AI Agents vs Workflows: When to Use Each

Jim Allen Wallace's exploration of AI agents versus workflows delves into the advantages and challenges of each approach in building systems with large language models (LLMs). Workflows, characterized by predetermined code paths and execution order, provide predictability, testability, and cost control, making them ideal for tasks with known, repeatable steps. In contrast, AI agents offer flexibility, allowing LLMs to determine execution paths at runtime, which is beneficial for tasks where steps are unclear or evolve based on input, though they present challenges in predictability and error management. Most production systems benefit from a hybrid approach, combining the reliability of workflows with the adaptability of agents. Wallace emphasizes the importance of infrastructure, such as memory management and real-time coordination, in supporting these systems, highlighting tools like Redis for their comprehensive offerings in managing memory and state layers efficiently.

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