Building AI Agents That Actually Work

Most AI agents fail in production — not because the underlying models are weak, but because the architecture around them is fragile. Getting an agent to work in a demo is easy. Getting one to work reliably at scale is a different problem entirely.

What Makes an Agent Different from a Chatbot

A chatbot responds. An agent acts. The difference is the loop: an agent can call tools, observe results, reason about the next step, and repeat until the task is done. This loop is powerful — and it is exactly what makes agents hard to get right.

Prompt Design Is Architecture

The system prompt is not just instructions. It is the agent's mental model of itself, its constraints, and its relationship to the tools it can use. Vague prompts produce erratic behavior. Precise prompts — with explicit descriptions of what the agent should and should not do — produce reliable ones.

"The quality of an AI agent is directly proportional to the clarity of its constraints."

Tool Use and Failure Modes

Every tool call is a point of failure. Network timeouts, malformed responses, unexpected schemas — agents must handle all of these gracefully. Build retry logic. Build fallback paths. Surface errors clearly rather than silently swallowing them.

Evaluation Is Not Optional

You cannot improve what you cannot measure. Build an eval suite before you ship: automated tests that probe edge cases, measure accuracy, and catch regressions. A ship-and-pray approach to AI agents is a support ticket waiting to happen.

Conclusion

Build small. Test everything. Constrain aggressively. Evaluate continuously. Agents that work in production are boring by design — and that is exactly the point.