Agent evals: how to tell whether an agent succeeded / Research Insights Made Simple #42 (#AI4SDLC)

The agent wrote “done”. But was the right file changed, was the booking added to the database, and were the task's conditions met? A polished answer does not yet mean the work is complete. And a low score sometimes points to a broken check rather than a weak agent.

Tomorrow at 10 a.m. Moscow time, we will discuss Anthropic's guide “Demystifying evals for AI agents” and continue our series on agent harnesses. After discussing their structure, we turn to the question: how can we demonstrate that a system works and that the latest change has actually improved it?

We will discuss: — How an outcome differs from a trajectory: what the agent left in the environment and how it got there. — Why we evaluate the model together with the harness, tools, and environment settings. — How a task suite, an individual task, and repeated attempts are structured — and why success percentages can have different denominators. — When programmatic checks, model judges, and human assessment are appropriate; how to check the graders themselves. — How pass@k differs from pass^k: at least one success and consistent success across all attempts answer different questions. — Why we should isolate runs, read trajectories, and turn real failures into test tasks.

We will also discuss trust in evals: how to distinguish a regression from random variation, spot incorrect criteria, and avoid mistaking a higher score for a better product. In the epilogue, we will turn to later Anthropic materials: Petri v2, measuring agent autonomy, and the analysis of the April regressions.

This is a practical guide based on the experience of Anthropic's team and customers. We will separate the authors' recommendations, the results of individual studies, and our own conclusions for developing our own agents.

The stream is for developers, technical leaders, and anyone who wants to evaluate AI agents on real tasks. Bring examples: what counts as evidence of success in your system?

#AI4SDLC #Agents #Engineering #Architecture #Evals