VeRO: An Evaluation Harness for Agents to Optimize Agents
Researchers have introduced VeRO (Versioning, Rewards, and Observations), a new evaluation harness designed to address the lack of systematic understanding regarding coding agent performance in agent optimization tasks. Agent optimization involves the iterative improvement of a target agent through edit-execute-evaluate cycles, a process that fundamentally differs from conventional software engineering due to the interleaving of deterministic code with stochastic Large Language Model (LLM) completions. VeRO provides a reproducible evaluation framework featuring versioned agent snapshots, budget-controlled evaluations, and structured execution traces. Additionally, it offers a benchmark suite of target agents and tasks with reference evaluation procedures. The authors conducted an empirical study using VeRO to compare optimizer configurations across various tasks, analyzing which modifications reliably enhance target agent performance. This tool is released to support further research into agent optimization as a core capability for coding agents, aiming to standardize how these complex, hybrid systems are evaluated and improved within the artificial intelligence community.
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VeRO: An Evaluation Harness for Agents to Optimize Agents
Researchers have introduced VeRO (Versioning, Rewards, and Observations), a new evaluation harness designed to address the lack of systematic understanding regarding coding agent performance in agent optimization tasks. Agent optimization involves the iterative improvement of a target agent through edit-execute-evaluate cycles, a process that fundamentally differs from conventional software engineering due to the interleaving of deterministic code with stochastic Large Language Model (LLM) completions. VeRO provides a reproducible evaluation framework featuring versioned agent snapshots, budget-controlled evaluations, and structured execution traces. Additionally, it offers a benchmark suite of target agents and tasks with reference evaluation procedures. The authors conducted an empirical study using VeRO to compare optimizer configurations across various tasks, analyzing which modifications reliably enhance target agent performance. This tool is released to support further research into agent optimization as a core capability for coding agents, aiming to standardize how these complex, hybrid systems are evaluated and improved within the artificial intelligence community.
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