead of hoping for the right format
• Fallback chains: When the primary model fails, have a deterministic fallback

Lesson 4: Cost Management Is a Feature

AI API costs can spike fast. Implement:

• Token budgets per request
• Caching for repeated queries
• Model tiering (use smaller models for simple tasks)

Lesson 5: Users Don't Care About the AI

They care about the outcome. The best AI features are the ones users don't even realize are powered by AI. Focus on the user experience, not the technology.

What's Next

I'm continuing to push the boundaries of what's possible with agentic systems. Follow along on this blog for more deep dives into specific patterns, architectures, and real-world implementations.