# Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations

arXiv: https://arxiv.org/abs/2509.25492 · HTML: https://mlsanity.com/paper/2509.25492/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 44 of its 110 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 44)

- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts](https://mlsanity.com/paper/2202.01279/) (2022)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks](https://mlsanity.com/paper/2411.04468/) (2024)
- [A Roadmap to Pluralistic Alignment](https://mlsanity.com/paper/2402.05070/) (2024)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles](https://mlsanity.com/paper/2407.00870/) (2024)
- [Interactive Debugging and Steering of Multi-Agent AI Systems](https://mlsanity.com/paper/2503.02068/) (2025)
- [Proactive Conversational Agents with Inner Thoughts](https://mlsanity.com/paper/2501.00383/) (2024)
- [ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles](https://mlsanity.com/paper/2310.15428/) (2023)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use](https://mlsanity.com/paper/2409.08775/) (2024)
- [UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library](https://mlsanity.com/paper/2202.09895/) (2022)
- [Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia](https://mlsanity.com/paper/2402.14147/) (2024)
- [Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems](https://mlsanity.com/paper/2001.04879/) (2020)
- [CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming](https://mlsanity.com/paper/2310.09235/) (2023)
- [Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams](https://mlsanity.com/paper/2402.17721/) (2024)
- [PolicyKit: Building Governance in Online Communities](https://mlsanity.com/paper/2008.04236/) (2020)
- [Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach](https://mlsanity.com/paper/2501.17258/) (2025)
- [The Value of Disagreement in AI Design, Evaluation, and Alignment](https://mlsanity.com/paper/2505.07772/) (2025)
- [Crowdsourcing the Perception of Machine Teaching](https://mlsanity.com/paper/2002.01618/) (2020)
- [Wordflow: Social Prompt Engineering for Large Language Models](https://mlsanity.com/paper/2401.14447/) (2024)
- [PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation](https://mlsanity.com/paper/2410.16547/) (2024)
- [Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement](https://mlsanity.com/paper/2305.01615/) (2023)
- [Clarify: Improving Model Robustness With Natural Language Corrections](https://mlsanity.com/paper/2402.03715/) (2024)
- [PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation](https://mlsanity.com/paper/2409.15644/) (2024)
- [The Future of HCI-Policy Collaboration](https://mlsanity.com/paper/2409.19738/) (2024)

## Built on by (most cited first, 1 of 1)

- [Elsewise: Authoring Open-ended Interactive Narrative with Possibility Space Visualization](https://mlsanity.com/paper/2601.15295/) (2025)

## Abstract (arXiv)

AI agents, or bots, serve important roles in online communities. However, they are often designed by outsiders or a few tech-savvy members, leading to bots that may not align with the broader community's needs. How might communities collectively shape the behavior of community bots? We present Botender, a system that enables communities to collaboratively design LLM-powered bots without coding. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors tailored to community needs. Botender facilitates testing and iteration on bot behavior through case-based provocations: interaction scenarios generated to spark user reflection and discussion around desirable bot behavior. A validation study found these provocations more useful than standard test cases for revealing improvement opportunities and surfacing disagreements. During a five-day deployment across six Discord servers, Botender supported communities in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.