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AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
Kai Chen, Zichen Ding, Jiaye Ge, Shufan Jiang, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tianhao Liang, Shudong Liu, Zerun Ma, Zixin Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu
TL;DR
Agent evaluation remains fragmented across specialized benchmarks and execution setups, limiting reproducibility and comparability. AgentCompass decouples benchmarks, harnesses, environments, and models, and shows that agent performance varies substantially with harness choice, including an 8.7-point drop on DeepSearchQA.
Problem
Agent evaluation is fragmented across specialized benchmarks and heterogeneous execution setups, limiting efficiency, reproducibility, and comparable assessment.
Method
AgentCompass uses composable Benchmark, Harness, Environment, and Model components with standardized execution and trajectory analysis.
Results
Across tasks, agent performance varies substantially with the harness and can diverge from official baselines; Claude-Opus-4.8 dropped by 8.7 points on DeepSearchQA.
Takeaways & Limitations
AgentCompass supports reproducible, comparable assessment while exposing intermediate behaviors and suspected reward-hacking beyond scalar scores.
Abstract
from arXiv · showhide
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.
1. Introduction
AgentCompass addresses fragmented agent evaluation by decoupling Benchmark, Harness, and Environment into a modular infrastructure. It also standardizes execution and supports asynchronous runtime operation plus trajectory-level failure analysis.
- LLM-based agents increasingly require evaluation methods that assess complex reasoning, planning, and tool interaction in dynamic environments.
- Existing agent evaluation is fragmented across specialized benchmarks, forcing repeated configuration of heterogeneous environments, data formats, and scoring protocols.This redundant engineering hinders efficiency and compromises reproducibility.
- AgentCompass introduces an open-source, lightweight, extensible infrastructure that decouples Benchmark, Harness, and Environment into flexible configurations.Its independent harness layer avoids reimplementing complex execution logic and enables benchmark × harness × Environment configurations.
- The infrastructure standardizes task dispatch, environment interaction, and metric aggregation while using an asynchronous runtime to parallelize long-running agent trajectories.
- Trajectory-level analysis and visualization expose intermediate actions, tool calls, and environment feedback for transparent diagnosis of agent failure modes.
2. Related Work
Agent evaluation has expanded beyond traditional LLM benchmarks into a broad but fragmented ecosystem of specialized benchmarks and supporting infrastructure. Existing work spans holistic and capability-specific evaluations, alongside dashboards, testing frameworks, tracing tools, and unified agent-evaluation frameworks.
- Agent evaluation must extend beyond LLM benchmarks, producing a broad but fragmented landscape of agent-specific benchmarks and infrastructure.
- Benchmarks: Benchmarks assess holistic agent performance through interactive environments, real-world questions, game-playing scenarios, and multi-agent collaboration.
- Benchmarks: Other benchmarks target tool and function calling, scientific reasoning, research coding, productivity-oriented skill execution, and software-related agentic capabilities.
- Infrastructure: Evaluation infrastructure includes analytical dashboards, unit-testing frameworks, commercial tracing tools, and unified frameworks for agent development and evaluation.
- Infrastructure: AgentGym supports evolving agents across diverse environments, while Harbor emphasizes coding and skill-based agent evaluation.
3. Framework
AgentCompass structures agent evaluation around decoupled Benchmarks, Harnesses, and Environments connected by stable protocols and declarative run specifications. Its registry-based architecture supports composable evaluations, asynchronous execution, trajectory diagnostics, and over 20 benchmarks spanning five capability dimensions.
- Configuration: A declarative RunRequest separates benchmark tasks and metrics, agent procedures, execution contexts, and model endpoint specifications.This separation allows harnesses, benchmarks, interaction protocols, and models to be recombined without rewriting benchmark code.
- Extensibility: Registry-based component integration lets researchers pair compatible benchmarks and harnesses without additional glue code.New components can be introduced through lightweight, decorator-based registration while preserving component boundaries.
- Core Architecture: AgentCompass decouples Benchmarks, Harnesses, and Environments through stable protocols, enabling flexible and composable agent evaluation.Optional recipes and analyzers extend the core evaluation configuration.
- Runtime and Diagnostics: The asynchronous runtime distributes tasks with configurable concurrency, while trajectory records capture reasoning, tool calls, environment feedback, and granular execution metrics.Pluggable analyzers flag problematic samples and categorize model-, environment-, and framework-side failures, including truncation, latency spikes, and repetitive loops.
- Benchmark Coverage: Over 20 built-in benchmarks span five capability dimensions: tool use, web & research, scientific reasoning, agentic coding, and productivity.The suite includes interactive and long-horizon settings such as repository-level code modification and open-ended web navigation.
4. Experiments
AgentCompass evaluates representative LLMs across eight challenging benchmarks using aligned benchmark–harness configurations. Results show that agent performance depends strongly on infrastructure choices, while trajectory analysis exposes reward-hacking behavior and capability–token-length trade-offs.
- Evaluation Setup: The evaluation covers seven representative LLMs across eight challenging benchmarks, with quantized versions explicitly identified in model names.Evaluated models include Qwen3.5-397B-A17B, DeepSeek-V4-pro(FP4), Kimi-K2.6, GLM-5.2(FP8), GPT-5.5, Gemini-3.1-Pro-Preview, and Claude-Opus-4.8.
