[All work](https://boiko.ai/work/)

Agent evaluation · Research engineering · Research by [Andrii Boiko](https://boiko.ai/about/)

# AI Agent Evaluation Research with HUD SDK

Andrii Boiko’s research with HUD SDK: AI agent evaluation, grader validation, reward integrity, trajectory analysis, and reproducible experiments.

---

## [Research focus.](https://boiko.ai/work/hud-sdk-agent-evaluation/#research-focus)

I used the HUD SDK to investigate how reliably an evaluation measures an agent’s ability to complete a task. My work connected evaluation environments, grader validation, and trajectory analysis: checking that task instructions, available tools, and scoring expressed the same objective.

The central research problem was measurement validity. A score becomes useful when the evidence explains what produced it, which alternative explanations have been tested, and how far the conclusion can reasonably extend.

## [Evaluation methodology.](https://boiko.ai/work/hud-sdk-agent-evaluation/#evaluation-methodology)

1. ### Validate the experimental setup

   Inspect environment behavior and tool interfaces, check consistency between task definitions and execution, and verify that the recorded evidence corresponds to the setup being evaluated.

2. ### Interrogate the reward signal

   Reproduce scoring calculations independently, examine how the grader represents the task objective, and use controls to test for misleading signals and shortcuts.

3. ### Explain agent behavior

   Analyze trajectories and tool failures, compare plausible explanations for success and failure, and distinguish agent limitations from problems in the evaluation itself.

## [Research contribution.](https://boiko.ai/work/hud-sdk-agent-evaluation/#research-contribution)

My contribution was connecting implementation-level debugging with experimental judgment. I investigated failure modes, checked reproducibility, and challenged whether a numerically correct result supported the behavioral claim attached to it.

This work brings together the parts of agent research I care about most: precise questions, inspectable experiments, independent checks, and conclusions calibrated to the evidence. Validating a score, validating the behavior it represents, and establishing generalization each require their own evidence.

## [Tooling.](https://boiko.ai/work/hud-sdk-agent-evaluation/#tooling)

HUD provides an SDK for defining agent environments and running evaluations. Its public repository documents the tooling used in this work.

[HUD SDK · public repository](https://github.com/hud-evals/hud-python)

## [Related work](https://boiko.ai/work/hud-sdk-agent-evaluation/#related-work-heading)

- [agent-claimcheck: comparing claims with evidence](https://boiko.ai/work/agent-claimcheck/)

- [Evaluation Inside the Agent Loop](https://boiko.ai/writing/evaluation-inside-the-agent-loop/)

Author: [Andrii Boiko](https://boiko.ai/about/)

## Contact and profiles

- [Andrii Boiko on GitHub (B0yko)](https://github.com/B0yko)
- [Andrii Boiko on LinkedIn](https://www.linkedin.com/in/andriyboyko)
- [Andrii Boiko on Hugging Face](https://huggingface.co/aboiko)
- [Email](mailto:andrewvboyko@gmail.com)

Source: [https://boiko.ai/work/hud-sdk-agent-evaluation/](https://boiko.ai/work/hud-sdk-agent-evaluation/)