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AI Evals for Agentic Systems
Quality, safety, and reliability are critical to the success of enterprise agentic systems. If AI systems are not reliable, it cannot be used at production level. Evals or AI Evaluation serve as the core governance framework.
AI Evals are code-based benchmarking mechanism for model safety, quality, and reliability.
Evals are different from conventional testing mechanism. Where as testing validates exact matches and predictable outputs,Evals assesses overall quality, intent, and alignment.
How Eval Works?
You make changes to your AI App such as by updating a prompt, or changing model or changing some logic. You want to know if those changes are causing any regression; i.e. if the system has stopped performing like it was performing earlier.
You send the output generated by AI App to Eval where it is evaluated on multiple subjective criteria such as correctness, completeness, safety, relevance etc. A Grader or Scorer is used to assign score to each metric. If score being generated is within acceptable thresholds, it is accepted; else changes are flagged for review.
