# AgentHabit: Characterizing Distinct Behaviors of Agents on Everyday Tasks

arXiv: https://arxiv.org/abs/2609.32795 · HTML: https://mlsanity.com/paper/2609.32795/

## 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 8 of its 37 references that are in the map.
- First posted on arXiv 26 Sep 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, 8 of 8)

- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Naturally Occurring Feedback is Common, Extractable and Useful](https://mlsanity.com/paper/2407.10944/) (2024)
- [How to Interpret Agent Behavior](https://mlsanity.com/paper/2605.13625/) (2026)
- [AgentAtlas: Beyond Outcome Leaderboards for LLM Agents](https://mlsanity.com/paper/2605.20530/) (2026)
- [Human Psychometric Questionnaires Mischaracterize LLM Behavior](https://mlsanity.com/paper/2509.10078/) (2025)
- [Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values](https://mlsanity.com/paper/2605.10365/) (2026)
- [Mind the Gap: Linguistic Divergence and Adaptation Strategies in Human-LLM Assistant vs. Human-Human Interactions](https://mlsanity.com/paper/2510.02645/) (2025)
- [Measuring Cross-Task Behavioral Consistency in Language Model Agents](https://mlsanity.com/paper/2608.13598/) (2026)

## Abstract (arXiv)

Large language model (LLM) agents assist users with everyday tasks that can be completed in many reasonable ways. Even when their answers are useful, how agents carry out these tasks may not match users' preferences and needs. For example, agents differ in whether they ask clarifying questions or search the web. We introduce HABIT, a taxonomy of 23 behavioral axes in five categories, which three authors and three LLMs derive bottom-up from 408 agent trajectories across 17 domains. On held-out tasks, HABIT distinguishes models more clearly than existing taxonomies of human values and agent actions while supporting comparably consistent annotation. Building on HABIT, we construct AgentHABIT, a benchmark that profiles each agent's behavioral tendencies from its trajectories on 86 everyday tasks. Profiling 18 models with AgentHABIT reveals a range of distinctive tendencies. For example, most GPT and Claude models state their assumptions and offer alternatives when requirements conflict, whereas Qwen and Google's models more often leave assumptions or changes to requirements unstated. These profiles remain recognizable even when built from entirely different sets of tasks, indicating that they reflect general tendencies rather than task-specific behavior. Prompting agents to adopt specific behaviors shifts some axes readily but barely changes others, while fine-tuning on another model's trajectories changes only part of a model's profile and leaves much of it intact. Overall, HABIT and AgentHABIT provide a systematic framework for characterizing how agents carry out everyday tasks beyond task success, offering insights to guide the development of agents whose behavior better fits users' needs.