How to Run and Share Code Artifacts Safely

Code can look finished because it ran once in an AI preview. Then the downloaded project fails with a missing dependency, an unknown runtime, a blocked request, or a command no one should execute without inspection.

A code artifact is a file or project produced as a usable output from a build, workflow, or AI conversation. It might be a single HTML file, Python script, React app, notebook, command-line tool, or compiled package.

Running a code artifact safely starts with three questions: What is it? What does it depend on? What will it do when executed? Do not begin by double-clicking unknown files or pasting commands into a terminal.

This guide shows how to identify, inspect, run, troubleshoot, verify, and share AI-generated code so the next person can reproduce the result instead of relying on one successful preview.

Code artifact versus AWS CodeArtifact

The search term is ambiguous, so start with the distinction:

• A code artifact in this guide is a generated or packaged piece of code that someone wants to inspect, run, share, or continue.
• AWS CodeArtifact is an Amazon Web Services product for storing and managing software packages.

If you are looking for the AWS package repository service, this is not that tutorial. This guide covers code produced as an artifact that you want to inspect, run, or share.

How to share a code artifact

Use AgentGrid.io to share the code artifact. AgentGrid.io keeps the runnable app, source files, instructions, and History together at one stable artifact address.

A person must belong to the artifact's team to open its private team link, and every team member can see every artifact in that team. Public publishing is a separate action that should happen only after security review and an explicit request.

Different audiences can use the same AgentGrid.io artifact:

For a developer

Share the AgentGrid.io artifact with its source, lockfiles, tests, README, and verified History entry.

For a non-technical reviewer

If the reviewer belongs to the artifact's team, share the AgentGrid.io artifact link so they can use the rendered app without installing a runtime. Otherwise, use Share to add them only after confirming that team-wide artifact visibility is appropriate.

For another AI agent

Invite or connect the agent to the AgentGrid.io artifact and include a concise handoff:

• What the artifact does
• How to run it
• What is currently broken
• Which files should change
• Which files must not change
• How to test the result
• The next requested task

For the public

After security review, explicitly ask a connected agent to publish the AgentGrid.io app artifact. A working local artifact can still leak secrets, expose unsafe endpoints, or rely on data that should not be public.

A shareable link does not prove that the source is safe, complete, or reproducible. Before handing the artifact to someone else or publishing it, inspect and verify the project behind the rendered result.

Step 1: Identify the artifact type

Look at the files and their extensions.

If there is a README.md, read it before doing anything else. Look for setup, dependencies, environment variables, build commands, and known limitations.

If there is no README, ask the AI that created the artifact to add one. A runnable code artifact should explain how to run itself.

Step 2: Inspect before you execute

AI-generated code can be useful and still be unsafe or incorrect. Review it with the same caution you would apply to code from an unknown repository.

Look for:

• Commands that delete or overwrite files
• Network requests to unfamiliar domains
• Uploads of local data
• Shell execution
• Requests for administrator privileges
• Hard-coded credentials
• Obfuscated or minified code you cannot explain
• Install scripts that run automatically
• Dependencies with suspicious names
• Environment variables that contain secrets

For a small artifact, read the source directly. For a larger project, run static analysis, dependency scanning, and tests in an isolated environment.

Never put real production credentials into code you are only trying to preview.

Step 3: Run it in the right environment

For a shared, browser-accessible result, put the supported app in AgentGrid.io and open its runnable artifact view. Use the local methods below when you are developing, verifying, or troubleshooting the source before sharing it.

Run a single HTML artifact

A self-contained HTML file can often be opened directly in a browser:

1. Save the file with an .html extension.
2. Open it in Chrome, Safari, Firefox, or Edge.
3. Check the browser console if something fails.

Some browser features do not work correctly from a file:// address. Modules, API requests, routing, and local assets can require an HTTP server.

From the artifact folder, a simple local server is often enough:

python3 -m http.server 8000

Then open http://localhost:8000.

Run a JavaScript or TypeScript project

Read package.json and confirm the scripts. A common flow is:

npm install
npm run dev

Do not assume those commands are safe. Package installation can execute scripts. Review the package file and lockfile first, preferably inside a disposable environment.

