Agentic AI is the next frontier in artificial intelligence. It is where models move beyond passive responses to become autonomous, goal-driven entities capable of reasoning, planning, and acting. This course explores the design and deployment of intelligent agents powered by large language models (LLMs), equipping learners to construct AI systems that can operate independently, collaborate with other agents, and assist users in complex tasks.
This course offers a practical and future-focused learning experience. You will work with leading frameworks like LangChain and Model Context Protocol, experiment with advanced prompting techniques, and build a personalized agentic AI assistant capable of task execution.
This course is designed for developers, data scientists, and AI practitioners looking to understand how autonomous agents are reshaping industries from customer service and productivity tools to research and decision support. Skills gained in this course are applicable to roles in AI product development, conversational AI, intelligent automation, and enterprise AI solutions. You will be equipped to design agents that enhance productivity, customer experience, and decision-making.
What you will learn
- Core concepts and frameworks for building and using LLM-powered agents
- Using context engineering for agent performance and reliability
- Function calling to enable the agent to execute tasks
- Designing autonomous and multi-agent systems using tools like AutoGen
- Understanding agent memory types using vector stores, episodic memory, and RAG and their role in agent behaviour
- Strategies for memory and knowledge compression
Skills you’ll gain
- Building and deploying agentic AI systems using LangChain and Model Context Protocol
- Designing and using intelligent assistants with pre-built agentic frameworks
- Engineering prompts and contexts to guide agent behaviour
- Integrating multiple agents and agent types for collaborative tasks
- Understanding how agents collaborate and manage knowledge over time
Course format
- Prerequisites:
- Completion of Machine Learning and Large Language Models courses or equivalent experience with LLMs is strongly recommended
- Familiarity with Python is required
- This course is suitable for learners with experience using LLMs and basic machine learning concepts
- Project: Build a personalized agentic AI assistant capable of autonomous task execution using popular frameworks
- Delivery: Hybrid delivery, instructor-led live sessions with hands-on assignments