A2A Incident Response Agents
AI-driven cloud monitoring, this challenge focuses on building a sophisticated, autonomous incident response system. You will design and implement a multi-agent solution using advanced agent frameworks and models to detect, analyze, and resolve simulated cloud infrastructure incidents proactively. The system will leverage the A2A (Agent-to-Agent) protocol for seamless collaboration between specialized agents. These agents will use extended thinking with Claude Opus 4.1 to perform root cause analysis and integrate with simulated enterprise tools via an MCP (Multi-Agent Communication Protocol) server to execute remediation steps, minimizing downtime and human intervention.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a graph-based multi-agent system using LangGraph with Claude Opus 4.1 for autonomous incident detection and resolution.
How work is evaluated
The evaluation will assess the system's ability to autonomously detect, diagnose, and propose/execute remediation for simulated cloud incidents. Key metrics will include incident resolution time, accuracy of root cause analysis, and effective use of A2A communication and MCP tools.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design and implement a graph-based multi-agent architecture for incident response.
Develop MCP-enabled agents capable of interacting with external (simulated) enterprise systems.
Utilize Claude Opus 4.1 for advanced extended thinking and root cause analysis.
Integrate A2A protocol for secure, stateful agent-to-agent communication.
Orchestrate complex agent workflows for proactive issue resolution.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the system's ability to autonomously detect, diagnose, and propose/execute remediation for simulated cloud incidents. Key metrics will include incident resolution time, accuracy of root caus...
Challenge input
JSON object with 'incident_type', 'error_logs', 'metrics_data'
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON object with 'incident_id', 'status', 'root_cause_analysis', 'remediation_plan', 'executed_actions'
- System detects the simulated incident.
- Correct root cause is identified.
- A valid remediation plan is proposed.
- Time To Resolution target: 30
- 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
- System detects the simulated incident.
- Correct root cause is identified.
- A valid remediation plan is proposed.
- Evidence of A2A communication between agents.
Metrics
- Time to Resolution · Target 30
- Analysis Accuracy · Target 90
- Action Efficacy · Target 85
Workflow tasks
- Task 1
Input: JSON object with 'incident_type', 'error_logs', 'metrics_data'
Output: JSON object with 'incident_id', 'status', 'root_cause_analysis', 'remediation_plan', 'executed_actions'
Execution harness: Python
- Task 2
Input: JSON object representing the incident context.
Output: Log of A2A messages exchanged between agents.
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.