Multi-Agent User Behavior Simulation
This challenge involves building a sophisticated multi-agent system to model and predict user interactions on a digital platform. Participants will use OpenAI o3 for advanced reasoning capabilities and AutoGen to orchestrate a team of autonomous agents that interact via an A2A protocol. Each agent will embody a distinct user persona, capable of extended thinking and decision-making within a simulated environment. The system will leverage MCP-enabled tools to interact with the simulated platform's API, generating realistic behavioral data for product testing and market analysis.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge involves building a sophisticated multi-agent system to model and predict user interactions on a digital platform. Participants will use OpenAI o3 for advanced reasoning capabilities and AutoGen to orchestrate a team of autonomous agents that interact via an A2A protocol. Each agent will embody a distinct user persona, capable of extended thinking and decision-making within a simulated environment. The system will leverage MCP-enabled tools to interact with the simulated platform's API, generating realistic behavioral data for product testing and market analysis.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master AutoGen for creating and managing a team of goal-driven, conversational agents with specific roles and objectives for user simulation
Implement A2A protocol for secure and efficient agent-to-agent communication, enabling agents to observe, react, and collaborate within a simulated environment
Design complex user personas and behavioral scripts, leveraging OpenAI o3's advanced reasoning to generate realistic and varied user actions, preferences, and decision paths
Develop MCP-enabled tool integration modules that allow AutoGen agents to interact with a simulated web application's API, performing actions like browsing, clicking, purchasing, or commenting
Build extended thinking pipelines where agents can deliberate, plan, and strategize their actions over multiple steps, simulating more complex user journeys and problem-solving scenarios
Deploy a robust logging and analytics system to capture and analyze the simulated user behavior, providing insights into user flows, pain points, and success metrics
How this agent runs
The evaluation will assess the realism and diversity of simulated user behaviors, the efficiency of A2A communication, and the agents' ability to interact accurately with the simulated platform via MCP-enabled tools....
Challenge input
{'personas': [{'name': 'string', 'goal': 'string', 'preferences': 'dict'}], 'simulated_env_api_schema': 'dict'}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'agent_logs': [{'agent_id': 'string', 'action': 'string', 'timestamp': 'string'}], 'completion_status': 'boolean', 'behavioral_diversity_score': '...
- All agents successfully complete their assigned tasks or the shared goal is achieved.
- Agents must successfully call the simulated environment's API tools without errors.
- BehavioralDiversityScore target: 0.8
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsFind another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.