If you’re building a system that doesn’t just suggest but acts — remediates code, edits a config, resolves a ticket — you eventually hit a question no benchmark answers: at what quality does it earn the right to act without a human in the loop? I ran product for an AI security remediation portfolio, where a wrong autonomous fix isn’t a bad suggestion — it’s a broken production system and a developer who never trusts the tool again. This is the reasoning I used, made usable.
← Work
Interactive tool · Agentic evaluation
When should an agent be allowed to act?
A sandbox for the one decision every team shipping an autonomous agent has to make — and usually makes by feel.
Agentic remediation — trust sandboxDrag the sliders. Everything updates live.
Your system
Fix correctness88%
Of the fixes it produces, how many actually resolve the issue.
Regression safety97%
How often a fix doesn’t break something that worked.
False-positive rate6%
Of what it flags, how much wasn’t real.
Volume100 / wk
Fixes shipped per week.
Autonomy
How much reaches production without a human looking first.
Human gate on risky changes
A reviewer checks the highest-risk slice before it ships.
What happens to trust
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Developer trust after 12 weeks (0–100)
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Net value / week
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Regressions shipped / week
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False positives seen / week
Trust over 12 weeks · dashed line = the floor below which developers stop applying fixes
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What’s modelled and what’s fake here, honestly. The coefficients are illustrative constants I chose to make the dynamics legible, not measurements from any system. What I’d stand behind is the structure: trust is asymmetric, false positives are underpriced by most teams, autonomy should trail trust rather than lead it, and one shared definition of “good” is what makes any of this comparable across products. Replace the numbers with yours.
The thinking behind this