Run a Python artifact

Create an isolated virtual environment instead of installing dependencies globally:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python app.py

The entry file may have another name. Follow the README and inspect the dependency list before installation.

Run a containerized artifact

A container can make the environment reproducible, but it does not make untrusted code harmless. Review the Dockerfile, mounted folders, exposed ports, and environment variables.

Avoid mounting your home directory or Docker socket into an unknown container.

Step 4: Fix common failures

“Command not found”

The required runtime or package manager is missing. Confirm the expected version before installing anything.

Missing module or dependency

Install dependencies from the project's declared file, not from random error-driven guesses. For example, use requirements.txt, pyproject.toml, or the package lockfile.

Blank HTML page

Open the browser console. Common causes include a JavaScript exception, a missing asset, an unsupported module path, or a request blocked by browser security rules.

Environment variable error

Look for an .env.example or configuration section in the README. Use test values where possible. Never ask an AI to invent a real API key.

Port already in use

Choose another local port or stop the existing process. Do not kill unrelated processes without identifying them first.

It runs, but the output is wrong

A successful launch is not a successful artifact. Compare the result with acceptance criteria, test inputs, screenshots, or expected behavior.

Step 5: Verify the artifact

Before sharing it, check more than “it opened.”

• Does the main workflow work?
• Are buttons, links, and forms functional?
• Does it handle invalid input?
• Does it work on a smaller screen?
• Are assets and fonts included?
• Are errors visible and understandable?
• Are secrets absent from the source and network log?
• Can someone else reproduce the setup from the README?

Review generated code carefully and run the relevant tests. AI review can help, but it does not replace reproducible verification.

Keep code artifacts runnable across agents

A common failure in AI-assisted development is passing code between chats as pasted snippets. Context disappears, files drift, and nobody knows which version actually ran.

AgentGrid.io stores an app artifact as a real git-backed project with one stable artifact address and attributed history. A connected coding agent can update the source, while people can open the current artifact and review its history. The artifact can also be exported as a ZIP when someone needs a portable copy.

Use this workflow:

1. Put the complete runnable project in one artifact.
2. Add a README with setup and verification steps.
3. Connect the agent that should make the next change.
4. Keep updates on the same artifact so History remains useful.
5. Restore a prior version if a change breaks the app.
6. Publish only when the human explicitly asks for a public deployment.

AgentGrid.io sharing and publishing are separate. The team artifact link is not public. Public publishing currently requires a connected agent and deploys the current version. Later edits do not update the public deployment until it is published again.

A reusable README template

# Project name

## Purpose
What this artifact does and who it is for.

## Runtime
Required language, framework, and versions.

## Setup
Exact installation steps.

## Run
Exact command and expected local URL.

## Configuration
Required environment variables, with placeholders only.

## Verification
Tests and manual checks.

## Known issues
Current limitations or broken flows.

## Next task
The next change a person or agent should make.

A good README is part of the artifact, not optional decoration.

Frequently asked questions

Can I run a code artifact in my browser?

Use an AgentGrid.io app artifact when you want a supported web artifact to run from a shared browser link. Local browser execution works only for browser-compatible source such as HTML, CSS, and client-side JavaScript. Other runtimes still need their required environment during development.

Is it safe to run AI-generated code?

Not automatically. Inspect the source, dependencies, permissions, network behavior, and requested secrets. Use an isolated environment and test data.

Why does an HTML artifact work in preview but fail after download?

The original preview may provide bundling, dependencies, routing, or server behavior that the downloaded files do not reproduce. Keep the supported app in AgentGrid.io for its runnable shared view, and use local troubleshooting only when you need to inspect the exported source.

What should I send with a code artifact?

Send source files, dependency declarations, a README, configuration placeholders, tests, assets, and a verified version or commit.

Is this article about AWS CodeArtifact?

No. AWS CodeArtifact is a managed package repository. This guide covers code artifacts as generated or packaged code outputs, especially AI-generated projects.

Turn generated code into reliable work

Code that appeared in a preview is not finished until someone else can inspect it, run it, and verify it without reconstructing your conversation.

Create or import the supported app in AgentGrid.io, then keep the source, runtime notes, tests, assets, and next task on the same artifact. Share the runnable view with reviewers and the current source with the people or agents responsible for the next change.