{"id":1405,"date":"2025-10-11T20:01:43","date_gmt":"2025-10-11T20:01:43","guid":{"rendered":"https:\/\/dataforma.tech\/?p=1405"},"modified":"2025-10-11T20:01:44","modified_gmt":"2025-10-11T20:01:44","slug":"implementacao-de-ia-agentica-guia-pratico-para-workflows-personalizados","status":"publish","type":"post","link":"https:\/\/dataforma.tech\/en\/blog\/implementacao-de-ia-agentica-guia-pratico-para-workflows-personalizados\/","title":{"rendered":"Implementing Agentic AI: A Practical Guide to Custom Workflows"},"content":{"rendered":"
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Did you know that the agentic AI market is expected to jump from US$1.5 billion in 2024 to a staggering US$1.5 billion by 2030?1<\/a> These numbers are impressive, but what is really happening behind this expansion?<\/p>\n\n\n\n

Today, we have a new form of artificial intelligence that goes beyond what we're used to seeing. Unlike traditional AI, which simply responds to our requests, agentic AI makes its own decisions and performs tasks automatically, without needing detailed instructions for each step.2<\/a>3<\/a>It's as if she thinks for herself \u2013 although we know that's not quite the case.<\/p>\n\n\n\n

You can look around and you'll notice that organizations have already started using these AI virtual agents in a variety of ways\u2014from simple tools that enhance what we already do to systems that automate entire workflows.4<\/a>The interesting thing about these systems is that they can learn from their experiences and create multi-step plans to solve complex problems.5<\/a>.<\/p>\n\n\n\n

Key characteristics include autonomy, goal orientation, adaptability, ability to improve themselves, and interactivity3<\/a>But the question remains: how can we use this in practice?<\/p>\n\n\n\n

In this guide, you'll discover concrete examples of how to implement these technologies, strategies for dealing with the challenges that will arise along the way, and a complete roadmap for creating custom workflows that can transform the way your business operates.<\/p>\n\n\n\n

Fundamental Concepts of Agentic AI and Personalized Workflows<\/h2>\n\n\n\n
\"Diagram<\/figure>\n\n\n\n

Image Source: Simform<\/a><\/sub><\/p>\n\n\n\n

Agentic AI represents a fundamental shift in how we think about artificial intelligence. To truly understand what it offers, we must first understand how it differs from what we already know.<\/p>\n\n\n\n

Difference Between Agentic AI and Traditional AI<\/h3>\n\n\n\n

When we interact with traditional AI, we're essentially talking to something that only responds to what we ask. It operates under a reactive model, responding to specific commands or analyzing data based on instructions someone programmed in advance.6<\/a>It's like having an assistant who only does exactly what you tell him to do, without taking any initiative of his own.<\/p>\n\n\n\n

Agentic AI works completely differently. It's proactive, taking initiative based on its analysis of complex environments. While generative AI focuses on creating content based on learned patterns, agentic AI goes further\u2014it applies these findings to achieve specific goals.6<\/a>.<\/p>\n\n\n\n

The main difference is in the autonomy and decision-making capacity<\/a><\/strong>Traditional systems rely on algorithms or rules that someone has previously defined, requiring constant human oversight. Agentic AI, on the other hand, demonstrates autonomous behavior, can assess situations, and determine the path forward with little or no human input.6<\/a>.<\/p>\n\n\n\n

The interesting thing is that it can learn and operate on its own, optimizing workflows and executing complex tasks without needing someone to control every step. This way, it can autonomously manage business processes, such as reordering supplies or optimizing supply chain operations.6<\/a>.<\/p>\n\n\n\n

What are autonomous agents and how do they work?<\/h3>\n\n\n\n

Autonomous agents are AI systems designed to perform tasks independently<\/a>, without needing someone to hold their hand all the time. They make decisions, plan actions, and adapt to different situations to achieve specific goals.7<\/a>.<\/p>\n\n\n\n

The operation of these agents follows a four-step process that works like this:<\/p>\n\n\n\n

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  1. Perception<\/strong>: Collect and process data from the environment \u2013 sensors, databases, user interfaces8<\/a>.<\/li>\n\n\n\n
  2. Reasoning<\/strong>: Analyze collected data to understand the context and identify possible solutions, often using a Large Language Model (LLM) as an orchestrator6<\/a>.<\/li>\n\n\n\n
  3. Action<\/strong>: Take concrete action based on analysis, performing tasks, making decisions, or interacting with other systems8<\/a>.<\/li>\n\n\n\n
  4. Apprenticeship<\/strong>: Continuously improve through the feedback received, refining their future actions8<\/a>.<\/li>\n<\/ol>\n\n\n\n

    These agents use advanced techniques such as natural language processing, machine learning, and real-time data analysis. Key features include autonomy, adaptability, interactivity, and continuous learning.9<\/a>.<\/p>\n\n\n\n

    Overview of agentic workflows<\/h3>\n\n\n\n

    Agentic workflows are AI-driven systems that operate autonomously, adapt to changing conditions, and perform tasks intelligently without constant human supervision.10<\/a>. They represent sequences of well-defined work dynamically executed by AI agents as part of a complete business process automation11<\/a>.<\/p>\n\n\n\n

    Here it is important to understand that these workflows are based on three fundamental pillars:<\/p>\n\n\n\n