Agentic AI, AI Agents & Multi-Agent Systems
By the end of Level 5, participants will understand the evolution from GenAI to Agentic AI, build tool-using agents in Python, implement workflows with LangGraph, understand function/tool calling, build multi-agent systems, and connect agents with MCP.
What you'll master in Level 4
By the end of Level 5, participants will understand the evolution from GenAI to Agentic AI, build tool-using agents in Python, implement workflows with LangGraph, understand function/tool calling, build multi-agent systems, and connect agents with MCP.
Training Objectives & Core Deliverables
Understand the evolution from GenAI to Agentic AI.
Explain what an AI agent is and how it differs from a chatbot/LLM application.
Understand agent architecture, reasoning, planning and memory.
Build tool-using AI agents with Python.
Implement workflows using LangChain/LangGraph.
Understand function/tool calling.
Build multi-agent workflows.
Understand MCP (Model Context Protocol) and its role in connecting agents with external tools and data.
Design basic enterprise Agentic AI solutions.
Day-by-Day Training Schedule & Live Labs
Introduction to Agentic AI
Build a basic AI agent that decides which action/tool to execute.
Tool Calling & Agent Workflows
Build an agent using weather, calculator and knowledge-retrieval tools.
LangChain & LangGraph Agents
Build a stateful research → analysis → response workflow.
Multi-Agent Systems & MCP
Build a multi-agent workflow and connect an external MCP tool.
Enterprise Agentic AI & Capstone
End-to-end enterprise multi-agent assistant.
Detailed Module-wise Curriculum
What are tools and why agents need tools
Function calling & tool schemas with structured input/outputs
Tool selection by an LLM & handling tool errors
Common Tools: Calculator, Weather API, Search, Database, File system, REST APIs, Python functions
Agent Reasoning: ReAct concept (Reasoning + Action)
Agent loops & planning vs execution
Deterministic vs LLM-driven workflows
Memory: Short-term memory, conversation state, long-term memory introduction
Why LLM frameworks are needed: Models, Prompts, Tools, Chains, Agents, Retrievers
Why graph-based agent workflows? (LangGraph architecture)
State, Nodes, Edges, Conditional edges, Agent loops, Persistence
Agent Workflow pattern: User Request → Planner → Researcher → Analyzer → Response
Human-in-the-Loop: Human approval, review, and revision before sensitive actions
Why multiple agents? Specialized agents & agent collaboration
Agent communication & shared state: Sequential, Parallel, Supervisor architecture
Supervisor Agent pattern: Supervisor → Research | Data | Writing → Reviewer → Final Response
Model Context Protocol (MCP): Why MCP? Architecture (Host, Client, Server)
MCP Components: Tools, Resources, Prompts
MCP Workflow: AI Agent → MCP Client → MCP Server → External Tool/Data → Result
Enterprise Examples: Database access, File systems, CRM, HR systems, Enterprise knowledge bases
Designing Enterprise Agents: Architecture, responsibilities, tool selection, workflow, state, memory
Agent Safety & Governance: Guardrails, tool permissions, prompt injection, data privacy, human oversight
Agent Evaluation: Task completion, tool selection accuracy, response quality, groundedness, observability
Production Considerations: Reliability, latency, cost, monitoring, logging, error handling, scalability
Enterprise Multi-Agent AI Assistant Capstone architecture
Certificate of Technical Competency — Agentic AI, AI Agents & Multi-Agent Systems
Complete all live hands-on labs to earn this verifiable competency badge. This credential unlocks progression to full program graduation.
Enroll in Agentic AI, AI Agents & Multi-Agent Systems
Reserve your seat for upcoming live batches, syllabus consultation & fee structure.Frequently Asked Questions
Frequently Asked Questions: Generative AI Training Plan
Find clear answers to common questions about our live interactive classes, cohort schedules, project mentoring, and career assistance.
You can choose to enroll in the complete 4-track master program (recommended for end-to-end expertise) or consult our academic team to join specific tracks matching your current skills.
Track 1: Scikit-learn, MLOps, ML Pipelines. Track 2: TensorFlow, Keras, CNN, NLP. Track 3: LangChain, Chroma/FAISS, RAG, Fine-Tuning. Track 4: LangGraph, MCP, ReAct, Multi-Agent Systems.
Yes, all classes are live, interactive online sessions with senior practitioners. Session recordings, code notebooks, datasets, and reading references are shared after each class.
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