Workflows used to mean japan whatsapp number data fixed paths: Click A, then B happens. One step led to another, like clockwork — predictable but inflexible.
Now, AI agentic workflows plan their own work, select tools, learn from mistakes, and adapt to changing conditions.
Andrew Ng, founder of Deeplearning.AI, finds this game-changing.
The business adoption rate is on the rise, too. With Gartner predicting that AI agents will be part of 33% of enterprise software apps, leading to 15% of day-to-day work done autonomously without human intervention, the question isn’t if you’ll use this technology, but when.
In this guide, we’ll discuss everything you need to know about AI-driven workflow automation.
What is an AI agentic workflow?
AI agentic workflow is a forwardlooking statements
sequential process that uses large language models (LLMs) to perform complex tasks with the help of AI agents. At their core, these agents combine generative AI’s cognitive abilities, natural language processing (NLP), and machine learning (ML). They make decisions based on context, learn from available data, communicate through plain language, and take specific actions to complete defined objectives.
Unlike standard automation, these workflows adapt as they run. They plan, assess progress, and change course when needed to complete tasks.
A quick look at how workflows have evolved
The concept traces back hong kong phone number to IBM’s MAPE control loop from the 1990s: monitoring, analysis, planning, and execution. Modern agentic workflows build on this foundation but with far more capability. Over the past few decades, workflows have undergone significant evolution. But here’s how it all began.
Traditional workflows operated like assembly lines. Each step happens in a fixed order with clear rules. Think of an expense report that moves from submission to manager approval to accounting in the exact same way every time. These systems can’t handle exceptions well and break when faced with unexpected situations.
AI workflows added intelligence to the process.
Instead of just following rules, these workflows use machine learning models to handle certain steps.