Agentic AI: Memory, RAG, and Multi-Agent Orchestration

Without memory, agents lose context; without knowledge, they become unreliable; and without collaboration, they struggle with complex problems. This course tackles each limitation through practical design and implementation.

Over three weeks, you’ll gain hands-on experience building systems that can retain context, retrieve domain knowledge, and collaborate across multiple specialised agents to solve complex tasks.

Design AI agent memory and Retrieval Augmented Generation (RAG) systems

You’ll learn how to build and implement multiple types of agent memory systems to ensure your AI agents behave consistently over time.

You’ll also design and build agentic Retrieval Augmented Generation (RAG) pipelines that allow agents to retrieve, reason over, and act on external domain-specific knowledge.

By combining memory and RAG, you’ll create AI agents that are not only reactive but context-aware and knowledge-enhanced.

Learn multi-agent orchestration and communication patterns

Single agents often struggle with complex, multi-step problems. This course introduces you to advanced multi-agent design patterns that enable scalable collaboration between AI systems.

You’ll explore supervisor, swarm, hierarchical, and debate-based architectures, learning when and why to use each pattern. You’ll also implement the Agent-to-Agent (A2A) protocol to enable standardised communication, task delegation, and coordination between agents.

Build production-grade multi-agent systems with shared memory

Finally, you’ll bring everything together in a project that demonstrates real-world AI system design.

You’ll build a fully functioning multi-agent system that integrates shared memory, agentic RAG pipelines, and A2A-based communication.