Agentic AI is where the hiring gap in India is widest right now. Plenty of engineers can call an LLM API. Far fewer can build a system that plans a multi-step task, calls the right tools in the right order, recovers when a tool returns garbage, and does not silently loop forever burning tokens. That second skill set is what this part of the course builds.
Short answer: AIDisha teaches Agentic AI as the final phase of its 6-month AI Engineer course, using LangChain, LangGraph, the Model Context Protocol and AWS Bedrock AgentCore. It is taught after Python, ML, NLP, Generative AI and RAG, because agents are built from those parts. Total course fee ₹34,994.
What makes a system "agentic"
An ordinary LLM call is a function: text in, text out. An agent adds three things.
- Planning — decomposing a goal into steps rather than attempting it in one shot.
- Tool use — calling APIs, querying databases, searching the web, running code, and reading the results back into its own context.
- State and iteration — remembering what has already been tried, and choosing the next action based on what actually happened rather than on what was planned.
Each of those introduces failure modes that do not exist in a single call: plans that never terminate, tools invoked with malformed arguments, state that grows past the context window, and errors that compound silently across steps. Building agents is largely about engineering around these.
Course content
Tool calling and function schemas
Defining tools an LLM can invoke reliably; using Pydantic to enforce argument schemas; handling tool errors, timeouts and rate limits; and writing tool descriptions that the model interprets correctly — a surprisingly large share of agent failures trace back to ambiguous tool descriptions.
LangChain agents
The ReAct pattern; structured and tool-calling agents; short-term and long-term memory; retrieval as a tool; and where LangChain's abstractions help versus where dropping to raw API calls is clearer.
LangGraph — stateful multi-agent systems
Modelling agent workflows as explicit graphs with nodes, edges and shared state; conditional routing; cycles with termination guarantees; human-in-the-loop checkpoints; persistence across runs; and supervisor patterns coordinating multiple specialised agents. LangGraph is the framework most production agent work in India is converging on, and it gets the deepest treatment in the course.
Model Context Protocol (MCP)
MCP as a standard interface between models and external tools and data sources; building an MCP server; connecting agents to it; and why a standard protocol matters once you have more than a handful of integrations.
Debugging, tracing and evaluation
LangSmith tracing to see exactly which step failed and why; evaluating agent trajectories rather than only final outputs; regression testing agents against fixed scenarios; and setting hard limits on iterations and spend so a misbehaving agent cannot run up an unbounded bill.
Deployment
Serving agents through FastAPI; AWS Bedrock AgentCore for managed hosting; containerised deployment with Docker; and production monitoring with CloudWatch, Prometheus and Grafana.
The project
A multi-agent LangGraph system in which several agents with distinct tools and responsibilities collaborate on a task through shared state — with tracing, evaluation, iteration limits and a real deployment. It is deliberately the most demanding project in the programme, and it is the one that tends to carry interviews.
Where Agentic AI sits in the 6-month curriculum
| Phase | Focus |
|---|---|
| Months 1–2 | Python, software fundamentals, FastAPI, Linux, Git |
| Month 3 | Machine learning, deep learning, NLP and transformers |
| Months 3–4 | Generative AI, LLMs, prompt engineering, embeddings, RAG and vector databases |
| Months 4–5 | Agentic AI — LangChain, LangGraph, MCP, multi-agent systems |
| Months 5–6 | MLOps, Docker, Kubernetes, CI/CD, AWS deployment, projects and interview preparation |
Agentic AI is not sold separately, because taught separately it does not work. An agent is an LLM plus tools plus retrieval plus state management plus evaluation — you need all of those first.
Frequently asked questions
What is Agentic AI?
Agentic AI describes systems where a large language model does more than answer — it plans a sequence of steps, chooses and calls tools such as APIs, databases or code execution, observes the results, and adapts its next action accordingly. The distinguishing features are autonomy over multi-step tasks, tool use, and persistent state across steps.
What is the difference between Agentic AI and RAG?
RAG retrieves relevant context and generates a single grounded answer — one pass, no decision-making. An agent decides what to do: it may run a retrieval, then call an API based on what it found, then execute code, then loop. RAG is often one tool available to an agent. AIDisha teaches RAG first, then builds agents on top of it.
Which agent frameworks does the course teach?
LangChain for tools, memory and structured agents; LangGraph for stateful, cyclical multi-agent workflows with explicit control flow; the Model Context Protocol (MCP) for standardised tool and data integration; AWS Bedrock AgentCore for managed deployment; and LangSmith for tracing and debugging agent runs.
Is Agentic AI worth learning in 2026?
Agentic AI is the fastest-growing category in Indian AI job listings, and the supply of engineers who have actually shipped an agent to production is small. It is also the harder half of the field — agents fail in ways single-shot LLM calls do not, and engineers who can debug those failures are disproportionately valuable.
Do I need to know Generative AI before learning Agentic AI?
Yes, and the order matters. Agents are built from LLM calls, tool definitions, retrieval and state management. Attempting agents without understanding prompting, embeddings, RAG and evaluation produces systems you cannot debug. AIDisha's curriculum runs Python and ML, then NLP, then Generative AI and RAG, then Agentic AI.