# An AI-operated audio workshop

Build a native Tauri audio workshop where the person and their existing coding agent can work on the same recording. Build it extremely well. Someone should be able to explain the edit they want, listen to what changed, and adjust it themselves. Follow the [shared launch](../shared/launch.md).

Choose an actual use for the person: preparing an interview excerpt, shortening a spoken explanation, or assembling selected practice recordings. Bring a suitable sample and complete that editing experience. Existing recordings from the [practice notebook](../native-phone/practice-notebook.md) or [voice-capture app](../native-phone/voice-capture.md) can supply the material.

## Make the recording easy to work with

Give the person clear playback, seeking, selection, and a timeline they can understand. Provide enough surrounding sound to judge a proposed cut. Where speech is involved, a useful transcript can help find passages and navigate to the corresponding audio.

Let them label sections and assemble a version from selected material. Keep the original recording, the editable arrangement, and the exported audio distinct. Save the work so they can return to an unfinished edit and understand which source passages it uses.

Support comfortable direct adjustments: move a boundary, change the order, retain a pause, or compare two versions. Make undo and returning to an earlier useful edit part of normal work.

## Let the agent operate the workshop

Connect the existing coding agent through proper tools for inspecting the recording, current selection, transcript, and edit. Give it meaningful operations to find passages, select regions, assemble an arrangement, change an edit, and prepare playback or export. Reuse its installation and account for the capabilities available there.

Make a request such as “Use the explanation of the cancellation policy, followed by the example near the end” produce an actual editable result. Let the agent inspect context around each passage and choose cuts that preserve the intended meaning. Show its work on the same timeline the person uses.

When the person moves a boundary or changes a passage, have the agent continue from that actual edit. Keep the conversation attached to the recording and version being discussed.

## Give listening the right tools

A transcript can locate words. Judging a clipped syllable, an awkward pause, background sound, or a change in vocal delivery requires hearing the audio. Give the agent access to an audio-capable model and the relevant recording or excerpt when that judgment is needed. Discover the suitable supported access route, reusing existing capabilities and making any additional costs clear.

Let the person audition proposed results promptly. If they ask for cleaner sound or different loudness, make the adjustment available for comparison with the original. Preserve useful natural speech and the person's intended effect as you judge the result.

Choose output options for where the recording will be used. Make the resulting file easy to find, play elsewhere, and share, with its content matching the selected version.

## Deliver a finished piece of audio

Complete the app, agent connection, playback and processing setup, and a real sample edit. Locate a passage by meaning, assemble a shorter version, change it manually, ask the agent to continue, and listen to the exported result.

Teach the person how model comprehension and precise editing operations cooperate. They can direct the meaning and desired effect while the application preserves exact source material and edits.

Later, connect the [batch-work queue](../background-services/batch-work-queue.md) for repeated processing or a phone companion for capture and auditioning away from the computer. Keep the same recordings and editable projects useful as those capabilities arrive.