arXiv:2602.22480v4 Announce Type: replace-cross Abstract: An important emerging application of coding agents is agent harness optimization: the iterative improvement of a target agent by editing and evaluating its code. Despite its relevance, the community lacks a systematic understanding of coding agent performance on this task. Harness optimization differs from conventional software engineering: agent harnesses interleave deterministic code with stochastic LLM completions, requiring structured capture of both intermediate execution traces and downstream outcomes. To address these challenges,
The proliferation of coding agents necessitates advanced methods for their optimization, moving beyond conventional software engineering paradigms to address unique challenges like stochastic LLM completions and execution traces.
This development is crucial for advancing autonomous AI systems, enabling them to self-improve and adapt, which will accelerate the automation of complex white-collar tasks.
The focus shifts from manual software maintenance to agent-driven optimization of other agents, representing a new frontier in AI development and deployment.
- · AI software developers
- · Companies adopting agentic workflows
- · AI research institutions
- · Cloud infrastructure providers
- · Traditional software engineering roles
- · Manual code optimization experts
Coding agents will become more efficient and capable of self-improvement.
This leads to a rapid acceleration in the development and deployment of increasingly sophisticated AI agents across various sectors.
The enhanced autonomy could lead to AI systems that rapidly optimize themselves without significant human intervention, altering the pace and nature of technological progress.
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Read at arXiv cs.CL