Real Users Broke My AI Agent - Failures Testing Never Catches
Real Users Broke My AI Agent - Failures Testing Never Catches
Your AI agent works perfectly in demos. You have tested it on every workflow you can think of. Then real users get their hands on it and it falls apart in ways you never imagined.
The gap between "works in testing" and "works in production" is enormous for AI agents, and it is mostly about how humans actually interact with software.
Context Drop on Interruption
The biggest killer: users interrupt agents mid-task. They click somewhere else. They switch apps. They close a dialog the agent was about to interact with. They resize the window so the layout changes.
In testing, you let the agent run uninterrupted. In production, users are doing five things at once and the agent is one of them. When a user interrupts an agent's workflow, the agent's context becomes stale. It has a plan based on a screen state that no longer exists. It clicks where a button used to be. It types into a field that is now hidden.
Recovery from interruption requires detecting that the screen state has changed, re-evaluating the current step, and either resuming or replanning. Most agents do not handle this at all.
Unexpected Input Patterns
Real users do things testing never covers:
- Pasting multi-line text into single-line fields - The agent expected one line and gets five.
- Using keyboard shortcuts while the agent is clicking - Conflicting inputs create impossible states.
- Switching languages mid-workflow - Suddenly the buttons have different labels.
- Having multiple instances of the same app open - The agent picks the wrong window.
The Speed Mismatch
Users expect instant responses. Agents think for 2-5 seconds between actions. During that thinking time, users get impatient and start doing things manually. Now the agent's next planned action conflicts with what the user just did.
What Actually Helps
- Screen state validation before every action - Never assume the screen is what it was 3 seconds ago.
- Graceful interruption handling - Detect when the expected UI state is gone and pause instead of acting blindly.
- User presence awareness - If the user is actively interacting with the same app, back off and wait.
- Chaos testing - Randomly interrupt agent workflows during testing. Click on things. Switch apps. Resize windows. Break it before users do.
Test with real users as early as possible. Every week in the lab is a week of missed failure modes.
Fazm is an open source macOS AI agent. Open source on GitHub.