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Sources
An agent saying it's done isn't proof. Check its output with a reviewer that never saw its reasoning
Metrics & Experimentation
- Deployment accounts argue that reliability is the measure, since a task completing once says little about the hundredth run.
- One judges an agent by the worst thing it does.
- Another reports a triage agent that beat the humans it replaced and was abandoned anyway after one unexplainable mistake.
- These are separate accounts reaching a similar conclusion from different failures.
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First seen 8 June 2026
If you are a PM
- Judge an agent by its worst result over many runs, not by a demo that worked once.
- Give the checker no access to the agent's reasoning. A reviewer who saw the reasoning inherits its assumptions.
- Plan for the case where an agent beats the humans and still gets switched off after one mistake nobody can explain.
If you are a designer
- Design the review screen around the output, not the agent's story of how it got there.
- Show the person the run history, not only the latest result. One bad run costs more than ten good ones.
Who said it
r/AI_Agentsreddit.com · 8 September 2026 · 6 threads
Controversial take about the quality of the code generated by AI