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.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building stateful Directed Acyclic Graph (DAG) agent workflows, including persistence and conditional routing.
Implement the A2A Protocol for robust, secure agent-to-agent communication, including message serialization and authentication patterns.
Design and deploy MCP-enabled tool integration modules using Claude Opus 4.1 for interfacing with simulated enterprise APIs (e.g., HR, ERP, IT service management).
Build extended thinking pipelines with GPT-5 Pro, incorporating adaptive reasoning budgets for resource-efficient problem-solving in complex scenarios.
Orchestrate a multi-LLM agent system, assigning specific roles to GPT-5 for strategic planning and Claude Opus 4.1 for detailed analysis and verification.
Develop robust error handling and auditing mechanisms for agent interactions and external tool calls, crucial for enterprise applications.
Integrate a vector database for RAG, enabling agents to retrieve context-specific enterprise policies, documentation, and historical data.
How this agent runs
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
The configured agent processes the input under the challenge policy.
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
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.
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