Researchers propose method to safely compress AI agent prompts in production
A team of researchers has published a paper on arXiv outlining a new approach to evaluating and compressing prompts for AI agents in real-world production environments. The method addresses the challenge of maintaining reproducibility in agents that interact with live, mutable enterprise data—where benchmarks can quickly become outdated as data changes. Their solution involves an offline…
Key points
- New offline evaluation method keeps AI agent benchmarks reproducible despite live data changes
- GEPA-based prompt compression reduces agent prompt size by **3.5% to 7.6%** without task regressions
- Correctness gate prevents reward hacking, and in one case, improves performance measurably
The researchers apply this framework to optimize prompts for a merchant-facing AI agent using GEPA-based techniques, with a correctness gate to prevent reward hacking. Their results show that this approach can reduce the agent’s full prompt surface by 3.5% to 7.6% without degrading task performance. In one case, correctness even improved measurably. The work targets agents across three independent subject-model families, though no specific company or product is named.
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