Knowledge Bases & Memory for Agentic AI

  • IntermediateLevel

  • 15253+Students Enrolled

  • 45 MinsDuration

  • 4.5Average Rating

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About this Course

  • Learn how to structure and store data in vector databases to enable intelligent information retrieval in AI agents.
  • Understand the role of knowledge bases and memory in Retrieval-Augmented Generation (RAG) and agentic workflows.
  • Build hands-on projects that show how agents use context, memory, and external knowledge to perform complex tasks.

Course Benefits

  • Understand how vector databases act as long term memory for agentic AI systems.
  • Learn practical agentic AI memory techniques to use real business data effectively.
  • Gain confidence in designing AI agent memory architecture for reliable RAG based agents.
  • Build a solid foundation before moving to advanced agentic AI tutorials and complex projects.

Learning Outcomes

Build Smart Memory

Learn how agents store and retrieve data using vector memory

Master RAG Basics

TUnderstand how Retrieval-Augmented Generation powers agents

Create Knowledge Hubs

Build structured, searchable knowledge bases from raw data

Who Should Enroll

  • Professionals: Gain hands-on knowledge to build AI agents that leverage your organization's data
  • Aspiring Students: Explore how cutting-edge generative AI tools like RAG and AI agents work.
  • Data professionals interested in applying knowledge bases to enhance retrieval and reasoning in AI systems.

Course Curriculum

This agentic AI memory course introduces RAG, vector databases, and knowledge bases, then shows how they connect to agentic AI. Learn how agents use vector stores as memory, how ai agent memory architecture is designed, and how to integrate these ide

tools

Learn how LLM usage has evolved from simple prompts to purpose driven systems that rely on external knowledge. Understand how vector databases power Retrieval Augmented Generation, how retrieval works internally, and how RAG becomes the foundation for autonomous agentic systems.

  1. 1. From Prompts to Purpose Evolving LLM Usage

  2. 2. Powering RAG with Vector Databases

  3. 3. Powering RAG with Vector Databases

  4. 4. RAG to Agents Building Autonomous AI Systems

See multi tool agents in action solving real scenarios, learn how open source frameworks like CrewAI can orchestrate multiple agents, and design collaborative multi agent systems that share memory, tools, and context across tasks.

  1. 1. Multi-Tool Agents in Action Solving Real-World Scenarios

  2. 2. Open-Source Agent Building with CrewAI

  3. 3. Designing Collaborative Multi-Agent Systems

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Tuana Çelik

DevRel & AI Engineering

Tuana Çelik is a Developer Relations Engineer at Weaviate. In her role, she educates the open-source community about AI tools, the latest methods, and workflows.

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JP Hwang

Developer Educator

JP Hwang is passionate about empowering others to build with AI. He brings a combination of technical expertise, empathy, and bad jokes to all his endeavors to make learning fun and empowering for both sides.

Get this Course Now

With this course you’ll get

  • 45 Mins

    Duration

  • Tuana Çelik, JP Hwang

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

The course introduces vector databases such as Weaviate and their role in powering RAG. Concepts are shown in the context of agentic AI workflows, where LLMs, tools, and knowledge bases work together. The focus is on how these components fit into the broader agentic AI ecosystem, rather than only on one specific vendor platform.

A vector database stores embeddings, which are numeric representations of text, images, or other data. This structure enables fast similarity search, which is essential for unsupervised semantic retrieval. In an agentic AI memory course, vector databases are critical because they act as long term memory for agents, powering semantic search in RAG pipelines and persistent memory in AI agents.

The course clearly distinguishes short term memory, which is session based and often tied to recent messages or context windows, from long term memory, which is stored in persistent vector databases or knowledge bases. This distinction is important for understanding memory management in AI agents and designing ai agent memory architecture that remains scalable and reliable over many interactions.

A certificate is provided once all modules are completed. This validates that the learner has finished an agentic AI memory course, understands how knowledge bases and vector databases support RAG, and has seen practical examples of agentic AI memory techniques that can be used in real projects.

Memory management in AI agents is explained through practical scenarios where agents fetch context from vector databases, reuse past interactions, and ground responses in knowledge bases. Examples show how short term context and long term vector memory work together in an agentic AI tutorial setting.

The agentic AI memory course explains how Retrieval Augmented Generation relies on vector databases and knowledge bases as memory layers. RAG is presented as the bridge between raw data and intelligent agents, showing how retrieval, ranking, and generation fit into a complete memory aware workflow.

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