Enterprise AI Integration Layer
This challenge involves building an advanced enterprise AI integration layer. This system will enable seamless communication between LLMs and existing enterprise systems, automating complex workflows across cloud services and data center infrastructure. It will utilize MCP-enabled tool integration for accessing proprietary APIs and data, and A2A protocol for cross-departmental agent collaboration. Developers will leverage Semantic Kernel for integrating LLMs like Claude Sonnet 4 and OpenAI GPT 5.2 with traditional applications, Haystack for robust RAG over diverse enterprise knowledge bases, and Lightning AI for MLOps, deployment, and managing stateful DAG agents. The goal is to demonstrate how modern AI can drive significant operational efficiency and innovation in large organizations.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a scalable enterprise AI integration layer using Semantic Kernel, Haystack, and Lightning AI for data center and cloud service automation with MCP-enabled tool integration.
How work is evaluated
The evaluation will assess the enterprise AI integration layer's ability to automate workflows, integrate with simulated enterprise systems, accurately retrieve information via RAG, and demonstrate robust agent collaboration.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design MCP-enabled tool integration for proprietary API access.
Orchestrate A2A protocol agents for cross-departmental collaboration.
Deploy and manage scalable AI services using Lightning AI.
Master Semantic Kernel for LLM integration with enterprise applications.
Implement robust RAG pipelines using Haystack for enterprise knowledge access.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the enterprise AI integration layer's ability to automate workflows, integrate with simulated enterprise systems, accurately retrieve information via RAG, and demonstrate robust agent collab...
Challenge input
{ 'incident_alert': { 'severity': 'string', 'service': 'string', 'log_message': 'string' } }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ 'diagnostic_report': 'string', 'action_plan': 'array', 'tools_invoked': 'array', 'a2a_interactions': 'array' }
- All critical incident response tasks must successfully generate a valid action plan and invoke the correct...
- At least 90% of RAG queries must provide contextually accurate answers with correct source attribution.
- ToolIntegrationSuccessRate target: 98%
- 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
- All critical incident response tasks must successfully generate a valid action plan and invoke the correct tools.
- At least 90% of RAG queries must provide contextually accurate answers with correct source attribution.
Metrics
- ToolIntegrationSuccessRate · Target 0.98
- A2ACollaborationEfficiency · Target 3
- WorkflowCompletionTime · Target 15
Workflow tasks
- Task 1
Input: { 'incident_alert': { 'severity': 'string', 'service': 'string', 'log_message': 'string' } }
Output: { 'diagnostic_report': 'string', 'action_plan': 'array', 'tools_invoked': 'array', 'a2a_interactions': 'array' }
Execution harness: Python
- Task 2
Input: { 'user_query': 'string', 'knowledge_base_mock_url': 'string' }
Output: { 'answer': 'string', 'source_documents': 'array', 'rag_pipeline_trace': 'array' }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.