Evaluations, or Evals in the context of AI agents, are a specific architecture used in autonomous systems to mitigate errors throughout a multi-step process in which the output generated by one individual step or agent is immediately reviewed and verified by the subsequent agent. This internal verification technique is in practice a quality control mechanism that confirms whether the data produced meets standards before the parent agent proceeds any further.
In practice, an AI agent double-checks whether the output of the previous agent makes sense against the specific task instructions, the given context, and available company data.
Interestingly, advanced versions of Evals may even chain a human to validate critical steps, typically used when accuracy is more critical than immediate execution speed for the specific business process being automated.
Some critics argue that Evals are merely artisan techniques used to patch the inherent limitations of large language models in complex decision-making. Instead of relying on these external layers of verification, they suggest that developers should focus on revising fundamental model integrity. They believe the core architecture should be improved rather than adding manual layers to fix model flaws.
In work automation, Evals are essential components of both vertical agents and modern agentic ecosystems. While they are the primary reason for increased response times, they remain the most effective known method for improving quality.
