AI Agent Observability and Control: Building the New Monitoring Stack
AI agents are not single API calls; they are multi-step workflows that plan, fetch information, call tools, and synthesize outputs under uncertainty...
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AI agents are not single API calls; they are multi-step workflows that plan, fetch information, call tools, and synthesize outputs under uncertainty...
Telemetry refers to the automatic collection and transmission of data about how a system is operating, what it is doing, and what conditions it encounters. This data can include numerical measurements, events, error records, timing information, and traces of requests as they move through a system. Telemetry is the raw feed that powers monitoring and observability: without it, you would not have the evidence needed to detect problems or understand system behavior. It matters because it lets engineers and operators see performance trends, spot anomalies, and investigate incidents with concrete facts rather than guesses. Collecting telemetry requires choices about what to record, how often, and how much to keep, since too little data leaves blind spots while too much can overwhelm storage and analysis tools. There are also considerations around privacy and security, because telemetry can include sensitive information and must be protected. Well-designed telemetry pipelines make it easier to analyze data in real time or retrospectively, enabling faster fixes and better decisions about how to improve a system.