arXiv:2605.29682v2 Announce Type: replace Abstract: Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wall time, or cost, cannot distinguish useful feedback from redundant or unstable interaction. We introduce \emph{Effective Feedback Compute} (EFC), a trace-level scaling coordinate for informative, valid, non-redundant, and retained feedback. We further define Estimated-EFC, NRS-EFC, harness efficiency $\eta$, and task-demand normalization for realistic traces and het
The proliferation of AI agents highlights the immediate need for more precise and efficient methods to evaluate and optimize their performance, addressing limitations of current expenditure metrics.
This development introduces a novel metric, Effective Feedback Compute (EFC), which promises to revolutionize how AI agent performance and efficiency are measured, moving beyond raw resource consumption.
The adoption of EFC will shift the focus in AI agent development from simple token or compute expenditure to the quality, relevance, and impact of feedback loops, enabling more effective and targeted improvements.
- · AI Agent Developers
- · AI Infrastructure Providers
- · Enterprises deploying AI agents
- · Inefficient AI Agent Architectures
- · Providers of redundant AI feedback mechanisms
AI agent research and development will become more efficient, focusing on high-quality interactions rather than sheer volume.
Improved AI agent performance will accelerate their deployment across various industries, leading to increased automation and productivity gains.
The enhanced efficiency in agent development could lower the barrier to entry for more complex AI applications, fostering innovation in areas currently constrained by computational costs.
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