The original headline is: "The 7 Ways AI Agents Fail in Production — And How to Catch Them"

Short summary

AI agents fail predictably in production through seven patterns: infinite loops burning tokens silently, context degradation reducing output quality without error signals, model-task mismatches inflating costs, secrets leaking to logs, multi-agent deadlocks, and gradual task drift. Detect each via specific metrics: loop detection, context pressure tracking, cost anomaly detection, entropy analysis, dependency graphs, and objective verification. Fixes include gentle nudges, context compression, mid-session model escalation, and circuit-breaking for security breaches.

• •Seven predictable failure modes: loops, context degradation, model-task mismatches, secret leaks, deadlocks, task drift
• •Each requires specific detection: loop detection, context pressure, cost anomalies, entropy analysis, dependency tracking, objective verification
• •Fixes range from gentle nudges and compression to mid-session escalation and circuit-breaking for security issues

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