# Agentic Persona Generation with Critique-Refinement: An Industrial Evaluation

arXiv: https://arxiv.org/abs/2606.09637 · HTML: https://mlsanity.com/paper/2606.09637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 10 of 10)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [Dated Data: Tracing Knowledge Cutoffs in Large Language Models](https://mlsanity.com/paper/2403.12958/) (2024)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [PersonaGen: A Tool for Generating Personas from User Feedback](https://mlsanity.com/paper/2307.00390/) (2023)
- [Who Uses Personas in Requirements Engineering: The Practitioners' Perspective](https://mlsanity.com/paper/2403.15917/) (2024)
- [The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams](https://mlsanity.com/paper/2509.03463/) (2025)
- [DSL or Code? Evaluating the Quality of LLM-Generated Algebraic Specifications: A Case Study in Optimization at Kinaxis](https://mlsanity.com/paper/2601.00469/) (2026)

## Abstract (arXiv)

Personas are widely used in software engineering to support requirements elicitation, design, and validation, but their manual creation is costly, time-consuming, and hard to scale. Recent LLM-based approaches automate persona generation from textual data; however, they typically rely on single-shot generation and subjective evaluations, limiting practical reliability. We present PerGent, an industry-grade method for persona generation built around an iterative critique-refinement loop. Specifically, PerGent uses a generator and a critic LLM agent, coordinated by an orchestrator, to iteratively refine personas using external resources such as interviews, surveys, and job postings through a critique-refinement loop with a user-defined maximum number of rounds. We deploy and evaluate PerGent in an industrial setting at Kinaxis, comparing it with three baselines, including one-shot methods. In an expert in-situ evaluation, PerGent achieved the highest expert approval rate (96.9%), exceeding all baselines. We further compare PerGent-generated personas with best-practice personas manually created by domain experts prior to the adoption of LLMs. Compared to baselines, PerGent reproduces a larger proportion of expert content while also contributing substantial new content beyond the pre-LLM personas. We conclude with lessons learned from deploying and evaluating PerGent at Kinaxis.