VeRO: An Evaluation Harness for Agents to Optimize Agents
#VeRO #Agent Optimization #Coding Agents #AI Evaluation #Edit-Execute-Evaluate Cycles #Machine Learning #arXiv
📌 Key Takeaways
- Researchers introduced VeRO, an evaluation harness for optimizing AI coding agents
- Agent optimization differs fundamentally from conventional software engineering
- VeRO provides both evaluation tools and benchmark suite for target agents
- The team conducted empirical studies comparing optimizer configurations
- VeRO has been released to support further research in agent optimization
📖 Full Retelling
A team of researchers led by Varun Ursekar, including Apaar Shanker, Veronica Chatrath, Yuan Xue, and Sam Denton, introduced VeRO (Versioning, Rewards, and Observations) on February 25, 2026, as an evaluation harness designed to optimize AI coding agents through iterative improvement cycles, addressing the community's lack of systematic understanding of coding agent performance in this emerging field that fundamentally differs from conventional software engineering. The paper, published on arXiv under the category of Computer Science > Artificial Intelligence, presents VeRO as a comprehensive solution for the increasingly important application of agent optimization - the iterative improvement of target agents through edit-execute-evaluate cycles. The researchers highlight that agent optimization presents unique challenges compared to traditional software development, as target agents interleave deterministic code with stochastic language model completions, requiring structured capture of both intermediate reasoning and downstream execution outcomes. VeRO provides two main components: a reproducible evaluation harness featuring versioned agent snapshots, budget-controlled evaluation, and structured execution traces; and a benchmark suite of target agents and tasks with reference evaluation procedures. Using this framework, the team conducted an empirical study comparing optimizer configurations across various tasks and analyzing which modifications reliably improve target agent performance.
🏷️ Themes
Artificial Intelligence, Coding Agents, Evaluation Frameworks
📚 Related People & Topics
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Artificial intelligence
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Large language model
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22480 [Submitted on 25 Feb 2026] Title: VeRO: An Evaluation Harness for Agents to Optimize Agents Authors: Varun Ursekar , Apaar Shanker , Veronica Chatrath , Yuan Xue, Sam Denton View a PDF of the paper titled VeRO: An Evaluation Harness for Agents to Optimize Agents, by Varun Ursekar and 4 other authors View PDF HTML Abstract: An important emerging application of coding agents is agent optimization: the iterative improvement of a target agent through edit-execute-evaluate cycles. Despite its relevance, the community lacks a systematic understanding of coding agent performance on this task. Agent optimization differs fundamentally from conventional software engineering: the target agent interleaves deterministic code with stochastic LLM completions, requiring structured capture of both intermediate reasoning and downstream execution outcomes. To address these challenges, we introduce VERO (Versioning, Rewards, and Observations), which provides (1) a reproducible evaluation harness with versioned agent snapshots, budget-controlled evaluation, and structured execution traces, and (2) a benchmark suite of target agents and tasks with reference evaluation procedures. Using VERO, we conduct an empirical study comparing optimizer configurations across tasks and analyzing which modifications reliably improve target agent performance. We release VERO to support research on agent optimization as a core capability for coding agents. Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2602.22480 [cs.AI] (or arXiv:2602.22480v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22480 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Varun Ursekar [ view email ] [v1] Wed, 25 Feb 2026 23:40:22 UTC (3,752 KB) Full-text links: Access Paper: View a PDF of the paper titled VeRO: An Evaluation Harness for...
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