Concern 142 · Working draft · editorial review in progress
Evaluation for agentic behavior
Evaluating agentic behavior means testing an AI's sequence of actions, use of tools, and effects over time—not just whether one answer sounds right.
Understanding the concern
An agent can succeed at a final task while making unsafe intermediate changes or spending more resources than expected. Repeated runs can also differ. Evaluation needs to inspect permissions, actions, recovery, and cost.
Benchmarks are useful but may not resemble the actual work environment. Claims should say what was tested and what the agent was allowed to do. A good demonstration is not proof of dependable performance across changing tasks.
An example
An assistant finishes a booking task but creates duplicate reservations along the way.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Agent
- An AI system that can choose and carry out several steps toward a goal, using the tools and access people give it.
- Benchmark
- A standard test used to compare systems. It only measures the tasks and conditions included in that test.
Evidence and its limits
The paper analyzes shortcomings in agent benchmarks, including cost and real-world usefulness.
Kapoor and colleagues · 2024 · Research analysis
AI Agents That Matter ↗ (opens in a new tab)Examines agent evaluation, cost, reproducibility and benchmark overfitting; concerns the benchmarks studied.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Did the agent complete the task within the agreed limits, including its intermediate actions?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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