5 Agentic AI Courses for Turning LLM Applications Into Autonomous AI Systems

An LLM can answer a customer question. An autonomous system might check the order, retrieve the relevant policy, and request approval before acting. Making that sequence reliable is an engineering challenge.

Agents need tools, memory, and trustworthy data. Add multiple agents and task handoffs, and error recovery and testing become harder.

Even successful demos can break when an API fails, a retrieved document is outdated, or an agent repeats a task. Good training should teach you how to notice those failures.

These five India-focused programs cover agent architecture, retrieval, automation, and deployment.

5 Agentic AI Courses

#	Program & Provider	Duration	Fee	Best Aligned With
1	Certificate in Agentic AI - IIT Bombay	5 months	₹1,80,000 + GST	RAG, multi-agent coordination, agent evaluation
2	Professional Certificate in Generative & Agentic AI - BITS Pilani Digital	Approx. 35 weeks	₹96,000 + GST	RAG applications, automation, engineering
3	Microsoft Agentic AI for Practitioners - Great Learning	22 weeks	₹99,000 + GST	Azure agents, AgentOps, AI-103 preparation
4	Agentic AI: From Concepts to Practice - IIIT Hyderabad	12 weeks	₹90,000 + GST	Agent architecture, testing, deployment
5	Executive PG Programme in Applied AI and Agentic AI - IIIT Bangalore with upGrad	7 months	₹1,40,000	ML foundations, agent orchestration, MLOps

1. Certificate in Agentic AI - IIT Bombay

What happens when an agent calls the wrong tool? IIT Bombay starts with Python and LLM fundamentals, then explores routing, memory, the Model Context Protocol (MCP), and multi-agent coordination. When comparing the best agentic AI course, pay attention to how thoroughly it teaches agent evaluation.

Delivery & Duration: Five months online, with faculty sessions and labs.

Credentials: Certificate of Completion from IIT Bombay.

Program Highlights: RAG, GraphRAG, Chain-of-Thought, ReAct, agent handoffs, human oversight, prompt-injection defense, and deployment.

Tools & Technologies: LangGraph, CrewAI, ChromaDB, LangSmith, FastAPI, and Streamlit.

Outcomes: Participants practice building coordinated agents, evaluating their decisions, and preparing workflows for deployment.

Why should you choose this course?

• It treats agent reliability and human supervision as engineering requirements.
• The progression from retrieval to multi-agent systems suits professionals with prior programming experience.

2. Professional Certificate in Generative & Agentic AI - BITS Pilani Digital

Getting an LLM to use a tool is only the beginning. BITS Pilani Digital also teaches document retrieval, API calls, agent coordination, and output evaluation. A five-week Python and cloud foundation precedes the main modules.

Delivery & Duration: Approximately 35 weeks online, combining self-paced study, weekend sessions, and projects.

Credentials: Professional certificate from BITS Pilani Digital.

Program Highlights: Advanced RAG, agent orchestration, MCP, workflow automation, and evaluation methods.

Tools & Hands-on Learning: LangGraph, CrewAI, Qdrant, Langfuse, and Streamlit support labs and a working system capstone.

Outcomes: Learners build, test, and demonstrate AI applications connected to real data and external tools.

Why should you choose this course?

• It connects agent development with application design and testing.
• The capstone asks learners to demonstrate a working system, not just a chatbot.

3. Microsoft Agentic AI for Practitioners - Great Learning

Production agents need monitoring, access controls, and safe releases. This program focuses on Azure, progressing from LLM workflows to multi-agent orchestration. For those searching for Agentic AI Certification Microsoft, it includes AI-103 exam preparation, not automatic Microsoft certification.

Delivery & Duration: Twenty-two weeks online, with 15+ mentored sessions and Azure labs.

Credentials: Great Learning program certificate, plus preparation for the separate Microsoft AI-103 examination.

Program Highlights: Supervisor-worker patterns, MCP, A2A communication, AgentOps, CI/CD, security and cost management.

Hands-on Learning: Over 15 use cases and a capstone deploying an end-to-end agentic solution.

Outcomes: Participants practice building, evaluating, and deploying single-agent and multi-agent systems on Azure.

Why should you choose this course?

• Azure deployment and production operations are central, not optional additions.
• Projects incorporate evaluation, logging, and safe release practices.

4. Agentic AI: From Concepts to Practice - IIIT Hyderabad

Agents can fail when tools time out or tasks collide. IIIT Hyderabad uses software architecture to explain coordination patterns and engineering trade-offs.

Delivery & Duration: Twelve weeks of live online learning, with planned campus immersion.

Credentials: Professional certificate from IIIT Hyderabad.

Program Highlights: Agent planning, memory, MCP, A2A, multi-agent orchestration, evaluation, deployment, and AgentOps.

Outcomes: Through coding labs and assignments, participants practice designing and operating agentic systems reliably.

Why should you choose this course?

• It prioritizes architectural decisions over learning isolated frameworks.
• The shorter schedule suits engineers with basic coding skills.

5. Executive Post Graduate Programme in Applied AI and Agentic AI - IIIT Bangalore

IIIT Bangalore builds from Python, machine learning, and MLOps before introducing autonomous workflows. That foundation helps explain how agents fit into deployed AI systems.

Delivery & Duration: Seven months online, with live support and industry projects.

Credentials: Executive Post Graduate Certificate from IIIT Bangalore.

Program Highlights: Machine learning, LLM applications, RAG, multi-agent workflows, and production governance.

Tools & Hands-on Learning: LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen, with two industry capstones.

Outcomes: Participants practice developing ML applications and integrating autonomous agents into deployed systems.

Why should you choose this course?

• Its ML-to-agentic-AI progression helps professionals strengthen their technical foundation.
• The capstones cover both model deployment and agent-based automation.

Conclusion

Building one agent is usually easier than managing what follows: tool access, handoffs, exceptions, and monitoring. Engineers may need deeper architecture skills, cloud deployment practice, or stronger evaluation methods.

Look closely at prerequisites too. A coding-heavy course serves a different learner than one focused on architecture and team-level design.

Before choosing online Agentic AI Courses, check what you will build and how its reliability is tested. A working demo matters. Knowing why it fails matters more.

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