Autonom8
In my experience, Agentic Workflow is an innovative process in the AI landscape that interacts with Large Language Models (LLMs) to complete complex tasks with increased accuracy.
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Agentic Workflow breaks down one complex task into several small steps, allowing the model to consider your feedback at every step and produce output that’s over 41% more accurate than traditional methods.
Unlike the traditional approach, Agentic Workflow doesn’t give a single prompt to complete a task. Instead, it breaks down the task into smaller, manageable tasks, improving the quality and accuracy of the output.
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The three pillars of Agentic Workflow are AI Agents, Prompt Engineering, and Generative AI Networks. Each of these plays a crucial role in the successful execution of tasks.
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AI agents are the core of the Agentic workflow process. Each of them has their own personalities, roles and functions, making them capable of carrying out intended tasks with high efficiency.
Prompt Engineering techniques like chain of thoughts, planning and self-reflection are used in Agentic Workflow. These techniques enable the AI agents to break down complex tasks, plan and adjust task sequence, and self-reflect on their output.
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GAINs enable multi-agent collaboration in Agentic Workflow. By working together, these AI agents can tackle complex challenges in a more comprehensive and innovative way than any single AI could on its own.
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Agentic Workflow uses four common AI agent design patterns: Reflection, Tool use, Planning, and Multiagent collaboration. These patterns enhance the efficiency and accuracy of task execution.
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A case study showed that using Agentic Workflow, GPT-3.5 achieved an accuracy of 95.1% on a coding task, surpassing the accuracy of even GPT-4 on traditional prompting methods.
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Agentic Workflow is transforming the way we interact with LLMs, breaking down tasks into manageable steps and enabling continuous improvement throughout the task-completion process.