Lesson 1765 of 2116
Agentic AI: Build Evals That Catch Loop and Tool-Misuse Failures
Standard answer-quality evals miss agent-specific bugs; design evals that score loops, wasted tools, and abandoned subgoals.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2trajectory eval
- 3tool efficiency
- 4subgoal completion
Concept cluster
Terms to connect while reading
Section 1
The premise
An agent can get the right final answer while wasting 40 tool calls and giving up on a subgoal silently; agent evals must score the trajectory, not just the result.
What AI does well here
- Score tool-call efficiency and redundancy
- Detect loops and dead-end retries
- Check whether all subgoals were addressed
- Compare runs across model versions
What AI cannot do
- Replace user-perceived quality measurement
- Detect issues your rubric does not name
- Stand in for production monitoring
Key terms in this lesson
End-of-lesson quiz
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