The Black Box Problem: Why Autonomous AI Agents Need Production-Grade Observability

As AI agents become more sophisticated with self-optimization loops and dynamic prompting, traditional application monitoring tools fall short in capturing critical operational metrics—including token consumption, per-step costs, and latency bottlenecks. Organizations deploying autonomous agents in production face blind spots that lead to runaway costs and degraded performance, making purpose-built telemetry layers essential for maintaining control and enabling agents to self-improve through cost and performance feedback signals.