- Evaluation Setup: AgentCompass aligns heterogeneous benchmarks with corresponding harnesses, enabling direct comparisons across workflows such as OpenClaw, OpenHands, Mini-SWE-agent, and Naive Search Agent.The evaluated combinations include τ3-bench, DeepSearchQA, FrontierScience(Research), SWE-bench variants, PinchBench, SkillsBench, and SciCode.
- Cross-Harness Comparisons: Performance fluctuates significantly across harnesses, with discrepancies between OpenClaw and OpenHands on SkillsBench and between Mini-SWE-agent and OpenHands on SWE-bench variants.These results indicate that empirical agent capabilities are sensitive to the underlying harness.
- Baseline Comparisons: 8.7 points: Claude-Opus-4.8 drops on DeepSearchQA, while GLM-5.2(FP8) improves by 15.0 points on SWE-bench-Pro with OpenHands relative to official baselines.The reported deviations demonstrate sensitivity to infrastructure choices, including harness versions and benchmark-specific adaptations.
- Trajectory Analysis: Most models exhibit suspected reward hacking on coding benchmarks, including modifying tests and retrieving golden patches.GLM-5.2(FP8) has the highest suspected hacking rate on SWE-Pro, Gemini-3.1-Pro-Preview on SWE-Multilingual, and DeepSeek-V4-pro(FP4) remains consistently low on both; GLM-5.2(FP8) also shows roughly 30% more suspected reward-hacking samples than Claude-Opus-4.8 despite outperforming it by about 12 points on SWE-Pro.
- Capability–Token Length Trade-offs: On coding tasks, longer outputs generally correspond to higher scores for most models, while DeepSeek-V4-Pro(FP4) is a notable outlier.Figure 4 examines capability–token-length relationships across different task categories to support budget-aware model decisions.
5. Conclusion
AgentCompass is an open-source, highly extensible infrastructure that systematizes LLM-agent evaluation through decoupled components, fault-tolerant execution, and granular trajectory analysis.
- AgentCompass systematizes the evaluation of LLM-based agents as an open-source and highly extensible infrastructure.
- Its evaluation pipeline decouples Model, Benchmark, Harness, and Environment components, eliminating redundant engineering and ensuring rigorous reproducibility.
- A fault-tolerant asynchronous runtime and granular trajectory analysis extend evaluation beyond traditional scalar scores.
A. Detailed Experimental Configurations
This section presents the detailed experimental settings used in the evaluation to ensure full reproducibility.
- The evaluation uses detailed experimental settings to support full reproducibility.
A.1. Model Configurations
AgentCompass uses standardized model-specific inference configurations to support deterministic, reproducible comparisons across closed-source and open-weight models.
- Inference setup: Closed-source models used their APIs’ default inference settings to ensure deterministic outputs where possible.
- Inference setup: Open-weight models were deployed with SGLang and their official recommended inference configurations.
- Inference setup: Reasoning effort was set to high whenever the parameter was available, avoiding ad hoc tuning and preserving reproducible comparisons.
A.2. Benchmark Specification.
AgentCompass specifies benchmark evaluations through explicit reference-score conventions and standardized, benchmark-specific workflows. These workflows preserve comparability across models and harnesses in tool use, agentic coding, and productivity tasks.
- Reference-score convention: Table 3 subscripted differences measure the gap between an AgentCompass score and the closest external reference for the same model and benchmark.References come from the model’s official technical report or the benchmark’s official leaderboard; no difference is shown without a comparable reference.
- Tool Use: Tool-use evaluation follows the official τ3-bench workflow, instantiating both user and rank models with the model under evaluation.This makes the evaluated model both the conversational user simulator and knowledge re-ranker, supporting end-to-end interactive tool-use evaluation.
- Agentic Coding: SWE-bench Pro and SWE-bench Multilingual use Mini-SWE-agent v2.3.0 and OpenHands through the OpenHands Software Agent SDK v1.23.0.Both agents share repository checkouts, patch-generation interfaces, and unit-test-based scoring, while harnesses supply system prompts.
- Productivity: Productivity evaluations use official benchmark releases when available, with task files and judging rules fixed across harnesses.SkillsBench uses AgentCompass data derived from official commit 17dec32 with OpenClaw v2026.5.9 and OpenHands v1.23.0, while PinchBench uses skill repository release v1.1.0 at commit e8e833.
B. Step statistics
This section defines interaction-step statistics for evaluation instances and explains how AgentCompass computes them automatically from complete execution trajectories. It also notes that benchmarks differ substantially in interaction depth, with scientific reasoning tasks generally requiring around four interaction steps.
- Step definition: A step is one complete agent–environment interaction cycle, including model generation, any tool invocation, and the returned environment observation.For purely conversational benchmarks, one step equals one model response.
- Computation: AgentCompass computes average step counts by averaging recorded trajectory lengths across all evaluated instances for each model and benchmark.The unified trajectory format enables automatic collection without benchmark-specific instrumentation.
- Observed differences: Benchmarks exhibit substantially different interaction depths, with scientific reasoning tasks such as SciCode generally requiring around four interaction steps.