Contact center AI observability software provides purpose-built tools to monitor, analyze, and assure the performance of AI agents, such as voicebots and chatbots, in customer service operations. These platforms give AI operations and customer experience (CX) leaders the visibility to manage the "black box" of conversational and generative AI, enabling them to proactively test AI behavior, diagnose failures, and validate that every automated interaction is accurate and safe. Organizations leverage this software to de-risk AI deployments, improve self-service containment rates, reduce AI-driven errors, and build essential trust in their customer service automation.
This category is essential for managing modern contact centers powered by customer service automation software or AI customer support agents, where traditional monitoring falls short. Contact center AI observability is distinct from contact center quality assurance software, which focuses on human agents, not AI behavior. It also differs from application performance monitoring software and observability software platforms that monitor infrastructure health rather than conversational AI quality. Finally, while bot platforms are used to build AI agents, AI observability is used to assure their performance and safety once they are live and interacting with customers.
To qualify for inclusion in the Contact Center AI Observability category, a product must:
Enable automated, at-scale testing of customer-facing large language models (LLM) by simulating realistic user interactions, including edge cases and stressful conditions
Capture and provide turn-by-turn diagnostics for all live conversations between the AI and customers
Automate scoring or flagging of AI performance based on specific metrics like intent accuracy, factual hallucinations, and adherence to compliance or safety protocols
Diagnose the source of an interaction failure, distinguishing between a model error, a data integration issue, or a technical problem
Report on key performance indicators (KPI) specific to automation, such as containment rates, task completion success, and escalation trends, not just general call metrics
Have the capability to assess the quality and performance of the communication infrastructure (e.g., telephony, network carriers) that supports the contact center AI