Editing Ai Agents (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert evaluation of agentic systems requires new metrics beyond accuracy:

'''Task completion rate''': What fraction of tasks is completed successfully end-to-end? Track separately for easy, medium, and hard tasks.

'''Step efficiency''': How many tool calls / LLM calls does the agent use to complete a task? Compare to a minimum viable path. Inefficient agents are expensive.

'''Safety and containment''': What fraction of runs produced actions outside the intended scope? What is the blast radius of agent errors? This is especially critical for agents with write access to real systems.

'''Failure mode taxonomy''': Classify failures — planning error, tool execution error, observation misinterpretation, hallucinated tool call, loop detection. Different failure types require different mitigations.

Expert practitioners implement '''human-in-the-loop checkpoints''' at high-stakes decision points (sending an email, making a purchase, deleting a file) regardless of automation level, treating these as irreversible actions requiring confirmation.
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