How to build eval suites that catch agent regressions across capability, safety, and cost.
11 min · Reviewed 2026
The premise
AI agent eval requires measuring not just final answers but trajectories — tool sequences, token costs, latency, and recovery behavior — across canonical task suites.
What AI does well here
Producing trace logs of every tool call and reasoning step
Following test scenarios with deterministic seeds when configured
Reporting structured success/failure indicators per subtask
Replicating prior runs when given identical inputs
What AI cannot do
Generate genuinely adversarial test cases against itself
Self-evaluate without bias toward its own outputs
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
Ask AI to explain trajectory eval in plain language, then underline anything that sounds uncertain or too broad.
Give it one detail from "AI Agent Evaluation Harnesses: Beyond Pass/Fail" and ask for two possible next steps plus one reason each step might be wrong.
Check cost regression against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson check
10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-agentic-evaluation-harnesses-final5-creators
What is the main idea of "AI Agent Evaluation Harnesses: Beyond Pass/Fail"?
How to build eval suites that catch agent regressions across capability, safety, and cost.
Use AI as the final authority for the whole decision
Avoid checking the answer once it sounds polished
Focus only on speed instead of judgment
Which concept is most central to "AI Agent Evaluation Harnesses: Beyond Pass/Fail"?
cost regression
trajectory eval
safety probes
unrelated shortcut
Which use of AI fits this topic best?
Generate genuinely adversarial test cases against itself
Let the AI decide what matters without your review
Producing trace logs of every tool call and reasoning step
Use the answer before checking whether it fits the situation
Which limitation should you watch for in this topic?
Producing trace logs of every tool call and reasoning step
Explain the topic in plain language
Organize a draft for human review
Generate genuinely adversarial test cases against itself
What should a careful learner remember about "Pattern: trajectory diff over flagship updates"?
Use AI to draft or organize ideas about trajectory eval, then verify before acting.
Skip the context so the tool can guess faster
Treat the output as private even after sharing it online
Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
Act immediately because the AI answer is written clearly
Use AI for drafting and comparison, but verify before publishing or relying on it.
Hide uncertainty so the final answer looks cleaner
Use private or sensitive details before checking permission
How should AI output about trajectory eval be treated?
As proof that no other source is needed
As a replacement for context, consent, or expert review
As a draft or helper output that still needs human judgment and verification
As something that becomes correct when it sounds confident
Name one way to verify an AI answer about trajectory eval.
Which action would help you apply "AI Agent Evaluation Harnesses: Beyond Pass/Fail" responsibly?
Self-evaluate without bias toward its own outputs
Use the tool to avoid thinking through the tradeoff
Keep going even if the output conflicts with a trusted source
Following test scenarios with deterministic seeds when configured
Which choice is a bad use of AI for this lesson?
Self-evaluate without bias toward its own outputs
Producing trace logs of every tool call and reasoning step
Ask for a plain-language explanation of cost regression
