Memory vs Context vs Skills in AI Agents

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Memory is information retained from past interactions. 

Context is information inside the model’s current context window. 

Let’s simplify it with an example.

Memory vs Context in AI Agents

Suppose you have a file called: company_policy.md

Scenario 1: The file is sitting on disk

The AI agent hasn’t read it.

Is it memory?

No.

Is it context?

No.

It’s simply knowledge (or a knowledge source). 

Scenario 2: The agent retrieves the file

The agent searches company_policy.md and injects the relevant sections into the prompt.

Now it becomes context.

The model can now use that information to answer your question.

Scenario 3: The agent creates the file

Now imagine the agent maintains information about the user:

user_profile.md

– Prefers Python

– Likes concise explanations

– Works in AI Engineering

After every conversation, it updates this file.

This file is memory.

Because it stores information the agent has learned and will reuse in future interactions.

Now, if relevant information from this file is included with the prompt, that memory information becomes the context.

Ok, then what is Skill?

Skills are reusable capabilities or workflows that enable Agents to perform specific tasks.

The idea is to move the predictable part of the AI Agent reasoning into a predefined workflow 

Let’s understand this with a research-agent example:

Without a Skill

User asks:

“Research Company X’s AI strategy.”

The agent reasons:

What should I do?

• Go to Company X website

• Find relevant pages

• Read the pages

• Extract findings

• Cross-check

The LLM is deciding the entire process dynamically.

Now this means:

• More LLM calls
• More tokens and cost
• Potentially different behaviour each time

With a Research Skill

You define:

Research Skill

1. Access specified website

2. Search for relevant information

3. Read relevant pages

4. Extract evidence

5. Cross-check findings

6. Produce a structured summary

Now the agent can essentially say:

“This is a research task. I’ll invoke the Research Skill.”

And the predefined workflow handles the predictable parts.

So, now the process is more deterministic and cost-effective

We can define multiple skills.

But how does the agent know which Skill to use?

While writing a skill, a name and short description are defined.

The agent is given these descriptions as part of its context.

When the agent determines that a skill is appropriate, it can load the full skill instructions and execute the workflow. 

For example:

Research Skill: Research a company using its website and other specified sources and produce a structured report.

When the agent determines that this skill is appropriate, it can load the full skill instructions into its context and execute the workflow.

Task Memory vs Skill

There is one more distinction worth understanding.

Suppose your research agent researches companies.

After researching 20 companies, it stores:

“For Company X, the annual report was more useful than the website. The AI strategy was mentioned in the investor presentation.”

That’s task memory: information/experience from a specific task.

So:

Task Memory → What did I learn from doing the task?

Skill → How should I perform the task?

And task memory can eventually improve the skill.

I hope you will not use these terms – Memory, Context, and Skills interchangeably now. 

Understanding this distinction makes concepts like conversational memory, RAG, and agent architectures much easier to reason about.

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