Agentic Private Evolution: Differentially Private Synthesis of Agent Workspace Data

Evaluating enterprise agents requires realistic workspace data, but private email threads, conversations, and documents cannot be freely shared. Private Evolution (PE) offers differentially private (DP) synthesis without training the generator. Applying PE to workspace data, however, presents three challenges: independent generation lacks shared context whose rules vary across data types; semantic similarity does not ensure structural validity; and vote-based selection can lose coverage of requested sample types. To address these challenges, we propose Agentic Private Evolution (Agentic-PE), a framework for DP synthesis of agent environment data. Starting from a DP attribute table that specifies the samples to generate, Agentic-PE introduces three complementary modules: a constraint module provides shared participants and data-type-specific context rules; a validation agent checks and repairs candidates before private voting; and constrained selection retains one feasible candidate per table row to preserve coverage. The modules operate on DP outputs and synthetic samples, adding no privacy cost. On email at a matched privacy budget, Agentic-PE achieves 6.4 times Aug-PE’s semantic recall at lower Fréchet Inception Distance (FID) and raises structural validity from 62.8% to 93.1%. Its corpus improves downstream training over a DP fine-tuning baseline and yields positively correlated retrieval and reply-model rankings on real and synthetic benchmarks. The framework also improves recall and FID over matched-budget Aug-PE on chat, meetings, and internal documents.