The outcome

Learn the interaction loop using a small task with a clear outcome.

TRAE is a AI-native integrated development environment with conversational and agentic coding workflows. Building and modifying applications through an AI-native editor while keeping project context, generated plans, agent actions, and visual previews in one workspace. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Review the current privacy policy and data-practices statement before installing or opening confidential code.

  2. 02

    Enable the strictest suitable privacy settings and start with a disposable or non-sensitive repository branch.

  3. 03

    Give one bounded task clear acceptance criteria, relevant files, prohibited actions, and validation commands.

  4. 04

    Review network-sensitive features, generated files, commands, tests, preview behavior, and the complete diff before shipping.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

TRAE states that codebase files may be temporarily uploaded to compute indexes and that embeddings and metadata can be retained. Privacy Mode can limit telemetry but does not replace an organizational data review. Avoid regulated or confidential repositories until terms, regions, subprocessors, retention, model training, and deletion meet policy; minimize context and extensions, isolate agent work, keep credentials out, and verify every command, dependency, diff, and deployment manually.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.