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Agentic AI: Building Autonomous Intelligence

Build agentic AI systems that take action through iterative, multi-step workflows.

Course Fee : 39,999 (Inclusive of 18% GST)

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Course 

Step into the next frontier of Artificial Intelligence — where models don’t just respond, they reason, plan, and act.
This advanced course demystifies the evolution from traditional AI systems to agentic intelligence — autonomous entities capable of self-directed learning, decision-making, and collaboration.

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Through an in-depth exploration of Large Language Models, Transformer internals, Embeddings, Vector Search, and the principles of Agent Architecture, you’ll gain a full-spectrum understanding of how modern AI systems think, remember, and adapt.

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From multi-agent coordination and planning to Retrieval-Augmented Generation (RAG) and Reinforcement Learning with Human Feedback (RLHF), this program equips you with the frameworks and hands-on skills to build intelligent, memory-augmented, goal-oriented AI agents.

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Designed and curated by industry experts, this is your gateway to mastering the intelligence layer that’s shaping the future of autonomous systems, enterprise AI, and next-generation applications.

Course Highlights

  • One 30-mins 1:1 Mentorship Session

  • One mock interview by an industry expert

  • Lifetime access to the cohort recordings

  • Lifetime access to the Network and Community

  • Open forums and interaction with the cohort

  • Doubt resolution during and post live sessions

  • Language of communication will be strictly English

Course Details

  • Course Starts - 20th Dec 2025

  • Course ends - 25th Apr 2026

  • Weekly Schedule - Sat & Sun 7:00 pm to 9:00 pm (IST)

  • Total Classes - 35 Classes of 2 hours each.

  • Mode - online (Live + Recordings)

  • Deconstruct the inner workings of Large Language Models — from Transformer attention to tokenization, context windows, and embeddings.

  • Build and deploy vector-based retrieval systems using FAISS and OpenAI embeddings for semantic similarity and knowledge search.

  • Understand and design Agentic AI architectures — including reactive, deliberative, and hybrid agents, and their communication frameworks.

  • Implement goal-oriented planning and multi-agent collaboration, leveraging reinforcement and adaptive decision-making loops.

  • Design memory-augmented agents with short- and long-term recall, using retrieval-augmented memory (RAM) and vector stores.

  • Integrate Reinforcement Learning and RLHF techniques to refine agent behavior through rewards and human feedback.

  • Develop and customize RAG pipelines for Q&A, summarization, and dynamic context retrieval.

  • Apply governance, safety, and ethical considerations in the design of autonomous AI systems.

  • Build your own agentic prototypes capable of reasoning, learning, and acting autonomously in real-world use cases.

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