A guided learning and building journey that helps experienced professionals move from AI confusion to practical confidence, real use cases and stronger career opportunities.
Python, GenAI literacy, Prompt Engineering, Advanced RAG with hybrid search and re-ranking.
ReAct agents, Tool Use, Reflection loops, LangGraph, Agentic RAG with HITL, multi-agent orchestration, MCP and A2A protocols.
Guardrails, prompt injection defence, and advanced agent memory.
LLM-as-a-Judge evaluations with Ragas and LangSmith, Docker, AWS App Runner, Azure App Service, GitHub Actions CI/CD.
Agentic RAG, escalation logic
Planner-Executor, citation enforcement
Vector scoring, bias detection
Schema-based contract extraction
Data-grounded portfolio intelligence
Anomaly detection and synthesis
Multi-agent debate and convergence
Explainable AI recommendations
Hypothesis loops and risk gating
Five progressive tiers — from Python fundamentals to production-ready Agentic AI systems. Built for both tracks simultaneously.
Python, Google Colab, LLM APIs, model parameters, prompt and context engineering, OpenAI SDK.
Documents, chunking, embeddings, vector databases, semantic search, retrieval and grounded RAG systems.
LangChain, tool calling, ReAct, LangGraph, agent workflows, memory and multi-agent patterns.
Planning, reflection, orchestrator/ supervisor patterns, workflows, governance and MCP.
Evaluations, guardrails, observability, LangSmith, deployment thinking and RAG optimisation.
The two cycles run independently and may be at different stages when you join. Start with the current live session, use recordings to catch up, and continue into the next repeating cycle.
Learn → Build → Repeat → Master. Keep returning to the cycles as AI evolves and turn experience into practical capability.
Join 1,800+ engineers learning Agentic AI from someone who ships it in production — not just teaches it from slides.
The complete Agentic AI engineering foundation.
Python, GenAI literacy, Prompt Engineering, Advanced RAG with hybrid search and re-ranking.
ReAct agents, Tool Use, Reflection loops, LangGraph, Agentic RAG with HITL, multi-agent orchestration, MCP and A2A protocols.
Guardrails, prompt injection defence, and advanced agent memory.
LLM-as-a-Judge evaluations with Ragas and LangSmith, Docker, AWS App Runner, Azure App Service, GitHub Actions CI/CD.
Agentic RAG, escalation logic
Planner-Executor, citation enforcement
Vector scoring, bias detection
Schema-based contract extraction
Data-grounded portfolio intelligence
Anomaly detection and synthesis
Multi-agent debate and convergence
Explainable AI recommendations
Hypothesis loops and risk gating
Live Core Classes
9:00 – 10:30 PM ISTInner Circle Call
9:00 – 10:30 PM ISTLive Agentathon
4:00 – 8:00 PM IST