Agentic AI Engineering: Planning, Tool Use & Multi-Agent Orchestration
Build AI Agents That Plan Tasks Use Tools and Collaborate to Solve Real-World Problems
About This Course
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.
Workshop Structure
📅 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.
Who Should Enrol?
- 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.
Important Dates
Registration Ends
November 2, 2026
IST 4:30 PM
Workshop Dates
November 2, 2026 – November 4, 2026
IST 5:30 PM
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.
Fee Structure
Student
₹2499 | $60
Ph.D. Scholar / Researcher
₹3499 | $70
Academician / Faculty
₹4499 | $80
Industry Professional
₹5499 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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