Orchestrate A2A AutoGen Agents for Critical Infrastructure Recovery
In response to a major data center outage, this challenge requires building an advanced multi-agent system capable of automated incident detection, root cause analysis, and orchestrating recovery procedures for critical IT systems. Participants will use AutoGen to set up a team of specialized agents, powered by Claude Opus 4.1, communicating via a simulated A2A (Agent-to-Agent) protocol. The system should intelligently prioritize recovery tasks based on system criticality and estimated impact. The solution will demonstrate the use of adaptive thinking budgets, allowing agents to allocate more computational resources (simulated deep reasoning) to complex or high-priority issues while maintaining rapid responses for simpler diagnostics. Agents will need to interact, share observations, and collectively decide on the optimal recovery path, integrating with simulated system monitoring and control APIs to execute commands and verify restoration.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AutoGen multi-agent system with Claude Opus 4.1 agents for automated critical infrastructure incident response, using A2A protocol for collaboration and adaptive thinking budgets for resource allocation.
How work is evaluated
The solution will be evaluated on its ability to correctly diagnose the root cause of a simulated outage, generate a coherent recovery plan, successfully execute (simulated) recovery actions, and verify system restoration, all while demonstrating effective A2A communication and adaptive resource usage.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Implement adaptive thinking budgets to optimize agent resource allocation based on task complexity.
Integrate Claude Opus 4.1 for advanced reasoning in diagnostics and recovery planning.
Design and implement a multi-agent system using AutoGen for complex incident response scenarios.
Develop agents capable of secure, standardized communication via a simulated A2A protocol.
Orchestrate collaborative agent actions for automated system restoration and verification.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The solution will be evaluated on its ability to correctly diagnose the root cause of a simulated outage, generate a coherent recovery plan, successfully execute (simulated) recovery actions, and verify system restora...
Challenge input
{ "system_state": { "server_1_status": "offline", "database_status": "unavailable", "network_status": "degraded" }, "logs": ["string", "string"], "...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ "root_cause": "string", "identified_symptoms": ["string"], "confidence_score": float, "agent_communications_log": ["string"] }
- Verifies if the identified `root_cause` matches the expected cause from the logs.
- Checks if the `recovery_plan` contains the essential steps in a logical order.
- Confirms that all simulated `executed_actions` resulted in `SUCCESS` in `verification_results`.
- AdaptiveBudgetEfficiency target: 0.75
- 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
- Verifies if the identified `root_cause` matches the expected cause from the logs.
- Checks if the `recovery_plan` contains the essential steps in a logical order.
- Confirms that all simulated `executed_actions` resulted in `SUCCESS` in `verification_results`.
Metrics
- AdaptiveBudgetEfficiency · Target 0.75
- A2ACommunicationEffectiveness · Target 0.9
- PlanPrioritizationAccuracy · Target 0.85
Workflow tasks
- Task 1
Input: { "system_state": { "server_1_status": "offline", "database_status": "unavailable", "network_status": "degraded" }, "logs": ["string", "string"], "monitoring_alerts": ["string", "string"], "impacted_services": ["string"] }
Output: { "root_cause": "string", "identified_symptoms": ["string"], "confidence_score": float, "agent_communications_log": ["string"] }
Execution harness: Python
- Task 2
Input: { "root_cause": "string", "impacted_services": ["string"], "recovery_playbooks": {"service_name": ["steps"]}, "available_tools": ["string"] }
Output: { "recovery_plan": ["string"], "executed_actions": ["string"], "verification_results": {"action_id": "SUCCESS|FAILURE"}, "agent_communications_log": ["string"] }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.