From chatbots, to coding copilots, to AI agents, generative AI-powered apps are seeing increased traction among enterprises. As they go mainstream, however, their shortcomings are becoming more clear and problematic. Incomplete, offensive, or wildly inaccurate responses (aka hallucinations), security vulnerabilities, and disappointingly generic responses can be roadblocks to deploying AI — and for good reason.
In the same way that cloud-based platforms and applications gave birth to new tools designed to evaluate, debug, and monitor those services, the proliferation of AI requires its own set of dedicated observability tools. AI-powered applications are becoming too important to treat as interesting but unreliable test cases — they must be managed with the same rigor as any other business-critical application. In other words, AI needs observability.
What is AI observability?
Observability refers to the technologies and business practices used to understand the complete state of a technical system, platform, or application. For AI-powered applications specifically, observability means understanding all aspects of the system, from end to end. Observability helps companies evaluate and monitor the quality of inputs, outputs, and intermediate results of applications based on large language models (LLMs), and can help to flag and diagnose hallucinations, bias, and toxicity, as well as performance and cost issues.
