AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

2026-07-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

AgentCompass is an open-source, lightweight, and extensible infrastructure designed to unify the evaluation of Large Language Model (LLM)-based agents. It addresses fragmentation and reproducibility issues in current evaluation pipelines by decoupling the process into three independent components: Benchmark, Harness, and Environment. This architecture enables flexible $\text{benchmark}\times\text{harness}\times\text{environment}$ configurations. AgentCompass features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to diagnose nuanced failure modes like reward-hacking. As of July 2026, it natively supports over 20 benchmarks across five capability dimensions, including tool use, web & research, and agentic coding. Experiments with models like GPT-5.5 and Claude-Opus-4.8 demonstrate its ability to standardize settings and reveal performance sensitivities to infrastructure choices, with deviations up to 15.0 points from official baselines.

Key takeaway

For MLOps Engineers or AI Scientists evaluating LLM-based agents, AgentCompass offers a standardized framework to ensure reproducible and comparable assessments. You should consider adopting its decoupled architecture to streamline your evaluation pipelines, diagnose agent behaviors beyond simple scores, and accurately compare model performance across diverse benchmarks and harnesses. This approach helps identify true agent capabilities versus infrastructure-induced variations, optimizing resource allocation for agent development.

Key insights

Unified evaluation infrastructure for LLM agents improves reproducibility and diagnoses complex behaviors beyond scalar scores.

Principles

• Decouple evaluation into Benchmark, Harness, and Environment.
• Standardize interaction histories for transparent diagnostics.
• Agent performance is sensitive to the underlying harness choice.

Method

AgentCompass uses a declarative RunRequest to define BenchmarkSpec, HarnessSpec, EnvironmentSpec, and ModelSpec, executed by an asynchronous dispatcher with fault tolerance and incremental saving. Trajectory analysis is performed by pluggable analyzers.

In practice

• Evaluate different agents on a shared benchmark.
• Compare alternative interaction protocols.
• Integrate new datasets without rewriting execution logic.

Topics

• LLM Agents
• Agent Evaluation
• Evaluation Infrastructure
• AgentCompass
• Trajectory Analysis
• Reproducibility

Code references

• open-compass/AgentCompass

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

Related on AIssential

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.