Tracing an AI Agent's Reasoning: Building Observability Into Your Pipeline (opens in new tab)

When an AI agent fails in production, it rarely fails loudly. It returns a confident, well-formatted, completely wrong answer and your monitoring shows green. The reason is that traditional observability was built for deterministic systems. Agents are not deterministic. They make branching decisions, call tools, pass intermediate state forward, and can corrupt context three steps ago in a way that silently breaks everything downstream. The fix is a structured tracing layer that captures not j...