Skip to main content
How often does your agent get the task right? A benchmark turns that question into something you can measure: a set of realistic tasks with clear criteria for success. Run it regularly to track quality and see whether your changes actually help.

​
Know how good your agent is

An eval measures how often your agent succeeds across the tasks you care about. It gives you a baseline for comparing changes to prompts, models, tools, and lessons. A larger, more representative benchmark gives you more confidence in the results. Include everyday tasks, difficult cases, and failures from production. Clear success criteria and reliable grading matter more than adding duplicates of cases you already cover.

​
Know when quality changes

Your agent can get worse even when its code stays the same. Data can become incomplete or outdated, users can start asking different questions, and model providers can change the model or its configuration behind the same endpoint. Run your benchmark regularly to detect changes in accuracy and latency. Keep a stable set of cases to track regressions, and add cases from recent usage to keep the benchmark relevant to your users.

​
Know what to improve

Failed samples show you where to investigate: missing knowledge, poor retrieval, incorrect tool use, or an instruction the agent did not follow. Inspect the failures, make a targeted change, and rerun the benchmark. Compare which samples improved and which regressed to check that your fix helps without introducing problems elsewhere.

​
Provide evidence for regulated use

In finance, insurance, and healthcare, demonstrating that an AI system works as intended can be part of meeting regulatory obligations. Evals provide repeatable evidence for validation before deployment and as the system changes.
  • FINMA: Its AI guidance examines whether supervised institutions test accuracy, robustness, and stability, define expected results and performance indicators, and monitor ongoing output quality and data drift. See FINMA Guidance 08/2024, sections 2.4–2.5.
  • SEC: Staff guidance for robo-advisers recommends considering compliance policies for testing and backtesting algorithmic code and monitoring its performance after deployment. See Robo-Advisers, section 3.
  • FDA: For AI-enabled medical device software, its final guidance on predetermined change control plans recommends describing how planned modifications will be developed, validated, and implemented to maintain safety and effectiveness. See FDA guidance on predetermined change control plans.
These sources address specific regulated uses and supervisory expectations; they do not impose one universal benchmark requirement on every AI agent. A benchmark contributes evidence to a broader validation process.
Sovara connects evaluation results to the underlying traces, so you can move from a score to understanding what happened. Next: Evals in Sovara.