Research Insights #42 materials: agent evaluations—how to tell whether the agent succeeded (#AI4SDLC)

I have gathered the materials for solo Research Insights Made Simple #42 from October 21, 2026. The episode is based on “Anthropic Demystifying evals for AI agents”. The agent wrote “done,” but was the booking actually created, was the right file changed, and were the task’s requirements met?

In the episode I covered:

🔸 The outcome and the path to it We check the outcome in the environment and read the trajectory to understand the causes of success or failure. The model needs to be evaluated together with its harness, tools, and configuration.

🔸 Who checks the checker Code is useful for precise conditions, a model judge for open-ended responses, and a human for expert assessment and calibration. Each approach has limitations; a low score may reflect an error in the check itself.

🔸 The difference between pass@k and pass^k Finding at least one solution over several attempts and succeeding on every attempt are different product requirements. One good demonstration is not enough for reliable operation.

🔸 Where to start The guide suggests a small set of 20–50 real tasks: clear criteria, reference solutions, negative examples, and a clean environment. The previous run’s history should not reveal the answer to the next one.

🔸 What to do after release Regression checks are complemented by monitoring, experiments, and user feedback. No evaluation suite catches every problem; trajectories also help uncover defects in tasks and graders.

I recommend this to anyone who can already run an agent and now wants a well-founded basis for deciding whether to give it work.

Episode materials: 📌 Episode page with timestamps and episode slides 📖 Longread on the evolution of agent harnesses 🎬 Video: YouTube and VK Video 🎧 Audio: Podster and Apple Podcasts 📖 Text summary and my annotated PDF (where I recorded my thoughts in the margins while reading)

Have you ever seen a test turn out to be wrong while the agent’s solution was correct? Bring your examples.

#ResearchInsights #AI4SDLC #Agents #Evals #Engineering