AI agents are having a moment. Everyone’s building them. Most of them are demos that fall apart in production.

The gap between “impressive demo” and “reliable system” is enormous — and it’s a gap most tutorials don’t address. This is what I’ve learned building agents that actually get used.

The Core Problem

Agents fail in production for two main reasons:

  1. Unbounded action spaces — the agent can do too many things, so it tries unexpected things
  2. Missing error handling — when a tool fails, the agent halts or spirals

The fix for both is the same: constraints.

Design Principles That Work

Keep the action space small

Every tool you give an agent is another surface for failure. Start with the minimum viable set of tools. You can always add more.

If your agent needs to “manage email,” don’t give it full Gmail access. Give it: read_email, draft_reply, send_draft. Three tools. Specific. Auditable.

Make tools idempotent

Your agent will call tools multiple times. Design them so calling the same tool twice with the same inputs doesn’t cause problems. This is especially important for anything that writes data.

Build in human checkpoints

For consequential actions — sending emails, modifying databases, making purchases — add a confirmation step. “I’m about to do X. Confirm?” This catches errors before they’re expensive.

A Pattern That Scales

The most reliable agents I’ve built follow this pattern:

Plan → Clarify → Execute → Verify → Report

The agent plans its approach, flags anything unclear, executes one step at a time, verifies each result, and summarizes what happened.

It’s slower than giving the agent full autonomy. It’s also the reason it works reliably.

The Guardrails Layer

Every production agent should have a layer that checks outputs before they’re acted upon. Simple things:

  • Does this output make sense for the context?
  • Is it within expected parameters?
  • Would a human flag this as suspicious?

You can implement this as a second LLM call that reviews the first one’s work. The cost is low. The reliability improvement is significant.

Closing Thought

The best AI agents are boring. They do exactly what you expect, every time. The exciting part is what they enable — not how they work.