Build A2A MCP Agents for Enterprise IT & Accounting Automation
Frontier model firms are embedding AI agents in companies like accounting and IT businesses, this challenge focuses on developing a sophisticated multi-agent system. You will design and implement a set of specialized agents that collaborate using the A2A Protocol to automate common enterprise IT and accounting workflows. The system must leverage MCP for secure, dynamic tool integration with simulated (or real, if accessible) enterprise systems, ensuring robust data handling and process orchestration. Emphasize fault tolerance, auditing capabilities, and adaptive decision-making within the agent swarm.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an A2A multi-agent system with LangGraph and MCP for automating IT and accounting processes using GPT-5 and Claude Opus 4.5.
How work is evaluated
The evaluation will assess the system's ability to accurately and efficiently automate IT and accounting tasks, adhering to communication protocols and demonstrating robust tool integration.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Integrate MCP-enabled tools for dynamic interaction with external IT and accounting systems.
Orchestrate a multi-LLM agent system for diverse task execution.
Develop agents capable of extended thinking and adaptive reasoning for problem-solving.
Design and implement A2A protocol for secure inter-agent communication.
Build graph-based agent workflows using LangGraph for complex enterprise processes.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the system's ability to accurately and efficiently automate IT and accounting tasks, adhering to communication protocols and demonstrating robust tool integration.
Challenge input
{ "ticket_id": "UUID", "issue_description": "String", "user_email": "String" }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ "ticket_id": "UUID", "resolution_plan": "String", "actions_taken": ["String"], "mcp_tool_calls": ["Object"], "a2a_interactions": ["Object"], "fin...
- Verify that agents communicate using the specified A2A message structure and demonstrate appropriate handoffs.
- Check for correct and relevant MCP tool calls with appropriate parameters.
- Ensure the agent system reaches a logical conclusion or proposed resolution for the given task.
- Accuracy Of Information Extraction target: 0.95
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Verify that agents communicate using the specified A2A message structure and demonstrate appropriate handoffs.
- Check for correct and relevant MCP tool calls with appropriate parameters.
- Ensure the agent system reaches a logical conclusion or proposed resolution for the given task.
Metrics
- Accuracy of Information Extraction · Target 0.95
- Efficiency of Agent Workflow · Target 3
Workflow tasks
- Task 1
Input: { "ticket_id": "UUID", "issue_description": "String", "user_email": "String" }
Output: { "ticket_id": "UUID", "resolution_plan": "String", "actions_taken": ["String"], "mcp_tool_calls": ["Object"], "a2a_interactions": ["Object"], "final_status": "String" }
Execution harness: Python
- Task 2
Input: { "invoice_data": "String (simulated invoice text)" }
Output: { "invoice_id": "UUID", "vendor": "String", "amount": "Float", "status": "String", "approval_recommendation": "String", "mcp_tool_calls": ["Object"] }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.