Ensure AI Agents Work: Evaluation Frameworks for Scaling Success

Aparna says teams need to check how agents perform in real use, especially as systems expand from text to voice and multimodal interactions. She cites call centers handling over one billion calls with voice assistants and points to Priceline’s PennyBot as a travel agent that can book a vacation hands-free.

Aparna describes three common components: a router that chooses the next step, skills that perform the work, and memory that maintains conversation state. These patterns apply across frameworks such as LangGraph, CrewAI, and LlamaIndex Workflows, as well as custom implementations.

Teams should check whether the router chooses the right skill and passes the right parameters, such as a requested material or price range. For a retrieval-augmented generation skill, evaluations can check retrieved chunk relevance and answer correctness using judge-based or code-based methods. Aparna also calls for measuring convergence: whether repeated runs complete a task reliably in a consistent number of steps.

Voice systems require checks beyond their transcripts because the audio itself adds evaluation needs. Aparna names sentiment, transcription accuracy, consistent tone, speech quality, and intent as areas to assess alongside conversation flow.

Aparna shows Arize’s co-pilot traces and says the company evaluates its own agent at each step. Its checks cover the overall answer, the search router’s route and arguments, and whether the skill completed correctly, helping teams locate where a failure occurred.