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The New Stack · September 20, 2026 · On the brief until October 4, 2026

Your AI agent failed. The model might not be the problem.

Computer ScienceEngineeringInformation Systems
THE AI ANGLE
Failing within broader software engineering environments and runtime workflows

Recent software engineering reports highlight that when AI agents fail, the underlying model itself may not be the root cause of the breakdown. Instead, operational success often hinges on surrounding system infrastructure, such as evaluation harnesses, runtime verification, retrieval engineering, and persistence mechanisms. For faculty, this points to the critical need to teach agentic AI as an end-to-end systems architecture challenge rather than focusing solely on model capabilities.

Summary written by AI Business Lens with an AI model from the article at thenewstack.io. It is not the article, and the publisher has not reviewed it. For publishers.

THE TEACHING ANGLE
Instructors can challenge students to evaluate whether agentic failures stem from model limitations or weaknesses in external scaffolding like runtime verification, execution harnesses, and persistence layers.

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