@sydneyrunkle: let's assume agent = model + harness unfortunately, good models are getting really expensive! so you need a great harne…
Summary
A guide on optimizing AI agent performance by improving the harness component to compensate for expensive model costs, focusing on hill climbing techniques.
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Observation: the best agent harness for each model will be from the model developer themselves
A discussion on how AI models perform best with harnesses developed by their own creators, as third-party harnesses may cause underperformance despite strong benchmarks, citing examples like Claude Code for Claude and Codex for GPT.
@omarsar0: // Adapt the Interface, Not the Model // I am fascinated by the results across my cheap-model-plus-good-harness builds.…
Proposes Life-Harness, a method that improves frozen LLM agents by adapting the runtime interface instead of model weights, achieving an average 88.5% relative improvement across 126 settings and 18 backbones.
@SergioPaniego: frontier agents are this good partly because the model was trained inside the very harness it ships with great to see t…
Sergio Paniego highlights that frontier agents' performance is due to models being trained inside their deployment harness. The new work 'Polar: Agentic RL on Any Harness at Scale' by NVIDIA AI enables turning harnesses like Codex, Claude Code, Qwen Code, or Pi into RL training environments without modifying their internals.
The agent bug I thought was the model turned out to be the harness
The author shares a debugging experience where an agent loop was caused by a harness truncating tool outputs rather than model failure, highlighting the reliability gap in agent infrastructure compared to 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.