arXiv:2605.27922v1 Announce Type: new Abstract: LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the base model, but also on the harness: the system layer that manages context, tools, state, constraints, permissions, tracing, and recovery. However, existing benchmarks typically abstract away execution, compare complete agent systems, or hold the harness fixed, making execution-layer variation difficult to study. We introduce Harness-Bench, a diagnostic benchmark for e
The rapid advancement and deployment of LLM agents in real-world applications necessitate more robust and comprehensive evaluation methods that account for their operational environment.
Harness-Bench addresses a critical gap in AI agent evaluation by focusing on the 'harness' layer, which is crucial for reliable and scalable agentic systems, moving beyond pure model performance.
This benchmark introduces a standardized way to measure the impact of execution environments on agent performance, allowing for better optimization and understanding of practical AI systems.
- · AI agent developers
- · Enterprises deploying AI agents
- · AI infrastructure providers
- · Companies relying on incomplete AI agent evaluations
- · Benchmarking methods ignoring execution layers
Improved performance and reliability of AI agent deployments in complex workflows.
Accelerated development of more robust AI agent frameworks and tooling that explicitly address harness effects.
Increased adoption of autonomous AI agents across industries due to enhanced trustworthiness and predictable performance.
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Read at arXiv cs.AI