AI

Reproducible Evaluation as the Substrate for Safe Prompt Compression in Production Agents

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 26/10/2026, 04:03:24 UTC.

Briefings

A new evaluation harness enables offline testing of live, mutable enterprise data for production conversational agents. This approach allows prompt compression to be evaluated against a correctness gate, ensuring no regression in task performance. Applied to a merchant-facing agent, the method successfully reduced the full prompt surface by 3.5-7.6% across three independent subject-model families while maintaining or improving held-out task correctness.

Key points

• An offline evaluation harness was developed to test conversational agents operating on live, mutable enterprise data, solving the problem of benchmark staleness.
• Prompt compression was evaluated using a correctness gate that guards against reward hacking, resulting in a 3.5-7.6% reduction in prompt surface without task regression.

Context

This research addresses the difficulty of evaluating conversational agents that operate on live, mutable enterprise data, where replayed sessions often drift as backend state changes. The study applies this framework to GEPA-based prompt optimization under a correctness gate, testing it on a production merchant-facing agent across three independent subject-model families.

Limitations

The study relies on a single preprint (arXiv:2610.31972v1) and does not include results from additional subject-model families or a full production agent deployment. Results are limited to the three independent subject-model families tested and do not account for long-term drift effects beyond the initial evaluation window.

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