AI Tracing Turns Silent LLM Failures Into Debuggable Stories
AI tracing captures every step of an LLM or agent request—prompts, data retrieval, tool calls, and model outputs—so teams can spot hallucinations and contradictory answers that normal latency and HTTP metrics miss. Monte Carlo’s 2026 survey found only 47% of builders can trace failures end‑to‑end, underscoring the need for a standardized trace layer.