Day 03: Memory – The Agent's Brain
We got our Agents to think and remember
Hi!
My name is Hamza, and I am so excited to welcome you our new course. Joining me is Jaya, who is a ninja in Agents!
In these 7 sessions we will uncover all we can about Agents, what they are, how they work and the what’s really behind all this hype?
Read: Day 1
Read: Day 2
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Back to Day 3 of the course….
In Day 2 of the course How Agents Think - Types, Behaviors & Design Patterns, we explored how agents think - from reactive responders to planning strategists and reflective troubleshooters. But there's one critical ingredient those agents need to truly evolve: memory.
An agent that forgets everything it did five seconds ago can’t grow. It can’t personalize. It can’t even hold a conversation properly.
That’s where memory comes in. Just like humans, agents need memory to stay coherent, consistent, and context-aware across time. Memory transforms a stateless LLM into a stateful system, one that adapts to you, learns from you, and builds knowledge over time.
"Intelligence without memory is just noise. Real reasoning starts when past context is remembered."
In this module, we’ll explore the layers of memory, lightweight strategies like caching, and introduce the foundations of retrieval.
Why Memory Matters in Agents
Imagine a support agent that forgets your issue every time you write a message. Or a data analyst agent that re-asks what your database schema looks like after every query. Without memory, even the smartest LLM becomes... dumb.
Memory is what makes agent behavior adaptive. It allows the system to:
Remember past interactions and preferences
Track goals and sub-goals across time
Build knowledge that compounds
Think of memory as the key to making AI truly intelligent, because it helps them learn context, adapt, and provide personalized responses through short-term (working) memory and the three forms of long-term memory - procedural, semantic, and episodic.
In short: memory is what lets agents act like intelligent beings, not just parrots.
What Kinds of Memory Do Agents Use?
Modern agents use multiple layers of memory, each suited for different functions. Here’s how they break down — with simple analogies and real-world use cases:
Working Memory
Temporary, high-speed memory for the current task or prompt cycle
Holds recent context, tool results, or short chains of reasoning
Cleared at the end of an interaction or session
Analogy: Like what you hold in your mind while doing mental math
Use case: Storing tool results during a multi-turn plan in a ReAct agent
Procedural Memory
Encodes how to do things like how to use a specific API, run a tool, or follow a multi-step process
Helps agents perform tasks they've been trained or fine-tuned to execute
Analogy: Like muscle memory or following a recipe from habit
Use case: Remembering the steps to install packages or write SQL queries in a coding agent
Episodic Memory
Like a journal of the agent’s interactions
Tracks sequences of actions, user inputs, and system responses over time
Often used for debugging or improving task continuity
Analogy: Like your browser history or diary
Use case: Recalling past user sessions to personalize future responses
Semantic Memory
Stores general knowledge, facts, and conceptual understanding
Uses meaning-based representation to support flexible recall
Enables agents to retrieve and relate ideas even if phrased differently
Analogy: Like understanding the concept of gravity — you don’t remember when you learned it, but you know it
Use case: Recalling the definition of “net revenue” even if a user asks about “monthly earnings”
Caching: A Practical Subtype of Memory
Caching is often considered a practical subtype of memory — more lightweight and task-specific than deep memory structures. Sometimes agents just need to remember something for a few minutes or avoid repeating a call.
That’s where caching comes in. Think of it as the agent’s sticky note:
Stores outputs from recent tool calls
Remembers verified results
Reuses recent answers to avoid recomputation
It doesn’t require embeddings or deep reasoning — but it makes agents feel faster, snappier, and more responsive.
When to use caching:
When you need to reduce redundant tool/API calls
When agent response time needs to be improved
When storing complex memory is overkill for a task
It doesn’t require embeddings or deep reasoning but it makes agents feel faster, snappier, and more responsive, especially in repetitive or low-stakes tasks.
Agent Memory in Practice: How Mem0 Powers Recall
We have discussed how agents need different types of memory - from working memory to episodic and semantic layers. But how does that actually look in a real system? Let’s discuss an example using mem0.
mem0 is an open-source memory backend purpose-built for AI agents. Unlike traditional vector stores or databases, mem0 is designed to act like a brain — organizing and retrieving contextual memory such as user preferences, tool outputs, and reasoning traces across sessions.
It supports multiple types of memory (working, episodic, semantic) and plugs directly into LLM workflows, giving agents continuity, adaptability, and personalization — without needing to build a memory system from scratch.
This diagram shows how memory fits into the broader agent workflow, with a specific focus on mem0 as the memory layer.
What’s Happening Here
When the user makes a request, the agent doesn’t immediately respond.
Instead, it first checks mem0 memory - a layer that stores:
User preferences
Past interactions
Previously retrieved facts or summaries
This memory can include working memory (e.g., what tools the agent just used) and episodic memory (e.g., last conversation topics).
If relevant context exists, it’s passed into the agent’s prompt before a response is generated via OpenAI.
This gives the LLM the ability to respond with continuity. Without mem0 or a similar system, the agent would forget everything between sessions making it feel shallow and repetitive.
How Memory Actually Improves Agents
Let’s compare two agents.
Agent CapabilityAgent Without MemoryAgent With MemoryBehaviorRepeats the same mistakesLearns from past inputs and outcomesInput HandlingAsks for the same input repeatedlyRemembers past user inputs across turnsConsistencyGives inconsistent or conflicting answersMaintains coherent, goal-aligned responsesReasoningStarts from scratch every timeBuilds on prior reasoning and contextTask ExecutionHandles one-off requests without contextFollows through on multi-step goalsTool Use AwarenessDoesn’t track what it’s doneKnows which tools it installed or used previously
Example:
Imagine a data analyst agent. In round one, it learns your table schema. In round two, it remembers it. By round three, it’s proactively flagging anomalies you didn’t even ask about.
That’s not just helpful - that’s powerful.
Next Up: RAG (Retrieval-Augmented Generation) — the engine that lets agents access real-world knowledge beyond their training data. You’ll see how memory + retrieval becomes a superpower, and how it powers truly vertical, intelligent agents. You’ll also learn how tools like embeddings and vector databases let agents understand and retrieve meaning at scale.
READY TO GO DEEPER?
Don’t forget to check out my AI Agents for Enterprise course on Maven if you are interested to be a part of something bigger.
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