Production AI systems fail in predictable patterns. Understanding these failure modes is the first step to building reliable systems.
**1. Hallucinations (Confident Wrong)** The model generates plausible but incorrect information. Common in RAG systems with poor retrieval or insufficient grounding.
Fix: Citation requirements, factuality evaluators, confidence thresholds.
**2. Tool Misuse** Agents call tools incorrectly, with wrong parameters, or at inappropriate times.
Fix: Tool boundary specifications, input validation, execution sandboxing.
**3. Policy Drift** The model gradually deviates from intended behavior as context accumulates or prompts evolve.
Fix: Regular eval runs against policy test suites, prompt versioning, drift detection alerts.
**4. Escalation Loops** The system escalates to humans too often (defeating automation) or too rarely (missing critical issues).
Fix: Calibrated confidence thresholds, escalation analytics, feedback loops from human reviewers.
**5. Prompt Injection** Malicious inputs manipulate the model into unintended behavior.
Fix: Input sanitization, adversarial testing, output guardrails.
Map your failure modes, measure their frequency, and prioritize fixes by business impact.