Is AI bringing application observability and behavior tracking together? (www.rudderstack.com)

AI-driven, agentic applications are collapsing the long-standing divide between infrastructure observability and user behavior tracking by making user intent itself a first-class signal. Instead of separate stacks—Datadog/Grafana/OpenTelemetry for traces, metrics and logs, and RudderStack/Segment/Amplitude for clickstreams and cohorts—conversational inputs become the “ground truth” of what users want and how the system executed. That shift forces a new category: intent observability, which asks not just “Did the server fail?” but “Did the agent understand and fulfill the user’s intent?” It also expands monitoring to include model cost, latency, reliability, and A/B testing of prompts and models. Practically, this means a unified trace that merges logs, semantic user events, embeddings and execution traces so teams can trace journeys, build intent-based segments, trigger activation loops (e.g., follow-ups on failed requests), and even support user-level billing for model usage. Early signals include OpenTelemetry moving beyond infra metrics to business events, analytics tools using LLMs for session understanding, and startups integrating embeddings, traces and feedback loops. The implication: observability and product analytics converge—DevOps, product and data teams will likely share dashboards and KPIs as platforms evolve or new AI-native stacks emerge that natively capture intent + execution.