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Design decisions can affect the reliability, scalability, maintainability, and operational visibility of AI agents. Understanding key design considerations can help teams create agents that are easier to manage, monitor, and evolve over time. This includes recognizing anti-patterns which are common design approaches that may seem beneficial initially but lead to problems such as poor performance, difficult maintenance, or operational challenges.
Most agent failures are not model failures, they are failures in architecture, control, and data design

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Foundational architecture considerations Tooling and scalability considerations Knowledge and document processing considerations