New Year Offer End Date: 30th April 2024
Program

Agentic AI Systems: Autonomous Reasoning, Planning, Tool Use & Multi-Agent Orchestration

Build AI Agents That Reason, Plan, Use Tools, and Collaborate Autonomously.

Skills you will gain:

About Program:

This 3-day hands-on workshop introduces the core concepts and practical implementation of Agentic AI, from autonomous reasoning and task planning to tool integration and multi-agent orchestration. Participants will build progressively advanced AI agents using modern frameworks and understand how autonomous AI systems can be applied to research, automation, software, and enterprise workflows.

Aim: To equip participants with practical knowledge of Agentic AI systems, enabling them to design AI agents that can reason, plan tasks, use external tools, maintain context, and collaborate within multi-agent workflows.

Program Objectives:

  • Understand the architecture and working principles of Agentic AI.
  • Learn autonomous reasoning, planning, and task decomposition.
  • Develop agents capable of tool and function calling.
  • Understand agent memory, state, and external knowledge integration.
  • Explore MCP and human-in-the-loop workflows.
  • Design specialized agents for different tasks.
  • Build and orchestrate multi-agent systems.
  • Understand agent evaluation, guardrails, and reliability.

What you will learn?

Day 1: Agentic AI Fundamentals, Reasoning & Autonomous Planning

  • From LLMs to AI Agents: Difference between chatbots, assistants, workflows and autonomous agents.
  • Agentic AI Architecture: Model, instructions, goals, tools, memory, state and environment.
  • Reasoning & Decision Making: How agents break complex tasks into manageable actions.
  • Task Decomposition: Converting high-level goals into subtasks and executable steps.
  • Planning Strategies: Sequential, iterative and goal-directed planning.
  • ReAct-Style Agents: Combining reasoning with actions and observations.
  • Agent Loops: Observe → Reason → Act → Evaluate → Repeat.
  • State & Memory Fundamentals: Maintaining context across multi-step tasks.
  • Failure Modes: Hallucinations, looping, poor planning and uncontrolled autonomy.
  • Introduction to Agent Frameworks: OpenAI Agents SDK and LangGraph.

Hands-On

Build Your First Autonomous Planning Agent

Participants will create an AI agent that:

Receives a Goal → Decomposes the Task → Generates a Plan → Executes Steps → Reviews the Result

Tools: Python, Google Colab, OpenAI Agents SDK / LangGraph

Practical Output: A functional goal-driven AI planning agent.


Day 2: Tool-Using Agents, Memory & MCP Integration

  • Why Agents Need Tools: Moving beyond text generation to real-world task execution.
  • Function/Tool Calling: Connecting agents with Python functions and external services.
  • Agent Tool Selection: Allowing the model to determine which tool should be used.
  • Structured Outputs: Producing reliable machine-readable agent responses.
  • External Knowledge Access: Connecting agents with documents, search and databases.
  • Short-Term Memory: Maintaining context during an active task.
  • Long-Term Memory Concepts: Persisting information across agent interactions.
  • Introduction to MCP: Model Context Protocol for connecting AI systems with external tools and resources.
  • MCP Architecture: Client, server, tools and resources.
  • Tool Permissions: Controlling what an autonomous agent can and cannot execute.
  • Human-in-the-Loop: Adding approval checkpoints before critical actions.
  • Guardrails: Input, output and tool-execution safety controls.

Hands-On

Build an AI Agent with Tools & External Knowledge

Participants will develop an agent capable of:

User Request → Reasoning → Select Tool → Execute Tool → Retrieve Information → Generate Final Response

Participants will connect multiple tools and observe how the agent dynamically decides which one to use.

Tools: OpenAI Agents SDK, Python, LangGraph, MCP concepts, Google Colab

Practical Output: A tool-using AI agent capable of completing multi-step tasks.


Day 3: Multi-Agent Systems, Orchestration & Agent Evaluation

  • Why Multi-Agent Systems?: When one general-purpose agent is not enough.
  • Agent Specialization: Creating agents for research, analysis, planning and validation.
  • Multi-Agent Architectures: Supervisor, hierarchical, sequential and collaborative agent patterns.
  • Agent Handoffs: Transferring tasks between specialized agents.
  • Supervisor Agents: Dynamically assigning tasks to appropriate worker agents.
  • Agent-to-Agent Communication: Sharing context, outputs and intermediate decisions.
  • Multi-Agent Orchestration: Coordinating multiple agents toward one common objective.
  • Shared vs Individual Memory: Managing state across collaborating agents.
  • Agent Conflict & Failure: Handling contradictory outputs and failed tasks.
  • Human Oversight: Designing approval checkpoints in autonomous workflows.
  • Agent Evaluation: Measuring task completion, accuracy, reliability and tool-use success.
  • Tracing & Observability: Understanding what happened during agent execution.
  • Production Considerations: Cost, latency, reliability, scalability and security.

Hands-On

Build a Multi-Agent Research & Decision System

Participants will create a workflow such as:

User Goal →Supervisor Agent →Research Agent → Analysis Agent → Planning Agent → Validation Agent →Supervisor Review →Final Decision / Report

Each specialized agent will perform a defined role and collaborate through an orchestrated workflow.

Tools: LangGraph, OpenAI Agents SDK, Python, Google Colab

Practical Output: A functional multi-agent system with task delegation, agent handoffs and final-output synthesis.

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

Intended For :

  • Students: UG/PG students from Computer Science, AI, ML, Data Science, IT, Software Engineering, Robotics, and related disciplines.
  • Ph.D. Scholars / Researchers: Researchers working in Generative AI, LLMs, autonomous systems, multi-agent systems, NLP, and intelligent automation.
  • Academicians / Faculty: Faculty interested in Agentic AI research, curriculum development, and advanced AI applications.
  • Industry Professionals: AI/ML engineers, software developers, data scientists, GenAI developers, automation engineers, AI architects, and technology professionals.

Prerequisite: Basic familiarity with Python and AI/ML concepts is helpful, but advanced programming experience is not mandatory.

Career Supporting Skills

Program Outcomes

  • Design goal-driven autonomous AI agents.
  • Implement reasoning and planning workflows.
  • Connect AI agents with tools and external resources.
  • Build memory-enabled and context-aware agents.
  • Create supervisor and specialized-agent architectures.
  • Implement agent handoffs and task delegation.
  • Develop multi-agent collaborative workflows.
  • Apply guardrails and human oversight.
  • Evaluate agent performance and reliability.
  • Build an end-to-end Agentic AI application.

FREEDOM TO LEARN • 10% OFF All Courses & Workshops • Use Code: NANOINDIA10 • ⏳ Offer Ends In: Loading... • Learn Today. Lead Tomorrow. • Explore Programs →
FREEDOM TO LEARN • 10% OFF All Courses & Workshops • Use Code: NANOINDIA10 • ⏳ Offer Ends In: Loading... • Learn Today. Lead Tomorrow. • Explore Programs →
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