diff --git a/docs/docs.json b/docs/docs.json
index 3944522cf..42d268dab 100644
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -2342,6 +2342,7 @@
"en/enterprise/guides/deploy-to-amp",
"en/enterprise/guides/private-package-registry",
"en/enterprise/guides/kickoff-crew",
+ "en/enterprise/guides/training-crews",
"en/enterprise/guides/update-crew",
"en/enterprise/guides/enable-crew-studio",
"en/enterprise/guides/capture_telemetry_logs",
@@ -2812,6 +2813,7 @@
"en/enterprise/guides/deploy-to-amp",
"en/enterprise/guides/private-package-registry",
"en/enterprise/guides/kickoff-crew",
+ "en/enterprise/guides/training-crews",
"en/enterprise/guides/update-crew",
"en/enterprise/guides/enable-crew-studio",
"en/enterprise/guides/capture_telemetry_logs",
@@ -3280,6 +3282,7 @@
"en/enterprise/guides/deploy-to-amp",
"en/enterprise/guides/private-package-registry",
"en/enterprise/guides/kickoff-crew",
+ "en/enterprise/guides/training-crews",
"en/enterprise/guides/update-crew",
"en/enterprise/guides/enable-crew-studio",
"en/enterprise/guides/capture_telemetry_logs",
@@ -3751,6 +3754,7 @@
"en/enterprise/guides/deploy-to-amp",
"en/enterprise/guides/private-package-registry",
"en/enterprise/guides/kickoff-crew",
+ "en/enterprise/guides/training-crews",
"en/enterprise/guides/update-crew",
"en/enterprise/guides/enable-crew-studio",
"en/enterprise/guides/capture_telemetry_logs",
diff --git a/docs/en/enterprise/guides/training-crews.mdx b/docs/en/enterprise/guides/training-crews.mdx
new file mode 100644
index 000000000..8366ad641
--- /dev/null
+++ b/docs/en/enterprise/guides/training-crews.mdx
@@ -0,0 +1,132 @@
+---
+title: "Training Crews"
+description: "Train your deployed crews directly from the CrewAI AMP platform to improve agent performance over time"
+icon: "dumbbell"
+mode: "wide"
+---
+
+Training lets you improve crew performance by running iterative training sessions directly from the **Training** tab in CrewAI AMP. The platform uses **auto-train mode** — it handles the iterative process automatically, unlike CLI training which requires interactive human feedback per iteration.
+
+After training completes, CrewAI evaluates agent outputs and consolidates feedback into actionable suggestions for each agent. These suggestions are then applied to future crew runs to improve output quality.
+
+
+ For details on how CrewAI training works under the hood, see the [Training Concepts](/en/concepts/training) page.
+
+
+## Prerequisites
+
+
+
+ You need a CrewAI AMP account with an active deployment in **Ready** status (Crew type).
+
+
+ Your account must have run permission for the deployment you want to train.
+
+
+
+## How to train a crew
+
+
+
+ Navigate to **Deployments**, click your deployment, then select the **Training** tab.
+
+
+
+ Provide a **Training Name** — this becomes the `.pkl` filename used to store training results. For example, "Expert Mode Training" produces `expert_mode_training.pkl`.
+
+
+
+ Enter the crew's input fields. These are the same inputs you'd provide for a normal kickoff — they're dynamically loaded based on your crew's configuration.
+
+
+
+ Click **Train Crew**. The button changes to "Training..." with a spinner while the process runs.
+
+ Behind the scenes:
+ - A training record is created for your deployment
+ - The platform calls the deployment's auto-train endpoint
+ - The crew runs its iterations automatically — no manual feedback required
+
+
+
+ The **Current Training Status** panel displays:
+ - **Status** — Current state of the training run
+ - **Nº Iterations** — Number of training iterations configured
+ - **Filename** — The `.pkl` file being generated
+ - **Started At** — When training began
+ - **Training Inputs** — The inputs you provided
+
+
+
+## Understanding training results
+
+Once training completes, you'll see per-agent result cards with the following information:
+
+- **Agent Role** — The name/role of the agent in your crew
+- **Final Quality** — A score from 0 to 10 evaluating the agent's output quality
+- **Final Summary** — A summary of the agent's performance during training
+- **Suggestions** — Actionable recommendations for improving the agent's behavior
+
+### Editing suggestions
+
+You can refine the suggestions for any agent:
+
+
+
+ On any agent's result card, click the **Edit** button next to the suggestions.
+
+
+
+ Update the suggestions text to better reflect the improvements you want.
+
+
+
+ Click **Save**. The edited suggestions sync back to the deployment and are used in all future runs.
+
+
+
+## Using trained data
+
+To apply training results to your crew:
+
+1. Note the **Training Filename** (the `.pkl` file) from your completed training session.
+2. Specify this filename in your deployment's kickoff or run configuration.
+3. The crew automatically loads the training file and applies the stored suggestions to each agent.
+
+This means agents benefit from the feedback generated during training on every subsequent run.
+
+## Previous trainings
+
+The bottom of the Training tab displays a **history of all past training sessions** for the deployment. Use this to review previous training runs, compare results, or select a different training file to use.
+
+## Error handling
+
+If a training run fails, the status panel shows an error state along with a message describing what went wrong.
+
+Common causes of training failures:
+- **Deployment runtime not updated** — Ensure your deployment is running the latest version
+- **Crew execution errors** — Issues within the crew's task logic or agent configuration
+- **Network issues** — Connectivity problems between the platform and the deployment
+
+## Limitations
+
+
+ Keep these constraints in mind when planning your training workflow:
+ - **One active training at a time** per deployment — wait for the current run to finish before starting another
+ - **Auto-train mode only** — the platform does not support interactive per-iteration feedback like the CLI does
+ - **Training data is deployment-specific** — training results are tied to the specific deployment instance and version
+
+
+## Related resources
+
+
+
+ Learn how CrewAI training works under the hood.
+
+
+ Run your deployed crew from the AMP platform.
+
+
+ Get your crew deployed and ready for training.
+
+