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Agent Camp - Austin

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What to expect

Agent Camp in Austin is an intensive workshop designed to transform participants from foundational AI concepts into advanced builders of production-ready AI agent systems. Whether you're exploring how to build autonomous agents with sophisticated reasoning capabilities or evaluating complex agentic workflows, this comprehensive session covers the entire journey from theory to practical implementation.

What You'll Learn

This workshop spans seven deep-dive modules covering the complete agentic AI landscape. You'll start with the fundamentals—understanding how AI evolved from passive models to autonomous, goal-driven agents capable of planning, reasoning, and acting. The session explores the distinction between agents, workflows, and automation, examining core components including LLMs, tools, memory systems, and planning architectures.

The ecosystem module reveals the infrastructure powering modern AI agents, from frameworks like LangChain and Google ADK to orchestration layers, vector databases, and deployment strategies. You'll understand the trade-offs between cost, latency, and scalability when building agents for production environments.

LLM-as-a-Judge techniques demonstrate how to leverage language models themselves as evaluators, creating scalable automated evaluation pipelines through prompting strategies, pairwise comparison, and rubric-based scoring. The memory architecture session covers short-term, long-term, and episodic memory systems, exploring how agents retain context across interactions through vector databases and retrieval mechanisms.

Agent simulation and evaluation modules teach you to design robust testing pipelines using synthetic data generation, scenario-based testing, and continuous evaluation frameworks. The RAG deep-dive progresses from fundamental retrieval-augmented generation through Graph RAG and Agentic RAG systems, covering embeddings, chunking strategies, multi-hop reasoning, and hybrid search optimization.

Finally, explore emerging AI protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication that enable interoperability between agents, tools, and systems—essential knowledge for building scalable, decentralized agent ecosystems.

Expert Instruction

Rama Krishna Raju Samantapudi brings extensive expertise as an AI/ML Architect and Scientist specializing in scalable AI systems, LLM-powered applications, and agentic workflows. With deep experience across Search, NLP, Conver

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