I’ve observed that without a clear understanding of these categories, teams often end up forcing a use case into the wrong evaluation tool. This 'square peg in a round hole' approach results in a fragmented mess. Instead of a cohesive system, you get an inextensible architecture where your testing and monitoring are awkwardly baked into the logic, making it nearly impossible to scale or pivot.

Approach for a comprehensive evaluation framework

Step 1: Start with unit testing and early persona-based testing setup.

As you start developing your application, it’s crucial to begin with unit evaluations for your agents early. This often involves building a “jury” of judges to evaluate system behavior. As you gain deeper insights into your target audience and specific use cases, start building representative multi-turn test scenarios to validate your system through simulated conversations. At this stage, you’ll likely have around 20% of your scenarios automated, with the remaining 80% still requiring manual validation.

Step 2: Refine your personas, judges and tests as your application goes through business user testing.

As you come to the stage where the business users begin testing your conversational AI application, use:

  • Conversational scenarios to add scenarios to your test suite.

  • Refine your personas to reflect the personas the users testing with.

  • Recalibrate the jury of judges to find out the kind of issues which were identified by business users.   

With this, you should be able to increase automated test coverage substantially from the earlier ~20%. 

Step 3: Introduce production observability.

Once the application reaches production, evaluation should move beyond synthetic testing and into real-world monitoring. Capture traces, user feedback, latency, costs, retrieval quality and failure patterns. Production observability helps identify issues that were never represented in test datasets and provides the feedback loop needed for continuous improvement.

Step 4: Continuously improve using production feedback.

Evaluation frameworks should evolve alongside the application. Review production conversations regularly, identify failure modes, update test datasets and recalibrate LLM judges. The goal isn’t to achieve a perfect evaluation framework on day one, but to continuously increase confidence as the system matures.