Frontier and Center: Who evaluates the evaluations?

Today, we hear from two members of Google Data Cloud's frontier AI team on the recurring challenge of how to systematically evaluate whether or not an agent is able to answer questions effectively based on its context. Read on for more, and watch this space for more blogs from this team. A passing grade is the least interesting thing an exam can tell you.

It says the student cleared the bar; leaving you entirely in the dark about how narrow their failures were, how effortless their passes were, or what to teach next. Yet this is exactly how we evaluate AI agents. We run a fixed benchmark, calculate a score, and declare progress.

In doing so, we are handing our agents a pass/fail exam when what we actually need is a map of the agent's capabilities: a picture of the terrain that shows exactly where capability falls off, and by how much. For data agents, this map matters a lot for data discovery in search and retrieval - the unglamorous first step where an agent, handed a vague human question and a warehouse or data lake of thousands of tables and files, has to find the right datasets before it can reason over anything. Discovery is a "needle in a haystack" problem.

Real users phrase their questions imperfectly, and inferring what datasets to retrieve presents a real challenge to agents. So the interesting question in evaluations is never "can the agent pass? " It is "how vague can the question get before the agent breaks?

" An exam cannot easily answer that, but a map can. Today, we share an approach rooted in information theory that we've been leveraging to add detail and nuance, i. , fidelity, to benchmarks, so we can better understand agents' performance as a part of their evaluations.

Along the way, the added fidelity exposed some deeper issues with the quality of emergent evaluation cases themselves. Difficulty, measured When it comes to retrieval, evaluation cases are often stratified into tiers of difficulty. This can happen organically, e.

, pervasive and enduring failure scenarios are deemed difficult. Or it can be from labels applied by humans or machines categorizing some questions as "easy" or "hard" for an agent to answer correctly, e. , based on the context provided in the query.

While this kind of sentiment-based labeling is not the only way to label test cases, it's frequently used despite its imperfections, such as being challenging to reproduce. Despite being an industry staple, the approach of assessing every evaluation case by hand is unrealistic at scale. What we need is a rigorous approach that can modulate the difficulty of evaluation cases.

We're iterating on a meta-benchmark we call Discovery Bench: a framework that modulates an evaluation case by generating "easy" and "hard" variations of every case. This allows us to audit how close or how far an agent is from succeeding in those cases. The lever for modulating the difficulty of an input query comes via a tried-and-trusted concept that's present across information theory and machine learning: surprisal, or the likelihood of an output given a set of inputs.