best of the best agentic harnesses do this…
Summary
The author shares insights on building effective agent harnesses: the best ones minimize LLM reliance for trivial tasks and reserve LLMs for complex reasoning, distinguishing genuine harnesses from simple wrappers.
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Stop Comparing LLM Agents Without Disclosing the Harness
This position paper argues that in long-horizon LLM agent tasks, the execution harness often determines performance more than the model itself, and current benchmarks misattribute harness-level gains to model improvements. It proposes a harness-aware evaluation framework with disclosure standards and variance decomposition protocols.
@omarsar0: // Self-Harness: Harnesses That Improve Themselves // (bookmark this one) Most of the agent scaffolds we rely on today …
This paper introduces Self-Harness, a new paradigm where LLM-based agents iteratively improve their own operating harness—prompts, tools, and control flow—without human engineers or stronger external agents, achieving significant performance gains across multiple models.
Harnesses for Inference-Time Alignment over Execution Trajectories
This paper studies harness design for LLM agents, separating it into task decomposition and guided execution, and shows that more elaborate harnesses are not uniformly better; it reveals failure modes and proposes partial harnesses as effective.
It's Not the Capability: Harness Sensitivity Is Non-Monotone Across LLM Agent Tiers
This paper empirically tests the common assumption that more structured harnesses universally improve LLM agent reliability, finding a non-monotone relationship across model tiers. It introduces the HEAT-24 benchmark and reveals that strict harnesses can harm frontier chat models while benefiting reasoning models.
@dair_ai: // State-Externalizing Harnesses // A new paradigm is emerging on how to effectively build agents and harnesses. If the…
Harness-1 introduces a state-externalizing harness that separates routine bookkeeping from policy decisions in search agents, enabling a 20B model to outperform larger frontier searchers across multiple benchmarks.