MCP-Enabled Proactive Incident Response
There is a need for proactive and automated incident response systems. This challenge involves building a sophisticated multi-agent system using LangGraph that leverages the MCP for seamless integration with enterprise security tools and data sources. The system will be capable of detecting anomalies, assessing potential threats, and orchestrating response actions in a preventative manner. Your agents, powered by Claude Opus 4.5 for advanced reasoning, will communicate using an A2A protocol, forming a dynamic graph-based workflow. The MCP will serve as the central nervous system, allowing agents to fetch security logs, access vulnerability databases, query user activity, and even initiate remediation scripts across diverse enterprise systems securely. This proactive system aims to identify and mitigate risks before they escalate into full-blown data breaches.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an MCP-enabled, graph-based multi-agent system using LangGraph and Claude Opus 4.5 for proactive security incident response with A2A communication.
How work is evaluated
The evaluation will simulate a security incident, assessing the multi-agent system's ability to detect, analyze, and propose/execute appropriate response actions. Key metrics include detection time, accuracy of analysis, and efficacy of remediation.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Create a proactive system capable of anomaly detection and automated risk mitigation
Implement MCP-enabled tool integration for accessing simulated enterprise security systems
Develop A2A protocol for secure and efficient agent-to-agent communication
Utilize Claude Opus 4.1 for advanced threat analysis and decision-making
Design a graph-based multi-agent workflow using LangGraph for incident response
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will simulate a security incident, assessing the multi-agent system's ability to detect, analyze, and propose/execute appropriate response actions. Key metrics include detection time, accuracy of analys...
Challenge input
{"incident_scenario": "<description_of_security_event>", "mock_log_data": "<json_of_simulated_logs>"}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{"detection_timestamp": "<ISO_timestamp>", "threat_classification": "<type>", "root_cause_analysis": "<text>", "remediation_plan": "<text>", "mcp_t...
- The system correctly identifies the type of security incident.
- The system demonstrates effective use of at least 3 distinct MCP-enabled tools.
- Internal logs show clear evidence of A2A messages exchanged between agents.
- TimeToAction target: 60
- 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
- The system correctly identifies the type of security incident.
- The system demonstrates effective use of at least 3 distinct MCP-enabled tools.
- Internal logs show clear evidence of A2A messages exchanged between agents.
Metrics
- TimeToAction · Target 60
- RemediationPlanCompleteness · Target 0.9
Workflow tasks
- Task 1
Input: {"incident_scenario": "<description_of_security_event>", "mock_log_data": "<json_of_simulated_logs>"}
Output: {"detection_timestamp": "<ISO_timestamp>", "threat_classification": "<type>", "root_cause_analysis": "<text>", "remediation_plan": "<text>", "mcp_tool_calls": "<list_of_tool_calls_with_args>"}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.