Tutorial: Building Agents That Learn from Experience
Step-by-Step Tutorial
You have a folder full of documents. An email inbox is overflowing. Repetitive tasks you do every week. These are perfect for AI agents. But standard agents? They get stuck. They make the same mistakes. They don’t learn.
What if you could build agents that improve every time they work?
This tutorial shows you how to build a learning agent right now, using patterns that align with what’s coming in Claude Dreaming. Your agent will review its own work, learn from mistakes, and apply those lessons to future tasks.
No waiting for research preview. No special features required. Just structured learning.
Part 1: Understanding the Learning Agent Loop
Before you build anything, understand what’s happening inside.
A normal agent workflow looks like this:
Session 1 → Task Complete → Session Ends
The agent does work. The session ends. It forgets everything except the final result.
A learning agent workflow looks like this:
Session 1 → Task Complete → Review & Feedback → Write Memory Notes → Session 2 → Better Performance
The review phase is key. The agent (or you) examines what happened, extracts lessons, and stores them. In the next session, the agent has access to those lessons.
