Simple way to explain Memory in AI Agents.
Win a NVIDIA RTX 4080 SUPER GPU!
👋 I am Aurimas. I write the SwirlAI Newsletter with the goal of presenting complicated Data related concepts in a simple and easy-to-digest way. My mission is to help You UpSkill and keep You updated on the latest news in GenAI, MLOps, Data Engineering, Machine Learning and overall Data space.
One of the most fulfilling aspects of being part of a community is being able to partner with various organisations with the goal of giving away exciting items.
This time I am partnering with NVIDIA to give away a NVIDIA RTX 4080 SUPER GPU to one of the SwirlAI community members.
In order to participate in the giveaway, you need to:
Register to the GTC 2025 via the following link:
DM me to get further instructions.
The conference will be running throughout March 17th - 21st. It will be held in a hybrid format: San Jose, CA and virtually.
Virtual attendance is FREE - so there’s no reason to miss out!
The lineup and the list of topics is really extensive.
Personally I am most excited about Robotics, IoT and AI Agents on the edge. These are the four sessions that I am adding to my attendance list:
An Introduction to NVIDIA Isaac GR00T for Humanoid Developers - excited to learn more about the platform for developing humanoid robots that NVIDIA is building.
A New Era of Generalist Robotics: The Rise of Humanoids - excited to get the latest insights into how foundation models are used in humanoid robotics and what challenges are still to be overcome.
Building Edge and Robotics Applications with Generative AI on NVIDIA Jetson - Sharpen my knowledge on latest developments in Agents on the edge and get inspiration for my own projects.
AI Meets Robotics: European Startup Showcase - looking forward to hear more about where we stand with Robotics advancements in Europe.
Be sure to subscribe to the Newsletter If you don’t want to miss any future giveaways or any other perks of being part of the community!
Refresher: Simple explanation of Memory in Agentic Systems.
In general, the memory for an agent is something that we provide via context in the prompt passed to LLM that helps the agent to better plan and react given past interactions or data not immediately available.
It is useful to group the memory into four types:
Episodic - This type of memory contains past interactions and actions performed by the agent. After an action is taken, the application controlling the agent would store the action in some kind of persistent storage so that it can be retrieved later if needed. A good example would be using a vector Database to store semantic meaning of the interactions.
Semantic - Any external information that is available to the agent and any knowledge the agent should have about itself. You can think of this as a context similar to one used in RAG applications. It can be internal knowledge only available to the agent or a grounding context to isolate part of the internet scale data for more accurate answers.
Procedural - This is systemic information like the structure of the System Prompt, available tools, guardrails etc. It will usually be stored in Git, Prompt and Tool Registries.
Occasionally, the agent application would pull information from long-term memory and store it locally if it is needed for the task at hand.
All of the information pulled together from the long-term or stored in local memory is called short-term or working memory. Compiling all of it into a prompt will produce the prompt to be passed to the LLM and it will provide further actions to be taken by the system.
We usually label 1. - 3. as Long-Term memory and 5. as Short-Term memory.
And that is it! The rest is all about how you architect the topology of your Agentic Systems.
Stay safe and hope to see you in the next Newsletter episode. We will continue building Agentic Systems from scratch!
Great Explanation. By the way what tool do you use to make these workflow animations. These are looking great.