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Sandboxes for AI agents.

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Core infrastructure to deploy agents safely.

Works with your agent stack

Why do agents need sandboxes?

LLMs generate text. But agents act.

They call tools that update apps, write to databases, trigger workflows, and depend on existing user data and system state. When something breaks in production, the failure lives inside that exact mix of data, context, and tool interactions.

If you can't recreate those conditions, you can't fix what failed.

Initialize state for each environment

Isolated runs within sandbox

Automatic reset and teardown

create_sandbox.py
sandbox = playgent.create_sandbox(
identity="Michael, a senior accountant at Acme Corp",
apps=["stripe", "gmail", "quickbooks"],
data={"gmail": "./gmail_seed_data.json", "stripe": "./stripe_seed_data.json"}
)
Creating sandbox environment...
Initializing user profile...
Connecting apps: stripe, gmail, quickbooks...
Generating MCP server...
Sandbox ready!
print(sandbox.mcp_url)
mcp://playgent.dev/sandbox/7x9k2

Playgent provides the continuous improvement infrastructure for agents.

We provide secure, on-demand sandboxes where agents can operate with controlled access. Instantly spin up environments with realistic data, run your agent, and tear everything down when you're done.

Our sandboxes make failures reproducible, enable safe iteration, and let enterprises improve agents without risking production.

Built for Enterprises

Intentional data solutions for industry leaders.

Learn more

Vertical-specific

Our researchers deeply understand your industry's unique challenges — from regulatory requirements to domain-specific workflows. We implement targeted solutions tailored to your firm, ensuring your agents are tested against realistic scenarios that matter to your business.

Create environments that mimic reality

Our platform creates high-fidelity simulations that mirror your production systems down to the API level. Test with realistic user data, authentic tool interactions, and stateful workflows that enable safe, comprehensive testing without production risk.

Faster time-to-deployment

Accelerate your development cycle with instant sandbox provisioning and parallel test execution. Our streamlined, guided deployment process helps teams iterate rapidly, identify issues early, and ship production-ready agents with confidence.

Frequently Asked Questions

Everything you need to know about Playgent's testing platform.

Playgent is the only testing platform that develops custom user environments. Go beyond static datasets and give your agents simulated users with full profiles and complete suites of tools. Our approach creates realistic, dynamic testing scenarios that reflect how your agent will perform in the real world.

Playgent has a proprietary method to mock tools and build stateful environments, so you never need to worry about auth setup or manually configuring integrations. To your agent, it seems like a real user with real connected apps—but it's all simulated behind the scenes.

We support over 100 integrations out of the box, including popular tools like GitHub, Jira, Linear, Slack, Zendesk, Stripe, QuickBooks, Gmail, and many more. If you need a custom integration, our team can build it for you as part of your onboarding.

You can export OTel traces from Playgent and import them into any evals or agent improvement tool. For enterprises training models, we build custom RL environments to close the loop from our environment to parameter optimization.

Absolutely. Playgent provides APIs and CLI tools that integrate seamlessly with GitHub Actions, GitLab CI, Jenkins, and other popular CI/CD platforms. Run your agent tests automatically on every pull request and catch regressions before they reach production.

Give your agents a playground.

Select a user persona to see how Playgent simulates realistic testing scenarios.

Please view on desktop for best experience

Playgent Sandbox
User Alex
GitHub Jira Linear
Generated Input
Fetch all risk reports and summarize the main points
Agent Output
Your Agent
Prompt
Tools
Optimizing prompt
Your Evals
Passed
Correctness 0.95
Clarity 0.88
Completeness 0.92
LLM Grader Rationale
The agent correctly fetched and summarized the risk reports as requested. The response is clear and addresses all main points.

Ready to test with confidence?

Whether you're launching your first AI agent or scaling to millions of users, we'll help you build the testing infrastructure you need.

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