AgentBoard

Overview

AgentBoard is an analytical evaluation board for multi-turn LLM agents that emphasizes progress metrics and cross-environment analysis rather than a single success-rate number. It packages many agent scenarios into one comparative evaluation surface.

Why it matters

It matters because not every harness substrate should be a training gym. Sometimes the right object is a board that helps you understand where the agent is failing before you decide what world to train in.

Distinctive trait

Its distinctive move is analytical decomposition: it tries to make agent behavior interpretable across tasks instead of treating evaluation as one giant pass/fail oracle.

Relationships

Read AgentBoard with rl-gyms-and-executable-environments-for-ai-harnesses, evaluation-and-review-loops, harness-engineering, and compare it with agentgym plus gaia.