VeRO: An Evaluation Harness for Agents to Optimize Agents
arXiv:2602.22480v1 Announce Type: new 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 analy
arXiv:2602.22480v1 Announce Type: new 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.
Executive Summary
This article introduces VeRO (Versioning, Rewards, and Observations), a systematic evaluation harness for agents to optimize agents. VeRO addresses the challenges of agent optimization by providing a reproducible evaluation framework that captures both intermediate reasoning and downstream execution outcomes. The authors conduct an empirical study comparing optimizer configurations across tasks, demonstrating the effectiveness of VeRO in improving target agent performance. VeRO is released as a benchmark suite to support research on agent optimization. The study contributes to the development of coding agents by providing a structured evaluation framework and benchmark suite, enhancing our understanding of agent optimization and its applications.
Key Points
- ▸ VeRO provides a reproducible evaluation harness for agents to optimize agents.
- ▸ VeRO captures both intermediate reasoning and downstream execution outcomes.
- ▸ The authors conduct an empirical study comparing optimizer configurations across tasks.
Merits
Structured Evaluation Framework
VeRO provides a structured evaluation framework that captures the complex interactions between deterministic code and stochastic LLM completions, enabling a systematic understanding of agent optimization.
Benchmark Suite
VeRO releases a benchmark suite of target agents and tasks with reference evaluation procedures, facilitating research on agent optimization and its applications.
Demerits
Limited Domain Application
VeRO is specifically designed for agent optimization and may not be directly applicable to other domains or tasks.
Complexity of LLM Completions
The stochastic nature of LLM completions may introduce complexity and variability in the evaluation process, requiring additional considerations and adaptations in the VeRO framework.
Expert Commentary
VeRO's introduction of a systematic evaluation harness for agents to optimize agents marks a significant contribution to the development of coding agents. The structured evaluation framework and benchmark suite provided by VeRO will facilitate research on agent optimization and its applications, enabling a more comprehensive understanding of agent performance and its implications. However, VeRO's limitations, such as its domain-specific application and the complexity of LLM completions, should be carefully considered and addressed in future research. Overall, VeRO's potential to enhance our understanding of agent optimization and its applications makes it a valuable tool for researchers and practitioners in the field.
Recommendations
- ✓ Future research should focus on adapting VeRO's evaluation framework and benchmark suite to other domains and tasks.
- ✓ Additional studies should investigate the complexity of LLM completions and its implications for the VeRO framework.
