How do I test an AI agent before it touches real data?
Build a sandbox first
Run the agent against a copy of your data or a dedicated test account. Give it read-only access at the start, and block write actions until you trust its behavior. Many frameworks let you swap real tools for mock versions that log what would have happened. Log every test run so you can compare results after each change.
Keep the sandbox separate from production credentials. Test API keys make sure a mistake cannot reach real customers or records.
Create a test set of tasks
Write ten to twenty tasks that match the work you expect, including a few tricky cases such as missing information or conflicting instructions. Record what a good result looks like for each one, then rerun the whole set after every change to the prompt or tools. Start with a few tasks you already know the answer to, then add messier cases once the basic ones pass every time.
Approve actions before they happen
For the first few weeks, require a person to approve any action that sends messages, spends money, or changes records. Relax the rule gradually for actions that prove safe in the logs, and keep approval for anything irreversible.
Common mistakes
- Testing only the happy path, so the agent fails the first time it sees messy input.
- Using production API keys in a test environment by accident.
Related questions
- How should I design the architecture of an AI agent?
- What is the ReAct pattern and why use it?
- How do I add memory to an AI agent?
- What is the difference between a single-agent and multi-agent system?
- How do I handle tool use and function calling in AI agents?
- What are common pitfalls in AI agent design?