Building AI Agents That Actually Ship

After building over 30 AI agents across various domains, I’ve learned that the gap between a demo and a production system is wider than most people think.

The Demo Trap

Most AI agent tutorials stop at “look, it called a function.” The real work starts when you need that agent to handle edge cases, retry gracefully, and not drain your API budget on a loop.

What Actually Matters

1. Error boundaries, not error handling. Don’t try to catch every exception. Design circuits that break cleanly.

2. Cost tracking from day one. Every LLM call has a price. I’ve seen agents burn through $200 in an afternoon because nobody instrumented the token counter.

3. Human-in-the-loop isn’t a fallback — it’s a feature. The best agents I’ve built know when to ask for help.

from langchain.agents import AgentExecutor

# The key insight: set max_iterations low and handle the timeout
agent = AgentExecutor(
    agent=agent,
    tools=tools,
    max_iterations=5,
    early_stopping_method="generate"
)

What’s Next

I’m currently exploring multi-agent orchestration with CrewAI at Gintonic AI — coordinating specialized agents that each handle one part of a complex blockchain workflow. More on that soon.