GitHub cut AI false positives by 95%
without ever rewriting the prompt
The five-step evaluation loop that makes a client-facing AI agent reliable enough to actually charge for.
What's inside
- The exact loop GitHub used to kill 95% of errors
- A 5-step framework: Define, Protect, Test, Diagnose, Regress
- Turn every real failure into a permanent test
- Ship agents that get better every time they break
The Eval Loop: Make Your AI Agent Reliable Enough to Bill For
by Divjot Sahni · EZYE Consulting · ezye.com.au
Why Prompt Tweaking Fails You
Your lead-qualifying bot works flawlessly in the demo
Then it says something wrong to a real client, and your name's on it
You open the prompt and start editing again, hoping this time it sticks
The reason this never ends: you're experimenting, not measuring
The fix isn't a better prompt. It's a loop that proves the agent fails less...
Step 1 — Define What 'Good' Actually Looks Like
Write down, in plain language, what a correct answer contains
Specify what the agent must never do (invent pricing, promise timelines...)
Turn each rule into a checkable condition, not a vibe
This written definition becomes the standard every test measures against...
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