Agent evals
Comes up in: AI engineering, AI system design
This concept is being written. It gets the full five steps — explanation, predict, interview angle, practice with tests, and review cards. Here is exactly what it will cover, so you can study it elsewhere in the meantime.
What it covers
- evaluating trajectories vs outcomes
- task success in sandboxed environments
- benchmarks (SWE-bench, τ-bench etc. — awareness, cite) and their limits
- cost per successful task
Words you will know
What you will practise
- outcome eval for a 3-tool agent
Interview questions this answers
- How do you evaluate an agent, not just a single response?