Workshop Registration End Date :11 Nov 2026

Virtual Workshop

Agentic AI Engineering: Planning, Tool Use & Multi-Agent Orchestration

Build AI Agents That Plan Tasks Use Tools and Collaborate to Solve Real-World Problems

Skills you will gain:

About Workshop:

This three-day hands-on workshop introduces the design and implementation of agentic AI systems using Python, LangChain and LangGraph. Participants progressively build a tool-enabled agent, add context management and retrieval, and develop a small multi-agent workflow. Practical sessions cover structured outputs, error handling, human approval and performance evaluation through a guided research assistant project.

Aim: To equip participants with practical skills to build AI agents that plan tasks, use tools and collaborate through reliable Python-based workflows.

Workshop Objectives:

  • Understand agent architectures, task planning and execution loops.
  • Integrate Python functions and document retrieval as agent tools.
  • Manage context and workflow state across multiple steps.
  • Coordinate specialist agents using a supervisor workflow.
  • Apply error handling, human review and basic evaluation.

What you will learn?

📅 Day 1: Agent Foundations, Task Planning & Tool Use

Focus: Build an agent that selects and uses tools to complete a task.

  • Understand AI agents, predefined workflows and agent execution loops.
  • Break user requests into steps with clear completion criteria.
  • Define Python tools with structured inputs and outputs.
  • Connect an agent to a document search tool and calculator.
  • Apply tool-call limits and handle basic execution errors.

🛠️ Hands-on:
Build a single agent that searches a supplied document collection, performs calculations and produces a structured answer.

🧰 Tools: Python, Jupyter Notebook, LangChain, Pydantic and an LLM API.

📅 Day 2: Context, Retrieval & Reliable Workflows

Focus: Maintain task context and build workflows that can recover from failures.

  • Manage conversation history, task state and relevant context.
  • Introduce retrieval-augmented generation using supplied documents.
  • Build conditional workflows with LangGraph.
  • Implement checkpoints, retries and stopping conditions.
  • Add human approval before a selected tool action.

🛠️ Hands-on:
Extend the agent into a document research workflow that retrieves evidence, maintains state and pauses for user review.

🧰 Tools: LangGraph, local document retrieval, checkpointing and Python logging.

📅 Day 3: Multi-Agent Orchestration, Evaluation & Capstone

Focus: Coordinate specialist agents and assess the quality of their outputs.

  • Compare single-agent and multi-agent designs.
  • Assign researcher, analyst and reviewer responsibilities.
  • Implement a supervisor workflow with controlled delegation.
  • Share structured results between agents.
  • Evaluate task completion, evidence support, tool errors, latency and token usage.
  • Inspect execution traces and identify unnecessary agent steps.

🛠️ Hands-on Capstone:
Build a small AI research team that searches supplied documents, analyses a curated CSV dataset and prepares an evidence-supported report for human review.

🧰 Tools: LangGraph, Pandas, Pydantic and execution tracing.

Mentor Profile

Fee Plan

StudentINR 2499/- OR USD 60
Ph.D. Scholar / ResearcherINR 3499/- OR USD 70
Academician / FacultyINR 4499/- OR USD 80
Industry ProfessionalINR 5499/- OR USD 100

Important Dates

Registration Ends
11 Nov 2026 Indian Standard Timing 4:30 PM
Workshop Dates
09 Nov 2026 to
11 Nov 2026  Indian Standard Timing 5:30 PM

Get an e-Certificate of Participation!

Intended For :

  • Undergraduate and postgraduate students in Computer Science, AI, Data Science and related disciplines.
  • Research scholars, academicians and professionals interested in AI-assisted research and automation.
  • Software developers and data analysts seeking practical agent development skills.

Prerequisites: Basic Python programming, familiarity with generative AI and access to the selected LLM API. Prior experience building AI agents is not required.

Career Supporting Skills

Workshop Outcomes

  • Build an AI agent with structured tool inputs and outputs.
  • Create a document research workflow with source-supported responses.
  • Implement checkpoints, execution limits and approval steps.
  • Coordinate research and analysis agents to generate a report.
  • Assess task completion, tool errors, response quality and resource usage.

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 →
Support