I built an AI agent using Python and an Anthropic model.

The way AI agents work is actually quite simple.

The agent receives user input and stores it in a conversation array (or message history). Then, it sends this conversation history to the AI model. The AI model processes the input and returns a response, and the agent saves that response back into the conversation array.

This process continues for every interaction.

If necassary AI corrects command first

To ensure the agent fully understands the user's command, the agent sends the entire conversation history along with the new user message. The AI model then generates a response based on the full context.

This happens because AI models do not have persistent memory by default. Instead, they rely on something called a context window, which acts as the model’s working memory. Each time you send a request, the agent must include previous messages so the model understands the conversation.

Because the context window is limited, the agent often stores conversation history externally (for example, in memory, a database, or files) and sends relevant parts back to the model when needed.

• User sends message
• Agent saves message to conversation history
• Agent sends full conversation to AI model
• If necessary AI corrects command first
• AI model generates response
• Agent saves response to conversation history
• Repeat for next request

This is why AI agents usually store both user messages and AI responses — because the model itself does not remember previous interactions unless they are included again in the next request.