From cf33f340a395c5a32c5f0f6eecce6eaa7d170d69 Mon Sep 17 00:00:00 2001 From: Iris Clawd Date: Wed, 25 Mar 2026 18:30:49 +0000 Subject: [PATCH] docs: add AMP Training Tab guide for enterprise deployments --- docs/docs.json | 4 + docs/en/enterprise/guides/training-crews.mdx | 132 +++++++++++++++++++ 2 files changed, 136 insertions(+) create mode 100644 docs/en/enterprise/guides/training-crews.mdx 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. + +