Sandbox Testing

Deploying an untested AI agent to production is like pushing code without running tests. Rach.Dev's sandbox environment gives you a production-identical testing ground where you can interact with your agent, trace its decision-making process, and share preview links with stakeholders for approval. The sandbox runs on the same infrastructure as production, so performance characteristics match exactly.

Decision tracing is the standout feature. Every sandbox conversation shows you the agent's internal reasoning: which tools it considered calling, what information it extracted from the user's message, why it chose a particular response, and which guardrails were evaluated. When the agent makes an unexpected choice, you can see exactly why and adjust the configuration accordingly. This transforms agent debugging from guesswork into a systematic process.

Shareable preview links let you send a sandbox URL to anyone — your product manager, compliance team, or client — so they can interact with the agent directly and provide feedback. Preview links are read-only in terms of configuration, so reviewers can chat with the agent but cannot change its behavior. You can create multiple sandbox environments with different configurations to A/B test approaches before committing to one. Simulated scenarios let you replay common and adversarial conversation patterns automatically to verify your agent handles them correctly.