AI Agents in Production Burn Tokens Fast — Here's Why Visibility Matters

Deploying AI agents beyond local demos often exposes a critical problem: agents can enter infinite loops, consuming millions of tokens and generating massive costs within minutes. Unlike traditional software, AI agents lack built-in debugging tools such as stack traces or breakpoints, making it difficult to pinpoint where reasoning went wrong. Developers are increasingly advocating for a 'flight recorder' architecture that models agent execution as a structured state graph, categorising each step into decisions, hypotheses, facts, and dead ends. This approach allows systems to trace failures back to their root cause and force the agent to revise its plan rather than continue guessing. Experts argue that without structured observability pipelines, AI agents remain costly and unreliable tools unfit for stable production environments.