Select your learning path
This comprehensive curriculum guides you from foundational GenAI concepts to building and deploying robust, Agentic AI systems in an enterprise environment.
This course is designed for anyone with a basic computer background who wants to understand and work with Agentic AI — no prior AI experience required.
The course is hands-on with live classes and certification exams, designed to take you from beginner to intermediate/advanced in 6–8 weeks. Depending on your familiarity with the concepts, some topics may require additional effort — but the structured format and live sessions are here to support you every step of the way.
Ability to design and build enterprise-ready Agentic AI systems.
Confidence to take an idea from whiteboard to prototype to production.
Practical experience you can discuss in interviews and on the job.
A capstone that proves you can do more than just prompt.
Understand how LLMs function (tokens, context, attention, limitations), Prompt Engineering, differentiate prompting from reasoning and retrieval, and identify when GenAI is not the optimal solution.
Explore API-first versus agent-first design, strategies for session management, memory, and context, plus effective tool calling and function orchestration.
Learn fine-tuning fundamentals and use cases, grasp Parameter-Efficient Fine-Tuning (PEFT) techniques, and dive into LoRA and QLoRA for adapting LLMs efficiently.
Explore techniques to reduce model size and computational cost for efficient deployment, including quantization methods and knowledge distillation strategies.
Discover what SLMs are, when to use them versus larger LLMs, and their benefits in terms of cost, latency, and deployment flexibility.
Master embeddings, chunking, and indexing. Learn about vector databases, common RAG failure modes, and advanced hybrid search and re-ranking techniques.
Define what makes a system "agentic," delve into planning, reasoning, tool use, and execution loops, and compare single-agent with multi-agent architectures.
Essential Python concepts for AI development, coding standards and best practices, working with libraries and frameworks, and writing clean, maintainable code.
Learn to design supervisor/worker agent patterns, task decomposition, and delegation. Address inter-agent communication, conflict resolution, and autonomy tradeoffs.
Compare LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Semantic Kernel. Understand when to use each framework and key tool selection criteria.
Develop strategies for prompt and agent evaluation, trace agent decisions, and utilize quality metrics beyond superficial assessments to detect silent failures.
Address data privacy, PII handling, prompt injection, and tool abuse mitigation. Implement role-based access control and ensure auditability for compliance.
Manage token economics, implement caching and batching, optimize latency for real-time systems, and master model selection and routing strategies.
Set up environment separation, version prompts, agents, and tools. Implement rollbacks, feature flags, and monitor behavioral drift over time.
Design, build, and deploy a real-world agentic system from architecture to production-grade, integrating security, cost, and governance.
Gain insights into current enterprise practices, common mistakes to avoid with Agentic AI, and critical considerations for human-in-the-loop processes.
Group Sessions & Personalized feedback to highlight your Agentic AI skills and projects effectively.
Mock interviews and guidance on discussing Agentic systems, architecture decisions, and technical challenges.
Regular assessments progressing from foundational concepts to complex real-life scenarios.
Contact us to learn more about enrollment and upcoming cohorts