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.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master Semantic Kernel for seamlessly integrating advanced LLM capabilities (Claude Sonnet 4, OpenAI GPT 5.2 ) into existing enterprise applications and legacy systems.
Implement robust RAG pipelines using Haystack to provide context-aware responses and actions based on diverse enterprise data sources, including internal documentation, network logs, and incident reports.
Design MCP-enabled tool integration for agents to securely interact with proprietary enterprise APIs, cloud service management platforms (e.g., Azure Resource Manager, AWS CloudFormation), and data center orchestration systems.
Deploy and manage scalable AI services using Lightning AI's MLOps platform, ensuring high availability, performance, and compliance within a corporate data center or hybrid cloud environment.
Develop stateful DAG-based workflows (using Lightning AI's orchestrator or integrated tools) for automating complex IT operations like network provisioning, server patching, or incident response, with adaptive thinking budgets for resource optimization.
Orchestrate A2A protocol agents for cross-departmental collaboration, enabling AI to coordinate tasks between network operations, cloud engineering, customer support, and cybersecurity teams.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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