Why AI agent evaluation needs two layers

Agent failures aren't binary. They often complete tasks while returning subtly wrong results - what industry calls 'corrupted data passes.' Traditional monitoring misses these failures entirely.

The failure rates

Evaluation Coverage	Rollback Rate
None	47%
Full	9%

The solution components

1. Trajectory metrics that log:

   • Every reasoning step
   • Tool selections
   • Decision points

2. Outcome metrics that verify:

   • Task completion
   • Latency bounds
   • Basic validation

Implementation checklist

• [ ] Calibrate evaluation judges first
• [ ] Wire checks into CI/CD
• [ ] Run adversarial tests pre-deployment
• [ ] Monitor trajectories in production

Teams skipping these steps face the harsh reality: 40% of agent projects get canceled by 2027.