COMPARATIVE APPROPRIATENESS OF AGENTIC VS. NON-AGENTIC AI SYSTEMS: TASK-FIT ANALYSIS FRAMEWORK

As artificial intelligence evolves at an unprecedented speed, practitioners must navigate an increasingly diverse set of AI system architecture options, with the most prominent decision now lying between established non-agentic designs and newly emerging agentic frameworks. Yet, clear guidance on how to choose between these paradigms remains limited, leaving teams to make high-stakes architectural decisions without relying on well-defined criteria. This uncertainty is further amplified by mounting pressure to adopt agentic AI, even when its suitability and implications for a given task are unclear. This paper addresses these challenges by introducing a Task-Fit Analysis Framework that systematically contrasts agentic and non-agentic AI systems, providing practitioners with evidence-based criteria for assessing which architecture is most appropriate for their use case. It evaluates these system types across five dimensions: autonomy and reasoning requirements, tolerance for non-determinism, ecosystem readiness, scalability considerations, and security implications. Drawing on recent research and analyses, the framework clarifies the trade-offs inherent to each paradigm, enabling more disciplined and well-informed AI system design decisions.