How to Prevent Your AI Agent from Burning $50 in a Loop

If you've built AI agents with LangChain, MCP, or the OpenAI Agents SDK, you've probably had this experience: your agent works great 90% of the time. The other 10%, it goes haywire — retrying the same failing API call endlessly, stuck in a reasoning loop, or burning through API credits with increasingly verbose prompts. The scary part? Each individual step looks perfectly reasonable. It's only when you look at the sequence over time that the problem becomes obvious. The Problem: Temporal Blindness Current tools for AI agent reliability fall into two categories: Observability tools (LangSmith, Braintrust, Langfuse) show you beautiful traces and dashboards — after the damage is done. By the time you see the trace of your agent calling the same API 47 times, you've already burned $50. Static guardrails (Guardrails AI, NeMo Guardrails) validate individual inputs and outputs. They can catch PII in prompts or malformed JSON in responses. But they can't detect patterns over time — they see ea