This repository contains the complete implementation and resources for the Agentic AI portion of the ETX-2 Hackathon. It demonstrates a comprehensive agentic AI use case that includes:
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Intelligent AI Agents: Python-based agent LLaMA Stack (LLS) for autonomous task execution (and an optional example agent that leverages DSPy for context engineering)
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Tool and Model Context Protocol (MCP) Integration: Built-in LLS Stack tools for internet search, and standaloneMCP servers for GitHub, OpenShift, and connection to other external services
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Infrastructure as Code: Complete Kubernetes/OpenShift deployment configurations using full GitOps principles
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Observability Stack: Monitoring, tracing, and logging capabilities for AI agent performance
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Educational Content: Comprehensive documentation and lab guides built with Antora for consumption by the ETX AI participants
The project showcases modern AI agent architectures with enterprise-grade deployment patterns, featuring distributed model serving, secure secret management, and scalable container orchestration. It serves as both a working implementation and a learning resource for building production-ready agentic AI systems. This can be used as a template for building and delivering your own agentic AI use cases.
Please see the Contributing Guide for information on how to contribute to this project.