Building AI Agents That Actually Work in Production

Your AI agent works perfectly in your local environment. It responds intelligently, handles edge cases gracefully, and impresses everyone in demos. Then you deploy it to production and... it starts hallucinating prices, gets stuck in infinite loops, and somehow convinced three customers that your refund policy is "just ask nicely."

I've been there. Multiple times.

After building and deploying over a dozen AI agent systems in the past year—from customer service bots to code review assistants—I've learned that production-ready agents require a completely different mindset than proof-of-concept demos. The difference isn't just about scale; it's about predictability, control, and graceful failure.

The Production Reality Check

Here's what nobody tells you about AI agents in production: they're not autonomous systems that think for themselves. They're sophisticated pattern-matching tools that need guardrails, monitoring, and fallback strategies for every possible failure mode.

The most successful agent I've deployed handles customer support for a SaaS platform. It resolves about 73% of tickets without human intervention. But here's the key—it's designed to fail gracefully the other 27% of the time. When it encounters something outside its training, it doesn't guess. It escalates.

Constraint-Driven Development

The biggest shift in my thinking came when I stopped trying to make agents more intelligent and started making them more constrained. Production agents need boundaries—lots of them.

Making the Decision

Here's my framework for deciding when to use AI agents:

• Define your constraints first, capabilities second
• Implement confidence thresholds and escalation paths before building response generation
• Create a monitoring dashboard that tracks intent accuracy, not just response times
• Build your human-in-the-loop workflow from day one
• Test with chaos inputs, not just happy path scenarios
• Plan for failure modes—what happens when the LLM API is down?

The agents that succeed in production aren't the smartest ones. They're the most predictable, most monitored, and most willing to admit when they don't know something. In a world of AI hype, that kind of humility is surprisingly powerful.

What's been your experience with AI agents in production? I'm always curious about the failure modes others have encountered—they're often the best learning opportunities.