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

Understand how to move past limitations to build reliable AI systems

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.

This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.

Learners should understand LLMs, prompt engineering, Python, APIs, basic agent workflows, and tool integration. Familiarity with RAG, vector databases, or agent frameworks will be helpful.

Learners need a computer (Windows, macOS, or Linux) with at least 8 GB RAM and a stable internet connection. Python 3.10 or later should be installed, along with a code editor such as VS Code. No paid software or specialised hardware is required.

This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.

Learners should understand LLMs, prompt engineering, Python, APIs, basic agent workflows, and tool integration. Familiarity with RAG, vector databases, or agent frameworks will be helpful.

• Explain how agent memory supports context-aware AI workflows.
• Apply vector-store memory and adaptive query routing for smarter retrieval.
• Design structured memory and context strategies for reliable agent responses.
• Create agentic RAG pipelines with retrieval confidence and grounded outputs.
• Compare multi-agent coordination patterns, supervisor workflows, and A2A communication.
• Evaluate human oversight, shared memory, conflict resolution, and production readiness.