The Gap Between Demo and Production

Every week, we see impressive AI agent demos on Twitter. An agent that books flights. One that writes and deploys code. Another that manages your email. They look magical in 30-second videos.

Then teams try to build similar systems for their business. Six months later, they have a fragile prototype that breaks whenever users do something unexpected.

The problem isn't the AI model. It's everything around it.

Production AI agents need the same engineering rigor as any mission-critical system. The model is just one component.

Architecture Patterns That Actually Work

After building dozens of AI agent systems, we've found three patterns that consistently succeed:

1. Supervisor-Worker Architecture

Instead of one monolithic agent, use a supervisor agent that delegates to specialized workers. The supervisor handles orchestration and error recovery. Workers handle specific tasks like database queries, API calls, or content generation.

This separation makes debugging easier and failures more contained.

2. Explicit State Management

Every agent action should be logged with full context. When something fails at step 47, you need to understand what happened in steps 1-46. Use event sourcing or similar patterns to maintain a complete audit trail.

3. Graceful Degradation

AI agents will fail. Models hallucinate. APIs timeout. The question is what happens next. Build fallback paths for every critical operation. Sometimes that means asking the user for help. Sometimes it means completing a partial task.

The Reliability Stack

Production agents need infrastructure most demos ignore:

• Rate limiting and queuing for API calls
• Retry logic with exponential backoff for transient failures
• Circuit breakers to prevent cascade failures
• Monitoring and alerting for anomaly detection
• Human-in-the-loop escalation for edge cases

None of this is glamorous. All of it is essential.

When to Build vs. Buy

Not every use case needs a custom agent. For well-defined workflows with predictable inputs, existing automation tools often work better. Build custom agents when:

• Your workflow requires complex reasoning across multiple steps
• You need the agent to handle novel situations gracefully
• Integration with proprietary systems or data is essential
• The task genuinely benefits from natural language understanding

Getting Started

Start with the smallest useful scope. Build one agent that does one thing well. Prove it works in production. Then expand.

The teams that succeed treat AI agent development as software engineering, not magic. They write tests. They handle errors. They monitor everything.

That's not as exciting as a viral demo. But it's how you build systems that actually work.