1 Day Workshop on Agentic AI & Workflow Automation

Duration: 1 Day | 8 Hours of Training

Eligibility: Engineering, biomedical and other students, researchers, and working professionals interested in AI. Basic Python familiarity is recommended along with some Genai concepts like LLM, RAG.

1. AI Agent Foundations

Components of an AI agent: model, instructions, tools, memory and control logic.

Understand the plan–act–observe cycle.

Compare predefined workflows with agent-driven decisions.

Identify practical use cases and limitations.

Demonstration: Observe how an agent decides which action to take.

2. Tool Calling & API Integration

Define tools and their input schemas.

Understand how an LLM requests tool execution.

Connect an LLM to a calculator and document retrieval function.

Validate tool inputs and handle basic execution errors.

Hands-on: Extend the Day 1 assistant to select and invoke tools.

3. Agent Workflows with LangGraph

Introduction to graph-based orchestration.

Understand nodes, edges, state and conditional routing.

Build a simple multi-step workflow.

Add a basic validation step and human approval checkpoint.

Hands-on: Create a workflow that retrieves information, drafts an answer and routes it for review.

4. Agentic RAG & Multi-Agent Collaboration

Understand when an agent should retrieve information.

Review retrieved information before generating an answer.

Introduction to researcher, writer and reviewer agent roles.

Demonstration: Extend the existing document assistant with a retrieval decision and review step.

Scope: Conceptual discussion and guided demonstration, without implementing a separate multi-agent system.

5. Capstone Project: Intelligent Document & Research Assistant

Accept user questions about uploaded documents.

Retrieve relevant information and provide source references.

Call a tool when required.

Generate a structured response.

Validate the answer and request human approval before a designated action.

Run the application through a basic Streamlit interface.

Implementation approach: Participants extend a prepared starter project. The trainer provides the core retrieval pipeline and application structure so participants can focus on integration and understanding.

Workshop Outcomes

By the end of the workshop, participants will have practical experience in:

Understanding Generative AI, LLMs and AI agents.

Writing effective prompts and generating structured outputs.

Building an LLM-powered conversational assistant.

Creating a document question-answering system using RAG.

Connecting AI applications to tools and APIs.

Building a basic controlled workflow with LangGraph.

Extending a starter project into an intelligent document and research assistant.

Understanding the limitations and safety considerations of agentic applications.