The Invisible Cost of AI Agents: Loops, Token Waste, and Zero Visibility

Short summary

AI agents deployed in production often get stuck in infinite loops, burning millions of tokens with no way to trace why decisions went wrong. The author proposes a 'Flight Recorder' architecture that treats agent reasoning as a structured state graph—categorizing steps as Decisions, Hypotheses, Facts, and Dead Ends—enabling automatic failure isolation and plan revision. Without structured observability pipelines, agents remain expensive black boxes that cannot reliably self-correct before costs spiral.

• •Production AI agents frequently loop infinitely, wasting tokens with no causal traceability
• •Proposes a 'Flight Recorder' architecture modeling agent reasoning as a queryable state graph with Decisions, Hypotheses, Facts, and Dead Ends
• •Calls for pre-response evaluation gates and structured observability to catch failures before billing spikes

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