Agentic AI Training India — Build Autonomous AI Agents with LangGraph & CrewAI
RAG answers questions. Agents get things done. The next wave of enterprise AI isn't a better chatbot — it's a system that plans, acts, loops, and delivers a completed outcome. This training is where your team learns to build those systems.
What is agentic AI — and why does it matter now?
Agentic AI describes AI systems that act autonomously to complete a goal — breaking it into sub-tasks, deciding which tools to call, evaluating whether they're on track, and looping until done. This is fundamentally different from a chatbot that answers one question at a time.
A RAG system answers "What does our refund policy say about damaged goods?" An agentic system answers that question, drafts a response email to the customer, checks whether the item was within the 30-day window, logs the decision in your CRM, and schedules a follow-up — all from a single trigger.
Indian enterprises in fintech, e-commerce, SaaS, and IT services are deploying agentic AI for sales intelligence, DevOps automation, financial reconciliation, HR workflows, and code review. The teams that understand how to build and control these systems have a significant structural advantage over teams still building single-prompt chatbots.
The agent frameworks covered in training
The agentic AI landscape moves fast. We cover the frameworks that actually run in production — not just the one with the most GitHub stars.
LangGraph
Stateful agent orchestration — the production standard
CrewAI
Role-based multi-agent systems — fast team setup
AutoGen
Conversational multi-agent — Microsoft research-grade
ReAct Framework
Reason + Act loop — the underlying agent pattern
All four frameworks are covered in the Production AI Engineering corporate course.
What Indian enterprise teams are building with agentic AI
Real-world use cases your team will be equipped to build after training.
Automated Research Agents
Agents that search internal knowledge bases, scrape approved web sources, synthesise findings, and produce structured reports — without human orchestration.
Sales Intelligence Agents
Agents that research a prospect, pull CRM data, check LinkedIn, draft a personalised email, and log the action — triggered by a single instruction.
DevOps Automation Agents
Agents that monitor systems, diagnose errors from logs, attempt fixes, escalate to humans only when stuck, and document everything they did.
Financial Analysis Agents
Agents that pull data from multiple sources, run calculations, flag anomalies, and produce reconciliation reports for human review.
HR & L&D Automation
Agents that track training completion, personalise learning paths, send nudges, and report to managers — all triggered from HR data.
Code Review Agents
Agents that review pull requests against your style guides, run specified checks, comment suggestions, and escalate complex decisions to senior engineers.
Agentic AI Training — Frequently Asked Questions
What is agentic AI?+
Agentic AI refers to AI systems that can autonomously reason, plan, make decisions, and take multi-step actions to complete a goal — rather than simply responding to a single prompt. An agentic AI system breaks a complex task into sub-tasks, decides which tools to call, evaluates its own outputs, and loops until it has completed the goal. This is fundamentally different from a chatbot or a single RAG query.
What is LangGraph and why is it the standard for AI agents?+
LangGraph is a library built on LangChain that enables stateful, cyclic agent workflows using a graph-based execution model. Unlike simple chains that run top-to-bottom once, LangGraph agents can loop, branch, reflect, retry, and maintain state across many steps. It is the production-grade standard for building autonomous AI agents in 2025–2026 because it gives developers precise control over agent behaviour and makes complex workflows debuggable.
LangGraph vs CrewAI vs AutoGen — which should my team use?+
LangGraph is best for teams that need fine-grained control over agent behaviour and production reliability — it requires more code but gives you full visibility. CrewAI is better for quickly setting up role-based multi-agent teams with minimal code. AutoGen (from Microsoft) is strong for research and conversational multi-agent scenarios. For enterprise production systems, LangGraph is typically the right choice. Our training covers all three and explains when each is appropriate.
Do we need to know RAG before learning agentic AI?+
Yes — RAG is the foundation. Most production AI agents use RAG as one of their primary tools (retrieving information to inform decisions). Our agentic AI training module follows the RAG module in the Production AI Engineering programme, so participants arrive with working RAG knowledge and build on top of it.
Ready to build agents, not just chatbots?
Tell us what your team wants to automate. We'll design a training programme around those use cases and send a proposal in 24 hours.
Agentic AI is covered in depth in the full Production AI Engineering course.