What Types of Memory Do We Need to Consider When Designing an AI Agent

As AI agents move from experiments and demos into real business environments, one question becomes critical:

Why do many AI agents sound intelligent, yet fail to improve over time?

The answer is often memory.

Most AI agents today are designed to respond to prompts, not to remember outcomes. They can explain problems, but they struggle to learn from experience, repeat the same mistakes, and fail to build trust with users.

This blog explores the different types of memory that must be considered when designing an AI agent, using a simple, real-world example to show how each type of memory contributes to intelligent behavior.

Beyond mere linguistic fluency, artificial intelligence requires four distinct memory types to function effectively in real-world scenarios

Short-term memory manages the immediate conversation, while semantic memory provides a stable base of factual knowledge. To prevent repetitive mistakes and ensure reliability, agents also utilize episodic memory to learn from past experiences and procedural memory to follow specific workflows.

The Four Pillars of AI Memory

Memory Type	Primary Function	The "Without It" Factor
Short-term	Holds current conversation context and constraints.	The agent repeats questions and feels disorganized.
Semantic	Stores stable domain knowledge, rules, and concepts.	The agent hallucinates or gives shallow, inconsistent answers.
Episodic	Recalls past events, outcomes, and lessons learned.	The agent repeats mistakes and treats every issue as new.
Procedural	Encodes step-by-step workflows and decision trees.	The agent cannot be trusted to execute actions safely or consistently.

 Effective AI requires orchestration, not just a context window. We must engineer four distinct memory types that function simultaneously.

A Simple Use Case Example

Scenario :An organization uses an AI agent to help operations teams investigate failed business transactions.

A user asks the agent: “Why did transaction TX-45821 fail, and what should I do next?”

The agent must do more than explain an error.It must understand the situation, recall past experience, and suggest a safe next step.

Let’s see how each type of memory comes into play.

1.  Short-Term Memory (Working Memory)

What It Is: Short-term memory holds the current context of the interaction.

Key characteristics: Temporary, Limited in size & Cleared after the session ends.

Example in the Use Case

When the user asks about transaction TX-45821, the agent remembers during the session:

• The transaction ID
• The error message
• The time window
• Any clarifications the user provides

If the user later says: “This happened yesterday in the production system” ,The agent does not ask for the transaction ID again.

Why It Matters: Without short-term memory, the agent would:

• Lose track of which transaction is being discussed
• Ask the same questions repeatedly
• Feel disjointed and frustrating

Short-term memory makes the agent coherent and conversational.

2.  Semantic Memory (Knowledge Memory)

What It Is: Semantic memory stores general domain knowledge such as rules, definitions, and documentation.

Key characteristics: Stable and reusable, Shared across users & Changes slowly over time.

Example in the Use Case

The agent looks up the error code and responds:

“This error indicates that a required reference value was missing during validation.”

This explanation comes from stored knowledge, not from past experience with this specific transaction.

Why It Matters

Semantic memory allows the agent to:

• Explain what an error means
• Use consistent terminology
• Avoid guessing or hallucinating

Semantic memory answers:

“What does this error mean?”

3.  Episodic Memory (Experience Memory)

What It Is: Episodic memory records past events and their outcomes.

Key characteristics: Grows over time, Based on real events, Enables learning without retraining models

Example in the Use Case

The agent recalls:

• This exact error occurred four times last quarter
• 
  In three cases, the root cause was outdated reference data
• In one case, retrying without a fix caused duplicate records

Based on this, the agent says:

“We’ve seen this issue before. Retrying immediately is risky and has previously caused duplicates.”

Why It Matters

Episodic memory allows the agent to:

• Learn from history
• Avoid repeating known mistakes
• Provide confidence-based guidance

Without episodic memory, the agent would treat every failure as new.

4.  Procedural Memory (How-To Memory)

What It Is: Procedural memory defines how to respond in a safe, structured way.

Key characteristics: Deterministic, Improves with repetition, Encodes best practices and guardrails

Example in the Use Case

The agent follows a predefined workflow:

1. Verify reference data
2. Confirm whether a retry is safe
3. Recommend the next step

The agent responds:

“Update the reference data first, then reprocess the transaction. Do not retry immediately.”

Why It Matters

Procedural memory ensures the agent:

• Acts consistently
• Applies best practices
• Avoids unsafe actions

Procedural memory answers:

“What should I do next?”

How the Example Comes Together

Here’s how the agent reasons through the example:

1. Short-term memory keeps the investigation focused on TX-45821
2. Episodic memory recalls similar failures and their outcomes
3. Semantic memory explains what the error means
4. Procedural memory determines the safest next action

Instead of just responding, the agent reasons.

In Nutt shell: Why Memory Matters

This simple example shows why AI agent memory must be designed intentionally.

An AI agent that only uses:

• Short-term memory can hold a conversation
• Semantic memory can explain concepts

But an AI agent that also has:

• Episodic memory can learn from experience
• Procedural memory can act responsibly

That’s the difference between:

• An AI agent that talks
• And an AI agent that operates intelligently

Memory is not an add-on.It is the foundation of intelligent AI agents.

When designing your next AI agent, don’t start with prompts.Start with memory.