I

Intent

Describe a feature. AI agents build, verify, and ship it.

PriceFreemiumReviewed2026-04-15

Expert verdict

Ship

3-1
3 Ships1 Skips
Visit www.augmentcode.com

The Panel's Take

Intent is Augment Code's multi-agent software development workspace. You describe what you want built — a feature, a fix, a refactor — and a coordinated team of AI agents takes it from spec to shipping code. The system maintains living specifications that stay current throughout the development process, so requirements don't drift as agents work. Under the hood, Intent runs agents in isolated workspaces so different tasks can't interfere with each other. A coordinator agent manages task delegation, routing work to specialized agents for code generation, design review, mobile implementation, and other concerns. The spec panel tracks project requirements and progress in real time, giving you a single pane of glass over what agents are doing and what remains. Augment Code has been quietly building toward this for a while — their IDE Agents and CLI products form the underlying layer, with Intent sitting on top as the higher-level orchestration product. It's positioned squarely against Devin and SWE-agent-style autonomous coding, but with more emphasis on keeping humans in the loop through living specs rather than handing off completely.

Share this verdict

Intent verdict: SHIP 🚀

3 ships · 1 skip from the expert panel

Full review: shiporskip.io/tool/intent-augment-code-multi-agent-living-specs-isolated-workspaces-2026

Weekly AI Tool Verdicts

Get the next verdict in your inbox

7 critics review a new AI tool every day. Weekly digest — free.

Looking for Intent alternatives?

Compare Intent with every other AI Agents tool reviewed by our panel.

See all AI Agents alternatives

Embed this verdict

Tool makers can add a live ShipOrSkip badge to their site. Badge loads track impressions; clicks route back to this review.

Ship · 7.5/10
HTML badge
<a href="https://shiporskip.io/api/badge-click/intent-augment-code-multi-agent-living-specs-isolated-workspaces-2026" target="_blank" rel="noopener"><img src="https://shiporskip.io/api/badge/intent-augment-code-multi-agent-living-specs-isolated-workspaces-2026" alt="Intent Ship verdict on ShipOrSkip" width="360" height="90" /></a>
Markdown badge
[![Intent Ship verdict on ShipOrSkip](https://shiporskip.io/api/badge/intent-augment-code-multi-agent-living-specs-isolated-workspaces-2026)](https://shiporskip.io/api/badge-click/intent-augment-code-multi-agent-living-specs-isolated-workspaces-2026)
Iframe widget
<iframe src="https://shiporskip.io/embed/intent-augment-code-multi-agent-living-specs-isolated-workspaces-2026" title="Intent ShipOrSkip verdict" width="360" height="260" style="border:0;border-radius:16px;max-width:100%;" loading="lazy"></iframe>

The reviews

The living specs concept is the right idea — autonomous coding agents fail because requirements get lost mid-task. Keeping a maintained spec that agents reference throughout solves the context drift problem. Isolated workspaces mean you can run parallel feature development without race conditions. This is a serious tool for serious teams, not a toy.

Helpful?

Every multi-agent coding tool in 2026 promises to 'build, verify, and ship' features autonomously. Most of them generate plausible-looking code that compiles but doesn't actually work as intended. Augment Code has solid underlying models but 'coordinated agent teams' still means you're debugging AI-generated code at the seams between agents. Until I see real production deployments with zero-intervention feature shipping, this is glorified autocomplete with extra steps.

Helpful?

Intent represents the transition from AI-assisted coding to AI-directed development. The living spec paradigm is a genuine architectural insight — specs as shared context between agents and humans is how autonomous software teams will be organized. Augment's bet on coordination over raw capability is the right design philosophy as models plateau in coding benchmarks.

Helpful?

The spec panel that tracks requirements in real time is a design win — it makes AI development legible to product managers and designers, not just engineers. Seeing what agents are doing across isolated workspaces without reading logs is the kind of transparency that actually builds trust in AI tooling.

Helpful?

Bookmarks

Loading bookmarks...

No bookmarks yet

Bookmark tools to save them for later