Building AI Agents That Actually Work

Everyone's building AI agents. Few are building AI agents that work reliably in production. After shipping dozens of agent systems for clients, we've learned what separates demos from deployable software.

The Demo Trap

Most AI agent demos follow a pattern: show a simple task, execute it perfectly, declare victory. The problem? Real-world tasks aren't simple. They have edge cases, ambiguous inputs, external dependencies, and failure modes that demos conveniently ignore.

We've seen clients come to us with "working" agent prototypes that fall apart the moment they encounter:

• Unexpected input formats
• API rate limits
• Network timeouts
• Ambiguous instructions
• Tasks that require clarification

What Actually Works

Production-grade agents need three things that demos skip:

1. Explicit Failure Modes

Every agent action should have defined failure modes and recovery strategies. Not "it might fail" but "when the API returns a 429, wait 60 seconds and retry with exponential backoff." Every edge case documented, every failure handled.

2. Human-in-the-Loop Checkpoints

For high-stakes decisions, agents should know when to pause and ask for human input. This isn't a limitation—it's a feature. The best agents are confident when they should be and humble when they're uncertain.

3. Comprehensive Logging

When an agent makes a mistake (and they will), you need to understand why. Every decision, every API call, every piece of context that influenced the output—logged and queryable. Without this, debugging is guesswork.

The Architecture That Scales

We've converged on a pattern: separate the "thinking" from the "doing." The reasoning layer (usually a large language model) decides what to do. The execution layer (deterministic code) actually does it. This separation means:

• Reasoning errors don't cause execution errors
• Execution can be retried without re-reasoning
• Each layer can be tested independently
• Costs are predictable and controllable

Start Small, Prove Value

The most successful agent projects start with a narrow scope. Not "automate our entire customer support" but "automatically categorize and route incoming tickets." Prove value on the simple case, then expand.

This approach has another benefit: it builds trust. Stakeholders who see a small agent working reliably are much more willing to expand its scope than those who watched a ambitious agent fail spectacularly.

The Bottom Line

AI agents are powerful, but they're not magic. Building ones that work in production requires the same engineering discipline as any other software: clear requirements, comprehensive error handling, extensive testing, and incremental deployment.

The teams that treat agents as "AI magic" will keep shipping demos. The teams that treat them as software will ship products.