The central challenge for modern product teams is that agent-based software operates at a speed and scale that makes human oversight nearly impossible without sophisticated observability. Traditional analytics, which focus on page views and user funnels, fail to provide the context needed for agents. When an agent interacts with production databases or APIs, a 'session' might look healthy simply because tokens are moving, even if the agent is actively destroying data or failing to respect security boundaries.

The core shift in mindset required here is moving from tracking sessions to measuring 'delegated work'. This involves instrumenting agent runs with unique IDs and tracking every step—from initial intent to tool calls, permission checks, and final user acceptance. Engineering logs or 'traces' alone are not enough because they lack the product context: they show the 'how' of an execution but not the 'value' perceived by the end-user. If an agent performs a task, but the user rejects the output or interrupts the process, the failure must be flagged for the product team to refine the agent's prompt or tools.

The gap between completion rate and user acceptance is the most critical metric for evaluating AI product market fit. A high completion rate coupled with low acceptance suggests the agent is producing low-quality work that users feel forced to rewrite. Conversely, if completion is low but acceptance is high, the agent might be too conservative, which is a manageable bottleneck compared to the alternative. Ultimately, by tracking these metrics, teams can build a 'rudder' for their agents, moving from reactive debugging to proactive shaping of agent behavior.

Relying on chat logs for qualitative review is a necessary but non-scalable approach that ignores the underlying mechanics of tool calls and error retries. Organizations need to build standardized pipelines that ingest these agent-level telemetry events, treating user corrections as explicit labels for their training and evaluation datasets. This approach transforms product analytics from a passive reporting tool into an active, high-leverage component of the development lifecycle, ensuring that as agents take on more agency, the business remains in control of the outcomes.