Agentic Workflow is an iterative system where an LLM doesn't just talk but acts like planning tasks, web search, code execution, data fetch, and self-correcting errors to achieve a complex goal without human intervention.
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Origin Story: The concept exploded with the "ReAct" paper published by Princeton & Google researchers.
Pitfall: If an agent makes one mistake early in the chain, it often doubles down on that mistake in subsequent steps.
Imagine you need to write an email
An Agentic Workflow is a system where an LLM is used as the reasoning engine to drive a multistep process autonomously.
Unlike a traditional linear script where a human hard-codes step A step B, an agentic workflow is dynamic. The AI analyzes the user's request, breaks it down into sub-tasks, and decides which tools to use and in what order to solve the problem.
The workflow usually requires four key components:
| Category | Tools |
|---|---|
| Orchestration | LangGraph, LangChain, AutoGen, CrewAI |
| LLMs | Anthropic Claude Haiku, Gemini Pro, GPT |
| No-Code Builder | Dify, N8N |
What community is saying.
I think AI agentic workflows will drive massive AI progress this year - perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it. - Andrew Ng
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