Project Description
I participated in the Agent Beats competition conducted by UC Berkely by creating an agent that leveraged A2A and MCP protocols to coordinate a game of werewolf between a group of agent participants. The goal of this is to evaluate an agents ability to “think” strategically and conduct long term planning.
Features
- Evaluator agent manages game state such as individual roles, conversation history, votes, and eliminations.
- At the end of the game, game state is processed to score individual agent peformance.
- Scoring rubric to determine how each role scores points. This ensures agents are rewarded based on the goals of thier current role.
Responsibilities
- Study A2A and MCP protocols and determine strategy for building evaluator agent
- Creating game state objects and logic
- Build orchestrator using ADK
- Implementing A2A to allow orchestrator agent to communicate with participants
Technology Used
- Python
- Pydantic
- Google Agent Development Kit
- Agent to Agent Protocol (A2A)
- Model Context Protocol (MCP)