Observability is key to ensuring the reliability and governance of AI systems in the enterprise. Without the ability to observe how AI decisions are made and their impact on the business, trust in these systems can quickly erode.
To secure the future of enterprise AI, it’s essential to focus on outcomes rather than just the models themselves. By defining measurable business goals and designing telemetry around those outcomes, companies can ensure that their AI projects deliver tangible results.
Implementing a three-layer telemetry model for LLM observability, similar to the approach used for microservices, can provide a structured framework for ensuring accountability and transparency in AI systems. By focusing on prompts and context, policies and controls, and outcomes and feedback, companies can build a foundation of trust in their AI deployments.
