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AgentCompass: A Unified Evaluation Infrastructure f... | AI Research

Key Takeaways

  • AgentCompass is an open-source infrastructure designed to solve the fragmentation and lack of reproducibility in how we evaluate AI agents.
  • As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.
  • However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering.
  • To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents.
  • Furthermore, it features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to transparently diagnose nuanced failure modes like reward-hacking.
Paper AbstractExpand

As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely Benchmark, Harness, and Environment, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic. Furthermore, it features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to transparently diagnose nuanced failure modes like reward-hacking. Natively supporting over 20 benchmarks across five capability dimensions, AgentCompass provides the community with a scalable and reproducible infrastructure for advancing agent research.

AgentCompass is an open-source infrastructure designed to solve the fragmentation and lack of reproducibility in how we evaluate AI agents. As Large Language Models (LLMs) evolve into autonomous agents capable of complex reasoning and tool use, existing evaluation methods have become disjointed, often requiring researchers to rebuild complex environments for every new test. AgentCompass provides a unified, modular system that standardizes how these agents are tested, allowing for consistent and scalable performance assessment across diverse tasks.

A Modular Design for Flexibility

The core innovation of AgentCompass is the decoupling of the evaluation process into three independent components: the Benchmark, the Harness, and the Environment. By separating these, the framework allows researchers to mix and match configurations—for example, testing different agent "harnesses" (the code that manages the agent's logic and tool use) against the same benchmark without needing to rewrite the underlying execution code. This modularity is managed through a registry-based system, making it easy to add new benchmarks or tools without modifying the central infrastructure.

Standardizing Execution and Analysis

Beyond just running tests, AgentCompass includes an asynchronous runtime that handles the high demands of long-running agent tasks, such as web navigation or multi-step coding projects. It also features a robust trajectory analysis tool. Instead of just looking at a final success or failure score, the framework records the entire interaction history—including reasoning steps, tool calls, and environment feedback. This allows developers to transparently diagnose specific failure modes, such as reward-hacking, latency spikes, or repetitive loops, providing a much clearer picture of why an agent performed the way it did.

Scalable and Reproducible Research

AgentCompass currently supports over 20 benchmarks across five key capability dimensions, including tool use, scientific reasoning, and agentic coding. By providing a standardized way to define models, tasks, and execution environments, the framework ensures that evaluations are reproducible. Experiments conducted using the platform demonstrate that agent performance is highly sensitive to the specific harness used, highlighting the importance of a unified infrastructure. By standardizing these protocols, AgentCompass helps the research community move toward more reliable and comparable benchmarks for autonomous AI systems.

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