Agentic Ai & GenAi with cloud 3.0

Understand what truly separates agents from LLMs and chatbots. Learn the Perception-Memory-Reasoning-Action loop, the full agentic spectrum from simple tool-calling to autonomous systems, and the architectural patterns that underpin every modern framework: ReAct, Supervisor, Swarm, Pipeline, and Mesh. Build the judgment to choose the right design for any real-world use case.

Go hands-on with every framework shaping the field: LangChain, LangGraph, OpenAI Agents SDK, Google ADK, AWS Strands, CrewAI, LlamaIndex, Claude Code, AutoGen, n8n, and LangFlow. Learn the strengths, tradeoffs, and ideal use cases for each, so you can confidently pick the right tool for the job rather than forcing one framework everywhere.

Master the two open standards defining the agentic era. Build custom MCP (Model Context Protocol) servers for tool integration, wrap existing agents as A2A (Agent-to-Agent) compliant services, and design cross-framework workflows where ADK, CrewAI, and LangGraph agents communicate seamlessly across vendors and clouds.

Build retrieval systems that go far beyond basic vector search. Master hybrid search, HyDE, re-ranking, contextual compression, and the full family of agentic variants: Adaptive RAG, Corrective RAG (CRAG), Self-RAG, and GraphRAG with Microsoft GraphRAG and LightRAG. Evaluate quality rigorously with RAGAS and DeepEval.

Move beyond prompt engineering into the discipline that defines production agent performance. Learn structured system prompts (CLAUDE.md, AGENTS.md patterns), token budgeting, dynamic context assembly, and the full memory stack including short-term, long-term, episodic, semantic, and procedural memory implemented in LangGraph and LlamaIndex.

Design and build production multi-agent systems using Supervisor, Swarm, Pipeline, and Hierarchical patterns. Implement handoffs, shared state, parallel execution, checkpointing, and human-in-the-loop gates with LangGraph, CrewAI Flows, OpenAI Agents SDK, and AWS Strands swarms.

Ship agents enterprises can trust. Learn the agentic attack surface including prompt injection, tool misuse, and data exfiltration, then implement defences using NeMo Guardrails, Guardrails AI, input/output tripwires, and sandboxed execution. Master trajectory-based evaluation, LLM-as-judge, and automated evaluation pipelines in CI/CD.

The Full Observability Lifecycle AgentOps is the emerging discipline for production agents, just as DevOps was for software. Get deep hands-on experience with AgentOps SDK, LangSmith, Opik, and Langfuse for session replay, tool call tracing, cost tracking, LLM-as-judge evaluation, and anomaly detection at scale.

Learn how real agentic systems are shipped. Containerise with optimised Dockerfiles, build CI/CD pipelines with GitHub Actions, and serve agents using FastAPI (async, streaming, WebSockets) and BentoML. Deploy across ECS Fargate, Cloud Run, Vertex AI Agent Engine, Azure Container Apps, AWS Lambda, and AgentCore Runtime.

Build four portfolio-ready systems on four different stacks: a multi-agent business automation platform on AWS with Strands and Bedrock, a RAG and MCP-powered enterprise knowledge assistant on GCP with Google ADK and Vertex AI, a multi-agent customer intelligence platform on Azure with CrewAI and Azure OpenAI, and a fine-tuned fully local agent stack with LangGraph, Ollama, and Chroma.