Most teams evaluate AI agents the same way they evaluate ML models. This is a fundamental mistake.
When you evaluate a traditional ML model, you're looking at a single input → output. You check: Is the prediction accurate? Does it meet the threshold?
But AI agents are fundamentally different. They're not making a single prediction. They're executing a trajectory of decisions:
• Step 1: Agent receives user input
• Step 2: Agent reasons about the problem
• Step 3: Agent decides which tool to call
• Step 4: Agent receives tool output
• Step 5: Agent reasons about the result
• Step 6: Agent decides on next action
• Step 7: Agent provides final response
If you only evaluate the final response, you're missing 90% of the problem.
The real evaluation happens by analyzing the entire trajectory. You need to ask:
• Did the agent follow its system prompt throughout the entire conversation?
• Did it make logical decisions at each step?
• Did it use the right tools in the right order?
• Did it handle edge cases correctly?
This is why traditional metrics like accuracy don't work for agents. You need a framework that evaluates the entire decision-making process.
What's the biggest mistake you've seen in agent evaluation? Checkout Noveum.ai if you are looking to evaluate your AI Agents.
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