Skip to content

Test the work and the way it gets done

Engineering field guide · Evaluation · Both days

How do you know an agent is doing useful work?

Day 1 builds benchmarks from real tasks. Day 2 adds behavior specs and regression tests for decisions inside long agent runs.

All topic guides · Practical takeaways

Bridgewater, Vals AI, and Basis all make quality explicit. Real tasks and examples of accepted work give you a standard that a generic model ranking cannot supply.

Session Approach
Bridgewater · Day 1 Measures retrieval with a small labeled question set. Precision at ten checks the usefulness of a search result list.
Vals AI · Day 1 Replays completed tasks to compare success, cost, and speed. Partial credit and fully correct answers support different deployment decisions.
Basis · Day 2 Adds behavior specs for the decisions inside a long run. These help inspect trajectories when you cannot label every possible final outcome.

Bridgewater · Day 1 · Vals AI · Day 1 · Basis · Day 2

A behavior check can explain a failure without proving the final answer is correct. Bridgewater’s reported search improvement and Vals’ spending reduction describe their own evaluations. Neither predicts your success rate.

  1. Choose twenty completed tasks and keep their accepted outputs as references.
  2. Run two agent configurations on the same tasks. Grade final results against a written rubric.
  3. Inspect failed runs against one behavior spec, such as checking a source before making a claim.

Record fully correct tasks, behavior violations, reviewer time, and cost per accepted result. Keep some tasks out of prompt tuning.

The comparison and experiment are our editorial synthesis. They are not a reported benchmark or the speakers’ prescribed procedure.