# Learning

Source: https://www.hyperagent.com/docs/learning

> For AI agents: the documentation index is at https://www.hyperagent.com/llms.txt and the complete documentation in one file at https://www.hyperagent.com/llms-full.txt. Any docs page is also served as Markdown by appending `.md` to its URL.

How agents get better at your work: they propose what they picked up from a run, you accept or dismiss it, and what you keep becomes standing knowledge.

An agent starts every run with what you gave it. **Learning** is how it gets better than that: it notices what a run teaches and offers to keep it, and you decide what sticks.

Each lesson is minor on its own. A month of them is the difference between an
assistant you brief every time and one that already knows the threshold, the
format, and who owns the process.

## How a lesson becomes knowledge [#how-a-lesson-becomes-knowledge]

The agent works, notices a correction you made or a method that worked, and files a proposal. Nothing changes what it knows until you accept it.



Where a proposal waits depends on how it was raised:

* **Background proposals** collect in the review queue, which you can reach from the thread's Learning panel, the Inbox, or the Learning page above.
* **Draft cards in the chat** appear when you ask an agent to reflect on a conversation, so you can edit exactly what would be saved.
* **Rubric proposals** go to the Rubrics page, kept separate because measuring quality is its own practice.

Each kind of learning is set per agent to Off, Suggested, or Auto-saved. Suggested is the default, so a new agent always asks first.

## What an agent can learn [#what-an-agent-can-learn]

Facts and preferences worth carrying forward: a threshold you set, how you
like a report opened. Saved as [memories](https://www.hyperagent.com/docs/concepts/memories).

A repeatable method, like pulling the weekly report from a connected app.
Saved as a [skill](https://www.hyperagent.com/docs/concepts/skills).

A refinement to the agent's instructions, added below what you wrote.

A definition of what good output looks like, so quality can be measured
rather than argued.

An agent can also propose a change to its own configuration, which waits in the same queue.

## Go deeper [#go-deeper]