The AI Trinity

In this hands-on workshop, participants will explore the cutting-edge world of Large Language Models (LLMs), Reinforcement Learning (RL), and building autonomous AI agents. Combining theory with hands-on coding examples, this session is designed to bridge the gap between theoretical concepts and real-world applications. By the end of the workshop, participants will have a solid understanding of how to build, train, and fine-tune an LLM for specific applications as well as how to increase their utility with RAG and AI Agents.

*Note: These are tentative details and are subject to change.

This workshop is designed to provide a comprehensive overview of LLMs, right from foundational NLP concepts to the latest in this domain. This workshop is aimed at working professionals but covers the required details to help beginners get started. You will gain valuable insights and hands-on experience to learn & adapt concepts to your professional lives.

Key Takeaways:

• Understand the fundamentals of Language Models and Transformer architectures.
• Gain hands-on experience with LLMs and related concepts such as PEFT, Prompt Engineering, RAGs,  LLM frameworks like DSPY.
• Explore advanced topics such as Reinforcement Learning from Human Feedback (RLHF) and tool-calling through MCP

*Note: These are tentative details and are subject to change.

New to the world of Agentic AI and want to quickly get proficient in the key aspects of learning, building, deploying and monitoring Agentic AI Systems? This is the workshop for you! In this workshop you will get a comprehensive coverage of the breadth as well as deep dive into the depth of the vast world of Agentic AI Systems. Over the course of six modules, you will spend the entire day focusing on the following key areas:

• Learn essential concepts of Generative AI, Agentic AI and Agentic RAG Systems
• Deep dive into industry standard design patterns for architecting Agentic AI Systems - Tool-Use, Reflection, Planning, Multi-Agent
• Leverage Industry-Standard frameworks including LangChain, LangGraph and CrewAI to build simple and advanced Agentic AI & Agentic RAG Systems
• Learn basics of how to deploy Agentic AI Systems as APIs as well as debug and monitor them

While we want to keep the discussions as framework and tool-agnostic as possible, since 90% of the workshop will be hands-on focused; we will be using LangChain and LangGraph (currently the leading framework used in the industry)  for most of the hands-on demos for building Agents and also a bit of CrewAI. While the focus of the workshop is more on building Agentic AI Systems we will also showcase how you can build a basic web service or API on top of an Agent using FastAPI and deploy and monitor it using frameworks like LangFuse or Arize AI Phoenix.

Important Note: You may need to register for some platforms like Tavily, WeatherAPI etc for the workshop (no billing needed), we will send the instructions ahead of time. That will be essential for running the hands-on code demos live along with the instructor in the session.

Additional Points

• Prerequisites: Solid understanding of Python, NLP and Generative AI will be useful
• Content Provided: Slides, complete code notebooks, datasets
• Infrastructure: Most of the hands-on demos we will do on Google Colab for the deployment and monitoring section we will provide the cloud infrastructure (either Colab or Runpod.io).

*Note: These are tentative details and are subject to change.

In this hands-on technical workshop, we’ll explore multi-agent orchestration using CrewAI, diving into how autonomous agents can collaborate to solve complex problems. You’ll learn how to define, configure, and coordinate agents using CrewAI’s core components, all in Python.

We’ll walk through the main classes of problems this approach is suited for and guide you step by step through building real-world workflows. Topics include agent creation, orchestration strategies, tool integration (including custom tools), and LLM-agnostic setups. We’ll also look at how to connect CrewAI with external libraries such as Streamlit to bring your solutions to life.

What You’ll Learn:

• How to define agents and tasks in CrewAI
• Coordinating agents through orchestration flows
• Using and creating tools and custom tools
• Working with different LLMs (LLM-agnostic setups)
• Integrating with libraries like Streamlit
• Best practices for building maintainable and scalable agent systems

Learn how to operationalize Large Language Models on AWS in this hands-on workshop.

The curriculum covers the complete LLMOps lifecycle including CI/CD pipelines, SageMaker deployment, LangChain integration, and monitoring with Langfuse.

Through practical labs, participants will build production-ready LLM systems incorporating best practices for scalability and reliability.

*Note: These are tentative details and are subject to change.

This workshop introduces AgentOps, a subcategory of GenAIOps, which focuses on the operationalization of AI agents. It dives into how we can create, manage, and scale generative AI agents effectively within production environments. You’ll learn the essential principles of AgentOps, from external tool integration and memory management to task orchestration, multi-agent systems, and Agentic RAG. By the end of the workshop, participants will have the skills to build and deploy intelligent agents that can automate complex tasks, handle multi-step processes, and operate within enterprise environments.

*Note: These are tentative details and are subject to change.

Agentic RAG adds a “brain” to the RAG pipeline – bringing reasoning, tool use, and adaptiveness – which translates to tangible business value in accuracy, flexibility, and user trust

This workshop is a deep dive into Agentic RAG (Retrieval-Augmented Generation) – an emerging approach that combines the power of LLM-based agents with retrieval techniques to build smarter AI applications. Over an 8-hour session (of course including breaks in between), participants will explore how to move beyond “vanilla” RAG pipelines and infuse them with agentic behavior for greater flexibility and intelligence. The workshop is hands-on, using Google Colab notebooks for each module so attendees can practice concepts live. We’ll leverage LangGraph (a LangChain-based framework for agent orchestration) along with the LangChain ecosystem (vector stores, tools, etc.) to design and implement these systems. The theme is highly relevant in today’s AI landscape – many enterprises are already moving from basic RAG to agent-driven systems to power next-generation assistants. In fact, new frameworks like LangGraph have emerged to meet this need, making now the perfect time to master Agentic RAG development. The workshop will also cover practical tips for building enterprise-grade agentic RAG applications

Big Business Benefits of Agentic RAG

Agentic RAG empowers large-scale, enterprise-ready AI systems by combining retrieval-augmented generation with intelligent, decision-making agents. The result? Smarter, more reliable, and adaptable GenAI solutions.

• Higher Accuracy & Trust: Agents reason through queries, validate retrieved information, and reduce hallucinations – essential for business-critical use cases.
  
  
• Dynamic Multi-Source Retrieval: Unlike traditional RAG limited to one vector DB, agents can pull context from multiple sources—internal files, databases, APIs, or the web—dynamically.
  
  
• Autonomous Problem Solving: Agentic systems can break down complex queries, reformulate them, and perform multi-step retrieval and reasoning with minimal human intervention.
  
  
• Scalable & Adaptable: Agent orchestration supports modular workflows that scale across domains, datasets, and tools—making the solution extensible and future-proof.
  
  

💡 Think of Agentic RAG as adding a decision-making brain to your RAG pipeline—boosting precision, flexibility, and business value.

*Note: These are tentative details and are subject to change.