Token Telemetry

Token telemetry is the practice of measuring, analyzing, and exposing token usage across AI systems. It goes beyond counting how many tokens a prompt or completion consumes: good telemetry shows which agent, tool, customer, task, model, or workflow generated the cost. In agentic software, token telemetry becomes an operational signal. It reveals when context windows are close to overflowing, when prompts have grown too large, which steps trigger unnecessary model calls, and where caching, model routing, retrieval cleanup, or shorter tool outputs can reduce spend. Strong token telemetry connects cost with latency, quality, error rates, and business outcomes instead of treating token counts as an isolated metric. This gives teams a reliable basis for budgets, alerts, review gates, and capacity planning. It matters most in multi-agent setups, where parallel agents can create significant inference costs before anyone notices. In practice, token telemetry belongs in dashboards, logs, and deployment gates so AI workflows remain economical, observable, and controllable. It also acts as an early warning system: sudden token spikes often point to prompt loops, weak retrieval results, or missing stop criteria.