Failure-Mode Analysis, Hallucination Detection, and Prompt-Injection Testing: A Production-Readiness Framework for Enterprise Agentic AI

Abstract

As large language models increasingly move from experimental chat interfaces into enterprise workflows, the primary challenge is no longer model capability alone but production trustworthiness. Agentic AI systems introduce new risk surfaces because they can retrieve information, reason across context, call tools, act on user intent, and interact with external systems. This article proposes a practical production-readiness framework centered on three pillars: failure-mode analysis, hallucination detection, and prompt-injection testing. Failure-mode analysis helps teams identify where agentic systems can break under real operating conditions. Hallucination detection evaluates whether generated outputs remain faithful, factual, and grounded in approved sources. Prompt-injection testing examines whether malicious or conflicting instructions can override system intent, expose sensitive information, or trigger unauthorized actions. Together, these practices convert AI reliability from an informal review process into an evidence-driven engineering discipline. The article argues that enterprises should treat these controls as mandatory gates before deploying large language model and agentic AI systems into high-impact business workflows.