Agentic AI Bootcamp: Build Powerful GenAI Apps & Multimodal AI Agents

Architect an autonomous AI agent that interacts with tools, APIs, and users end-to-end.

Use frameworks like LangChain, LangGraph & MCP to implement agent behaviour.

Deploy the agent in a cloud environment (e.g., Docker + AWS ECS) so it’s live and operational.

Deep dive into LLMs and prompt engineering to power your agents.

Build chains, tool invocations, memory flows and agent orchestration via LangChain and LangGraph.

Explore Agentic RAG (Retrieval-Augmented Generation) and multi-agent patterns (agent-to-agent communication) for higher-level workflows.

Ingest data using loaders, splitters and vector databases (for example, via Pinecone, Weaviate or Chroma).

Build retrieval pipelines to feed relevant context into LLM prompts, thereby improving accuracy and relevance.

Integrate the RAG system into an agent so that it can reason over your knowledge base and take action accordingly.

Use FastAPI as the backend API for your agent, and Streamlit as the front-end interface to interact with it.

Connect the UI to your deployed agent’s backend, enabling real users to interact with your system.

Implement deployment best practices, including containerisation, API endpoints, security, and scaling considerations.

Define a real-world use case (founder/entrepreneur/solopreneur angle), design the MVP (minimum viable product) for that case.

Build the MVP during the course and deploy it with a custom domain or production target.

Present your agent project as a capstone/demo day, ready for LinkedIn, portfolio, clients or investors.

Understand high-level architecture of agent-based systems, model context protocol (MCP), and multi-agent ecosystems.

Gain confidence to lead AI initiatives, pitch agent-based solutions, and consult or work as an agent-builder.