The new handoff: How to share your AI agent’s code with your team6 min read

Your AI agent just built something useful. Maybe it’s a dashboard, a new onboarding flow, or a small internal app.

You can see it working. The code looks good. Now your teammate asks a perfectly reasonable question:

“Can you send it to me?”

That’s where an AI coding workflow often becomes a manual handoff. You send a screenshot, copy a code snippet, push a branch, or explain how to get the project running locally.

But the person asking might not want the source code. They want to open the thing, try it, and tell you what needs to change.

The right way to share AI-generated code depends on what happens next.

First, what are you actually sharing?

When someone asks for the code your AI agent generated, they might mean three different things.

1. The source code

A developer needs to inspect the implementation, run tests, or merge it into an existing codebase.

A Git repository and pull request are usually the right answer. They provide a familiar workflow for reviewing changes, checking tests, and integrating the code into production.

2. A working version

A product manager wants to click through the onboarding flow. A designer wants to see the responsive layout. A stakeholder wants to try the dashboard.

Sending source files isn’t particularly useful here. They need an app or artifact they can open in a browser.

3. Work that someone else will continue

Your teammate wants to review the app, suggest a change, and have an AI agent implement the feedback.

This is a different kind of handoff. The destination needs to hold more than a snapshot. It needs to preserve the work, its history, and access for whoever continues it.

These workflows overlap, but they’re not interchangeable.

The difference between sharing code and handing off work

Imagine an agent builds a customer analytics dashboard.

It includes three KPI cards, a usage chart, and a date filter.

You could send your teammate the React files. They would need a development environment to see the result.

You could send a screenshot. They would understand the layout but wouldn’t be able to test the filter.

You could deploy it somewhere. They could interact with it, but you would still need a process for collecting feedback and updating the project.

Now imagine a slightly different workflow.

The agent creates the dashboard in a shared artifact workspace and gives you its URL.

Your teammate opens the artifact, tests the filter, and leaves feedback on the chart. You ask the agent to make the change in the same artifact.

The URL stays the same. The work has a history. There’s no need to start another conversation and explain what the previous agent built.

That’s the handoff worth designing for.

How to share AI-generated code with your team

Here’s a practical workflow using AgentGrid.io, a shared artifact workspace for people and AI agents, built by Anima.

Step 1: Build with your preferred AI coding tool

Keep working in Claude Code, Codex, Cursor, or another tool you’re comfortable with.

For example:

Build a responsive product analytics dashboard with a usage chart, date filter, and three KPI cards. Use React.

You don’t need to change the tool you use to generate the code.

Step 2: Put the result somewhere your team can access

Connect your agent to AgentGrid and ask:

Put this dashboard on AgentGrid as an app. Give me the artifact link, and keep it private.

AgentGrid stores the app as an artifact with its own URL and version history.

If you’re using a coding agent that can connect independently, follow the AgentGrid connection instructions. Browser-based assistants such as Claude or ChatGPT use their supported integrations.

The important part is that your agent creates the artifact in a workspace it has permission to access.

Step 3: Give your teammate access

Open the artifact and copy its link.

Before sending it, make sure your teammate has access to the workspace. A private artifact URL identifies the work; it doesn’t grant permission by itself.

For internal collaboration, that’s usually what you want. A teammate can review the work without making it publicly accessible.

See how artifact sharing works.

Step 4: Collect feedback on the actual result

Your teammate opens the dashboard and discovers that the date filter isn’t behaving as expected.

Instead of writing a separate Slack message explaining which component is broken, they can leave feedback attached to the relevant part of the artifact.

AgentGrid supports review comments on app elements and document passages. Authorized people and agents can work through those comments together.

That keeps the discussion close to the thing being reviewed.

Step 5: Let the agent continue the work

Now you can tell a connected agent:

Open this AgentGrid artifact and fix the date filter. Keep the existing layout and update the same artifact.

Include the actual artifact URL.

The agent can inspect and update the artifact if it has the required access and capabilities.

Changes appear in the same version history, attributed to the agent that made them. Your teammate can return to the same artifact to review the result.

See version history in AgentGrid.

What about Claude, ChatGPT, Cursor, and Codex?

Some AI tools already have useful native sharing capabilities.

Claude can share supported artifacts within its ecosystem. GitHub is great for reviewing code. Development platforms can provide preview deployments.

You don’t need to replace those workflows just to share something.

AgentGrid is useful when the next step involves people and agents working on the same artifact, particularly across different AI tools.

For example, your team might use Claude to draft an interface, Codex to refine its behavior, and a human reviewer to leave feedback.

The shared artifact provides a common destination for that work.

When should you publish instead?

If the work is intended for people outside your workspace—such as a client who needs a public demo—you may want to publish it.

AgentGrid can publish supported app and Markdown artifacts to public URLs.

But sharing and publishing aren’t the same action.

A private artifact link requires authorized workspace access. A published deployment is publicly accessible. Later changes to the artifact need to be published again before they appear on that public page.

Only publish when that’s what you intend.

A better question to ask when the agent finishes

Instead of ending every coding session with “export the files,” ask:

Who needs this next, and what do they need to do with it?

If a developer needs to merge the code, create a pull request.

If someone needs to test the app, give them a working preview.

If people and agents need to review and continue the work together, give it a shared home.

The output of an AI coding session doesn’t have to be the end of the workflow. It can be the starting point for everyone else.

👉 Explore AgentGrid.