Agentic AI Courses in India Compared (2026)

Short answer: in India, "agentic AI course" can mean anything from a one-hour recorded primer to a five-month university certificate. Decide first what you need:

• a quick orientation before you commit,
• a short, live, build-focused course, or
• a longer program with a university name on the certificate.

Then compare live hours, what you actually build, and who issues the certificate.

How we compared

We used only facts each provider publishes about its own course:

• Format: live classes, recorded videos, or a mix
• Duration and time: total length and, where stated, live hours or weekly effort
• Agent topics named in the syllabus (tool calling, multi-agent orchestration, MCP, evaluation)
• Certificate: who issues it

We didn't rank the courses, and we left out reviews, ratings, placement numbers and salary claims, because we can't verify them consistently. Fees change often, so we've left them out; each provider's page shows its current fee.

Side-by-side comparison

Who each option is for

• GUVI's self-paced Agentic AI course suits someone who wants a short, low-commitment introduction before deciding whether to go deeper. It's an orientation, not a build-heavy course.
• GUVI's AI & ML Programme suits beginners and career switchers who want the whole AI/ML journey, with agents as one part. It offers several Indian languages (the page lists Tamil, Hindi, Telugu and Kannada alongside English). If you already code and only want agents, much of it will be review.
• Simplilearn's Applied Agentic AI suits professionals whose company runs on Microsoft and Azure and who value Microsoft-branded credentials.
• Analytics Vidhya's Pioneer Program suits self-directed learners who want a large content library at their own pace. Its 6-week live course suits people who want a short, fully live format.
• IIT Bombay's and Johns Hopkins' certificates suit people for whom a university name on the certificate matters most and who meet the eligibility rules.
• Datamarcos's Applied Agentic AI Certification suits working developers, data professionals, testers and technical leads who want eight weeks of live, mentor-led building. No prior AI experience is needed, but you should be ready to learn basic Python. Every module ends in a working project, and the course finishes with one integrated multi-agent application. It issues a Datamarcos certificate, not a university one. If the university name matters most to you, choose a university program instead.

How to choose: 7 questions to ask any provider

1. How many hours are live, and how many are hands-on labs? Recorded and live hours aren't the same thing.
2. What will I have built by the end? Ask for a sample capstone brief, not just a topic list.
3. Does the syllabus cover evaluation and safety? Agents that call tools need guardrails, approvals and tests. The OWASP Top 10 for LLM Applications is a good checklist of the risks.
4. Which frameworks, and why? LangGraph, CrewAI, AutoGen, MCP and vendor SDKs all have trade-offs. A good course explains when to use each, and when a simple workflow beats an agent (Anthropic's Building effective agents makes this point well).
5. Who teaches, and can I see their work? Look for named instructors with public experience.
6. What are the prerequisites? A beginner course and a course for engineers aren't interchangeable.
7. What exactly does the certificate say, and who issues it?

Skills that agentic AI roles ask for

Without quoting any single job post, these skills recur in public agentic AI and AI-engineering job descriptions: Python, calling LLM APIs with structured outputs, retrieval (RAG), tool and function calling, orchestration frameworks such as LangGraph, connecting tools through the Model Context Protocol, evaluation and tracing, and deploying behind an API with access controls. Use this list to check any syllabus.

Where Datamarcos fits

We built the Applied Agentic AI Certification for working professionals who want a short, live path from LLM basics to a working, evaluated agent system. The 8 weeks run in this order:

1. LLM foundations and system thinking
2. Prompt engineering and structured output
3. Production RAG systems
4. Building AI agents
5. Multi-agent systems and orchestration
6. Model Context Protocol (MCP) and enterprise integration
7. Voice, multimodal agents and automation
8. Evaluation, reliability and capstone

Sessions are live and mentor-led with Deepesh Mishra. We don't promise placement, salary or a hike.

Want more depth in security, LLMOps and production deployment? See the 18-week Generative AI & Agentic AI Engineer program. If you work directly with clients, the FDE courses comparison covers forward deployed engineering programs. More guides are in the Datamarcos guides hub.