Agentic Workflow Orchestration is a cognitive architecture that leverages Large Language Models (LLMs) to create autonomous workflows. These workflows involve iterative processes where LLMs plan tasks, execute them, and self-correct based on feedback or outcomes.
It addresses the challenge of creating autonomous systems that can handle complex tasks without human intervention by enabling LLMs to learn from their actions and adapt accordingly.
The technology implements reflection patterns and tool-use loops to transition from zero-shot capabilities to iterative refinement. This allows the LLMs to continuously improve their output through a cycle of planning, execution, and correction.
Manufacturing is not directly involved as this technology focuses on software development and cognitive architectures rather than physical products.
The build process involves developing and training LLMs, integrating them with execution tools, and setting up feedback loops for continuous improvement. This includes defining the workflow structure, selecting appropriate LLM models, and designing the interaction between the model and external systems.
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