AI Lab / AI Failures And Evals
Evals Before Agents
A practical note on writing small pass/fail cases before adding tools, memory, autonomy, or impressive-looking agent loops.
Post
Reading note.
Before building an AI agent, write the checks that would prove it is helping and not taking confusing action.
How to read this
Personal writing is labelled personal; external claims, quotes, and media require source context.
The Rule Of Thumb
Add an agent only after the job has visible success criteria. Otherwise the system gains more ways to be confidently busy while the product owner gains fewer ways to know what happened.
Start with examples: accepted answer, rejected answer, missing-source answer, unsafe-action answer, stale-data answer, and user-confusion answer.
- task
- expected output
- forbidden action
- source requirement
- rollback
- review path
Why This Is Not Anti-Agent
Agents are useful when the task requires state, tools, planning, or interaction with changing systems. Evals make that power inspectable.
The best agent loop has a boring receipt trail behind the impressive parts.