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Workshops:

When it comes to GenerativeAI-powered applications, one of the most trending framework is that of Agent, which can be defined as highly-specialized entities that can achieve user's goal by panning and interacting with the surrounding ecosystem. In this session, we are going to explore the main components that features AI Agents, such as LLMs, Prompts, Memory and Tools.
LlamaIndex is a powerful open-source framework for building agentic RAG applications. In this tutorial we will start from the basics of working with LLMs in LlamaIndex, add information loading and parsing, embedding and storing data, and creating a RAG pipeline. With our RAG pipeline in place we will turn it into a tool that can be used by agents, and build an agent in LlamaIndex that utilizes that tool for information retrieval.

Finally, we will build a full-scale LlamaIndex Workflow that uses agentic techniques to retrieve, reflect, and error correct.

This session will dive into the creation of AI Agents using modern frameworks like CrewAI, LangGraph, and more. The goal is to equip all attendees with the knowledge and skills to build systems that leverage multiple tools for complex, real-world tasks. This training will show participants how to design and implement Agentic AI workflows capable of integrating APIs, executing code, generating images, and performing advanced decision-making to accomplish multi-step operations.

Over two hours, participants will gain hands-on experience setting up a development environment, designing and lanching agents, crafting chain-of-thought prompts, and creating a custom AI agent framework that automates workflows.

Our focus in this session is to introduce ideas with the aim to bridge the gap between the world of AI agents and the world of optimal decision making. "Should I give this customer a discount?", "Should I pick supplier A instead of B?", "What's the best hotel to recommend this customer?"

While most AI agentic workflows have been developed on top of unstructured data (such as RAG applications), our objective here is to learn how we can connect AI agents to tabular datasets, and use business context to extract actionable insights from the data.

How do I know if what comes out of my agentic LLM application is correct?” “What does a good output look like?” “How can I avoid hallucinations and wrong answers?” Just as in 2024, everyone working to develop production LLM applications is asking these questions, and rightly so!

This year, however, agents are on the rise, as are the number of companies building, shipping, and sharing LLM application prototypes.

In this event, we’ll explore the latest on agent evaluation from the leading LLM application evaluation framework: RAG ASsessment (RAGAS).

The session's primary objective is to provide practical implementation experience with agent-based architectures. Attendees will learn to design agent roles, implement task delegation patterns, and manage complex workflows - skills directly applicable to their professional projects. By working through our paper-to-podcast pipeline, participants will understand how to break down complex tasks into manageable components and orchestrate multiple agents effectively.

Whether you're building content automation systems, optimizing business processes, or developing AI applications, you'll gain immediately applicable skills for creating robust agent-based solutions.

Let's be honest, integrating complex AI agents and powerful LLMs into our everyday workflows is still tricky, and we haven't truly moved beyond treating LLMs as glorified chatbots. This talk cuts through the hype and shows opinionated workflows of how to use Gemini models to solve real-world problems. We'll dive into the evolution of LLM agent frameworks (LangChain and LangGraph) and where the industry appears to be going forward. Join us to learn how to work with Gemini for simple to advanced generative AI app development, regardless of your experience level. And gain the skills and knowledge to build your own generative AI solutions to everyday problems.

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