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This article proposes a hybrid architectural pattern that integrates predefined workflows with adaptive agentic behavior specifically for handling uncertainty in stages like hyperparameter tuning. By placing an agent within a fixed workflow sequence, developers can maintain overall control while allowing the system to dynamically select actions when intermediate results are unknown. This approach offers a practical framework for building robust LLM applications without sacrificing structural predictability or requiring full autonomous decision-making throughout every step.

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Put the Agent Inside the Workflow

Towards Data ScienceAugust 1, 20266 min read

A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior The post Put the Agent Inside the Workflow appeared first on Towards Data Science.

Workflow or agent? Workflow or agent? The workflow paradigm follows a sequence we define in advance. This makes the application very easy to understand, and gives us clear control over how information moves from one stage to the next. It works well when we know which operation should happen at each stage. For more open-ended questions, however, the next useful action may depend on what the system discovers along the way. The agent paradigm, on the other hand, starts with a goal and decides which actions or tools to use along the way. This makes it more flexible when the solution path is uncertain. However,…

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