An AI agent can look useful even when it isn’t. The difference lies in the checks: what it measures, where it goes wrong, where it stops and how much it costs to correct. The focus here is on how to verify whether an agent works well inside a real process, rather than on how to build one.
What is the first mistake when evaluating an AI agent?
The first mistake is judging it by the final answer rather than by the process. An agent can produce a plausible output and still have taken the wrong path, skipped a check or followed incomplete instructions. That’s why evaluation has to start from goals, expected steps and checkpoints.
Which metrics really matter?
It depends on the use case, but in practice you need at least four levels: correctness of the result, adherence to instructions, reliability in the intermediate steps and the ability to handle errors. If an agent works across several turns or several tools, you also need to measure where the first failure occurs.
Why isn’t a fluent answer enough?
Because fluency proves neither the soundness of the decision nor the quality of the workflow. An agent can sound convincing even when it has ignored a constraint, used the wrong tool or taken an unintended shortcut. Evaluation has to separate form from behaviour.
What role do safety checks play?
Checks show whether the agent stays within the defined limits. In a business context, that means verifying permissions, correct use of tools, respect for operational boundaries and the ability to stop the action when needed. Without these checks, an agent can’t really be governed.
How does evaluation connect to business value?
An agent is only worth something if it reduces time, errors or costs in a real process. So evaluation shouldn’t stop at the technical score: it also has to measure operational impact, the remaining human workload and the ability to deliver a useful result for day-to-day work.
What is the most useful criterion for a company starting now?
The most useful criterion is simple: define success first, then measure it. First decide what the agent must do, which errors are unacceptable and which thresholds trigger a correction; then build the test. Without this sequence, every evaluation remains open to debate.
When should you stop and correct?
It’s worth stopping when failure is systematic, when the agent’s behaviour can’t be explained by the metrics collected or when the cost of control outweighs the benefit. In those cases the problem goes beyond the model: it lies in how the process is designed.
What should an SME look at before adopting an AI agent?
An SME should ask whether it has a process stable enough to automate, whether it can measure errors and whether it has a clear criterion for stepping in. If these three elements are missing, the project risks being an interesting demo rather than a reliable tool.
LU3G insights
Sources
- Amazon Web Services: Agent skills should also be evaluated on correct selection and adherence to instructions, not only on the quality of the final answer.
- Amazon Web Services: In multi-turn systems an early error can contaminate later turns; evaluation must identify the first point of failure.
- NIST AI Risk Management Framework: AI should be managed with an approach based on risk, control and continuous measurement.
- OECD Framework for the Classification of AI Systems: Classifying AI systems helps to read the context, use and level of impact of automation.
- AI4Business: The public debate on AI agents covers safety, operational limits and external testing.
Giordano Vinci
Giordano Vinci is CEO of LU3G, a digital agency that works alongside companies and SMEs on websites, SEO strategy, advertising campaigns and online growth. His approach combines consulting, method and strategic vision, with the aim of turning digital into a practical business tool.
Giordano Vinci
Giordano Vinci is CEO of LU3G, a digital agency that works alongside companies and SMEs on websites, SEO strategy, advertising campaigns and online growth. His approach combines consulting, method and strategic vision, with the aim of turning digital into a practical business tool.