Agentic Ai Architectures And Design Patterns Medium

Bonisiwe Shabane

-Oct 23, 2025, 3:40 PM

agentic ai architectures and design patterns medium

“Autonomy is not just about intelligence—it’s about intent, memory, and coordination.” We are entering an era where software isn’t just reactive—it’s proactive, collaborative, and increasingly autonomous. This shift is being driven by Agentic AI: systems composed of intelligent agents that can reason, plan, remember, use tools, and interact with each other to accomplish complex goals.Agentic AI represents a paradigm shift... At its core is the concept of agency: the capacity for independent reasoning, planning, and action that transcends traditional automation. For a deeper dive into the meaning of "agent," "agency," and "agentic," See my companion article here. For an AI system to be considered agentic, it must exhibit four foundational traits:

Autonomous decision-making: The ability to analyze situations and act independently without explicit instructions for each step This document provides guidance to help you choose a design pattern for your agentic AI system. Agent design patterns are common architectural approaches to build agentic applications. An agent design pattern offers a distinct framework for organizing a system's components, integrating the model, and orchestrating a single agent or multiple agents to accomplish a workflow. AI agents are effective for applications that solve open-ended problems, which might require autonomous decision-making and complex multi-step workflow management. Agents excel at solving problems in real-time by using external data and they excel at automating knowledge-intensive tasks.

AI agents are suitable when you need AI to complete goal-focused tasks with some degree of autonomy. For other use cases, you can use assistive and generative AI applications. To learn about the differences between AI agents and non-agentic AI applications, see What is the difference between AI agents, AI assistants, and bots? This guide assumes that you have a foundational knowledge of agentic AI systems and how their architecture differs from that of non-agentic systems, such as those that use direct model reasoning or retrieval-augmented generation... For a summary of the agent pattern guidance, see the compare design patterns section later in this document. The following are the high-level steps to choose a design pattern for your agentic AI system.

These steps are described in detail later in this document. 1:19 pm November 21, 2025 By Julian Horsey What if you could master the future of artificial intelligence in just 30 minutes? Sounds impossible, right? Yet, as AI continues to transform industries, the demand for systems that can think, plan, and act autonomously has never been higher. Enter Agentic AI, a innovative approach that enables machines to tackle complex, multi-step tasks with minimal human input.

From automating customer support to crafting entire marketing strategies, these systems are reshaping what’s possible. In this condensed guide, we’ll unpack the core insights of an 8-hour Agentic AI course, offering you a fast track to understanding the tools and techniques driving this transformation. Whether you’re a tech enthusiast or a curious professional, this perspective will challenge how you think about AI’s role in the modern world. By the end of this 30 minute video guide by Tina Huang, you’ll uncover the essential building blocks of Agentic AI, including how large language models (LLMs), external tools, and evaluation mechanisms come together... You’ll also explore innovative design patterns, like the Reflection Pattern and Multi-Agent Systems, that enable these agents to solve problems with creativity and precision. But this isn’t just about theory; it’s about real-world applications and the challenges of balancing autonomy with control.

So, if you’re ready to rethink what AI can do and how you can harness it, let’s unravel the possibilities together. After all, the future of AI isn’t just about machines, it’s about how we shape them. Agentic AI represents a class of systems designed to perform multi-step workflows, ranging from straightforward, rule-based tasks to highly autonomous operations that integrate external tools and APIs. These systems excel in tasks such as: The autonomy of Agentic AI systems exists on a spectrum. On one end, workflows are rigid and follow predefined rules, while on the other, they are dynamic and capable of independent decision-making.

By combining modularity with automation, these systems outperform traditional AI models in both speed and efficiency. For example, instead of relying solely on a single language model, Agentic AI integrates multiple components, such as APIs, databases, and external tools, to deliver more robust and context-aware results. For the last few years, most of the progress in AI has been tied to size. Bigger models, bigger datasets, bigger everything. And sure, that brought us a long way. But as we head into 2026, it feels like we’ve hit a point of diminishing returns.

Models keep getting larger and demo videos keep getting flashier, but that doesn’t translate into real operational value for most companies. The gap between “cool prototype” and “this actually runs our business” is still too wide. What is starting to move that line is the shift toward agentic AI. Instead of waiting for a prompt and producing a single answer, these systems operate more like persistent software components that chase a goal, react to new information, and adjust as they go. It’s a very different mindset from what we’ve been building toward over the last decade, and it requires us to rethink the architecture around AI – not just the models themselves. Generative AI changed how people interact with computers, but the loop hasn’t changed much.

You ask, it answers, and the conversation resets. Agentic systems don’t behave that way. They take in live data, watch for changes, make decisions, and revise them if things don’t play out as expected. Think of problems that don’t fit neatly into a single step: customer journeys that unfold over days or weeks, inventory levels that fluctuate by the hour, fraud patterns that evolve in real time. These aren’t “give me an answer once and I’m done” problems. They’re ongoing loops.

The surprising part is that the bottleneck isn’t the model. It’s the architecture around it. If an agent doesn’t have the right data, or the data doesn’t agree across systems, the agent ends up making the wrong call, quickly and confidently.

Agentic Ai Architectures And Design Patterns Medium

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