AI agents can draft, answer and decide at speed. But how well they perform in your real workflows depends on the people who train and evaluate them.
Training is scenario work
An agent learns what "good" looks like from examples. Humans design realistic scenarios, write the instructions and mark which responses are right, partly right or unacceptable.
Evaluation never ends
Once an agent is live, its outputs need regular review: conversations, reasoning steps, task completion and edge cases. Human reviewers catch what automated checks miss, and feed it back into the next improvement cycle.
Clean data is the foundation
Annotation, labeling and data preparation sound routine but decide the ceiling on quality. Inconsistent labels produce inconsistent agents.
This is operational work: documented guidelines, reviewer calibration, sampling and quality scores. It is exactly the kind of work a disciplined operations team does well.
Key takeaways
- Agents improve through well-designed scenarios and honest evaluation.
- Human review catches issues that automated tests miss.
- Consistent guidelines and quality sampling protect data quality.
