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in the WAL file much faster, but performance still falls off with
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Agents execute at machine speed. If an agent goes rogue (or is hijacked via a prompt injection) and tries to enumerate valid reset tokens by observing timing differences in API responses or rapidly exfiltrate an entire users table by paginating through SELECT queries, a “security guard agent” that is asynchronously (and very expensively) evaluating agent behavior will not catch it in time. “AI defense” in practice should mean deploying ML models that monitor the behavioral exhaust of agentic workloads (query volume, token burn rate, iteration depth, unusual table access patterns). If the agent deviates from its bounded, purpose-based scope (i.e. it’s computed risk score is above a threshold for risk tolerance), the system should automatically sever its JIT access the millisecond the anomaly is detected.