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You train an agent two ways: by reviewing its work before it acts, and by sending it away to study something too large for one conversation. Both write back to the same memory, so what it learns sticks.

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Reviewing before it acts

Training mode lets you check an agent’s output before it goes out. Use it while you build trust in a new agent, dial in a workflow, or work on something where a mistake would be costly. In training mode, the agent presents its work as artifacts for review instead of sending or executing directly. You see exactly what it intends to do. You approve or revise it. Only then does it go out.

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When to use it

A new agent

Watch how it handles real tasks before you let it act unsupervised.

A new workflow

Validate a fresh skill or process end to end before trusting it.

High-stakes output

Customer-facing replies, anything irreversible, anything you want a human gate on.

Tuning the persona

See how persona and instruction changes land before they reach anyone.

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The flow

Turn it on by asking, in plain language:
1

Turn on training mode

Ask the agent to enter training mode in your conversation. It applies to that agent until you turn it back off.
2

The agent drafts

It does the work and presents the result as an artifact instead of acting.
3

You review

Approve it, edit it, or send it back with feedback.
4

Graduate

Once the agent consistently gets it right, turn training mode off and let it run on its own.
Pair training mode with the feed: review what the agent produced in the moment, then audit the trail afterward.

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Sending it away to study

Some things take real study: your communication style, the shape of a project, your key relationships, a body of reference material. A normal conversation is too small a window. In a deep learn session the agent studies in the background and comes back having absorbed it.

Learn your style

How you write and communicate, so its output sounds like you.

Learn a project

The context, decisions, and players behind a body of work.

Learn your world

Key relationships, accounts, and the things you care about most.

Learn a domain

Reference material and documentation it should master.

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How a session starts

A session begins one of two ways:
  • You ask. Tell the agent to get up to speed on something: “spend some time learning our brand voice,” “get to know my top accounts.”
  • The agent proposes one. When a task needs far more context than a chat holds, the agent suggests a session.
Either way, you confirm before it runs. A long session never starts without your go-ahead. Once confirmed, the session runs in the background. You do not have to stay in the conversation. The agent works through the material on its own and reports back with a summary of what it learned.

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Scoping a session

A good session starts with a clear goal. Before it begins, the agent works out with you:
  • What to focus on: the specific area to study.
  • What “good” looks like: concrete outcomes (“you should know my top five accounts,” “match my email tone”).
  • What to study: the people, projects, channels, or docs that matter.
  • What it should handle afterward: the tasks the learning should enable.
With the scope set, the agent runs the session and writes what it learns to its memory. The new context applies to every future conversation.
Deep learn pays off most for an agent you will rely on long-term, not a one-off task.

Where the learning lives

Everything training and study produce is written to durable memory.

Shape behavior as you review

Fold what you learn in training back into the agent’s persona.