Why AI Agent Workflows Break in Production
If you watch Twitter or LinkedIn right now, you would assume we are weeks away from full corporate automation. The demos are flawless. An AI agent reads an inbox, drafts a response, cross-references a CRM, and sends an email.
But the demos happen in a bubble.
You feed the agent clean data, it does the task, and everyone cheers. Then you put it in the real world. A customer sends an email with a weird typo. A vendor changes their invoice format. Suddenly, that “95% reliable” agent starts making mistakes.
And worse, it doesn’t tell you it made a mistake. It just keeps working.
The Compounding Error Problem
Software engineering was built on determinism. If a traditional script encounters an unexpected format, it throws an error and stops. You get an alert, you fix the bug, and you move on.
AI agents are inherently nondeterministic. They guess what you want. Most of the time, they guess right. But when they guess wrong, they don’t throw an error. They hallucinate a plausible continuation.
I’ve watched teams chain AI tools together for a single process:
Tool 1 pulls a number from a messy PDF, but gets it slightly wrong.
Tool 2 uses that wrong number to calculate a budget.
Tool 3 updates an internal dashboard with the bad math.
The final output looks completely professional. And it is completely wrong. By the time a human notices the error, it is buried three layers deep in a process no one is monitoring and potentially no one understands.
The Solution: Agentic “Fresh Eyes” Review
If you are building AI workflows today, you have to plan for bad guesses.
The traditional advice is “human in the loop.” Put a human review step right before the final action. Let the AI do the heavy lifting, but let a person hit the approve button.
But what if the volume is too high for a human to review every action? Or what if human reviewers suffer from “rubber stamp fatigue,” blindly approving AI outputs because they look correct at a glance?
If you struggle to keep a human in the loop, you can solve this agentically by using an independent “fresh eyes” agent.
Instead of chaining one agent from start to finish, you build two:
The Executor: This agent does the messy work. It extracts the data, runs the math, and formats the output.
The Auditor: This is a completely separate agent, preferably using a different model family (e.g., using Gemini to audit Claude’s work). It does not share context with the Executor. It only receives the final output and the original source document, with a strict prompt: “Find the error. Prove to me this output is completely supported by the source document.”
By forcing a second, unbiased agent to audit the work from scratch, you catch the nondeterministic hallucinations before they hit your dashboard.
Before you trust an agent in production, you must map out exactly how it fails.