Best Practices for Testing AI Agents
A community discussion on how to properly test and evaluate AI agents. Establishing good testing methods is crucial for building reliable agents that perform well without wasting tokens.
When developers build AI agents, they need ways to measure if the agent is doing its job correctly. These measurements are called "eval tests." The discussion focuses on the most effective ways to set up these tests. Good evaluation practices help ensure that an agent doesn't get stuck in loops or give wrong answers. This is especially important for keeping costs low, as poorly tested agents might use more processing power and tokens than necessary to complete a task.
Key points
Quick term guide
- AI agents
- AI agents are AI tools that can carry out steps toward a goal, not just answer once.
- AI agent
- An AI program that can inspect information and suggest what to do next.
- testing
- The process of checking that software does what it's supposed to do, usually by running it and looking for errors.
- tokens
- Tokens are small pieces of text that AI systems count when reading or writing.
- developers
- Developers are people who build software, apps, or websites.
- eval tests
- Methods used to measure and score how well an AI system is performing its intended task.
- valuation
- The amount investors think a company is worth.
- sources
- Evidence showing where a piece of information came from.