This is really cool.
TL;DR: Basis and Braintrust are creating an open standard for evaluating long-running agents based not only on what they accomplish, but on whether they follow a reliable process along the way.
If you don't know what that means, I'll try to explain:
Agent evals are tests that measure how well an AI agent performs. They’re important because agents can behave differently each time, and one mistake can affect everything that follows.
AI teams use evals to check whether an agent is reliable and safe enough for real-world use, and whether changes actually improve it.
Most agent evaluations focus on outcomes: Did the agent produce the correct answer and/or successfully complete the task?
That works when success is easy to verify, like a math problem against an answer, if an appointment was booked, or if a game-playing agent won a game.
But many tasks take hours or days, have hundreds of decisions, and don’t actually have a clean answer.
An agent might reach the right result while following an unreliable process... if we only grade the outcome, we lose the signal about what actually went right or wrong along the way.
Basis is creating behavior specs: reusable descriptions of how an agent should operate across an entire trajectory.
For example, a tax agent might be required to check the latest official tax rules before giving an answer.
A judge (another AI model acting as an evaluator) can then review what the agent did and determine whether it followed that rule to let teams evaluate the agent’s process even when they don’t know the perfect final answer.
Braintrust records what the agent does, evaluates whether it followed the behavior specs, and helps teams compare its performance across different models and versions.
Together, Basis and Braintrust are open-sourcing the standard so researchers can study these behaviors and use what they learn to evaluate, improve, and eventually train agents.
It’s essentially a way to grade not only what an agent accomplished, but how it operated along the way.
very cool!
@pitdesi