Agentic Ai Architecture Geeksforgeeks

Bonisiwe Shabane

-Oct 23, 2025, 3:31 PM

Agentic AI systems offer a new way of using artificial intelligence allowing them to act on their own and pursue goals in changing environments. Unlike traditional AI which depends on human input, agentic AI can observe, reason, learn and make decisions independently without needing specific instructions. There are several types of agentic architectures each with its own strengths and weaknesses, suitable for different tasks and environments. Some common types include: 1. Single-agent architecture: A single AI system that functions independently, making decisions and taking actions without the involvement of other agents.

2. Multi-agent architecture: This architecture involves multiple AI systems interacting with each other, collaborating and coordinating their actions to achieve common goals. Its sub types are: The architecture of an agentic AI system is composed of several key components that work together to ensure it operates independently and effectively. These components enable the system to make decisions, adapt to new information and learn from past experiences. Agentic AI is a branch of artificial intelligence focused on building autonomous, intelligent agents capable of making decisions, interacting with other agents and completing complex tasks with minimal human intervention.

It combines Generative AI, LLMs, multi-agent systems, memory and workflow orchestration to build advanced AI applications. This Agentic AI tutorial offers a comprehensive guide to core concepts, frameworks, tools and practical projects to master agentic systems. Strong programming skills are important for developing autonomous agents. This section introduces key programming tools, libraries and frameworks required for developing agentic AI application. Generative AI empowers agents to produce text, code and actions autonomously. Understanding LLMs is critical for applying GenAI inside agentic systems.

Prompt engineering is the practice of crafting inputs to get better outputs from LLMs. Agentic AI is a type of AI designed to excel in one specific area rather than trying to know a bit about everything. Unlike General AI, which has knowledge across many domains but may not be an expert in any single one, Agentic AI is highly skilled in a focused task. For example, a travel-planning Agentic AI won’t just give us flight options rather it can search multiple platforms, compare prices and book tickets automatically. Agentic AI systems operate through various steps such as: Let's see the key differences between traditional AI and Agentic AI,

The potential applications of Agentic AI are vast and varied. Here are some few examples: AI Agent Architectures examine the complex structures that shape how machines perceive, reason, and act in their environments in the pursuit of autonomous intelligence. This article explores the various structures that shape AI's decision-making capabilities An intelligent agent system's basic components and interactions are outlined in an AI agent architecture, which functions as a conceptual design. It offers a methodical framework for creating, putting into practice, and comprehending agents that may independently interact with their surroundings to accomplish predetermined goals.

Agent architectures are important because they provide a methodical way to design and evaluate complex AI systems, enabling scientists and engineers to build agents with certain features and functions. Let's discuss each component of AI Agent architecture in detail: This breakdown provides a detailed explanation of each component of the AI agent architecture along with examples illustrating their functionalities and applications. AI agent architectures offer a organized system for comprehending how smart systems independently perceive, reason, and make decisions in their surroundings. The components talked about - profiling module, memory module, planning module, action module, and learning strategies - all work together to allow agents to engage with their environment, make choices, and adjust as needed. AI agents can reach higher levels of autonomy and intelligence by combining sensory skills, knowledge storage, decision-making abilities, action execution, and learning mechanisms.

These structures act as crucial models for creating and assessing intricate AI systems, enabling researchers and engineers to create agents that can effectively complete particular tasks and objectives. Building AI agents means creating software that can perform tasks intelligently and autonomously. This involves defining what the agent should do, training it with data and using technologies like machine learning and natural language processing. The goal is to make the agent capable of understanding instructions, learning from experience and improving its performance over time. Anthropic identifies five foundational patterns for constructing AI agents. AI agents have become the backbone of complex task automation, decision-making and workflow management across industries.

These frameworks helps developers and businesses to build intelligent agents that operate autonomously, coordinate tasks and integrate with a wide range of tools and platforms. In this article we will see different AI Agent Frameworks to make our own agents. LangGraph is designed for building and managing advanced autonomous agents capable of handling independent workflows. Its architecture is inspired by directed acyclic graphs (DAGs) which helps in the execution of complex, branching processes such as starting a chatbot session, continuing the conversation and ending it upon achieving goals. Use Case: Automating support ticket where agent receives input, gathers context and autonomously routes and resolves queries, escalating only when necessary. Let see a code example to understand better, the working of code is explained below:

AutoGen is a Microsoft-backed multi-agent framework designed for enabling rich, dynamic conversations and automation workflows among AI agents, humans and tools. It emphasizes flexibility, configurability and seamless integration with both large language models (LLMs) and external APIs. In today's fast-paced digital landscape, the intersection of Artificial Intelligence (AI) and microservices architecture is reshaping how applications are built and deployed. Microservices offer flexibility and scalability, making them ideal for AI applications, which often require robust infrastructure to handle complex computations and large datasets. This article explores how microservices can enhance AI applications, promoting scalability, flexibility, and efficiency. Microservices architecture is a design approach where an application is structured as a collection of loosely coupled services.

Each service is self-contained, performing a specific business function, and can be developed, deployed, and scaled independently. This modularity facilitates faster development cycles and greater resilience. Using microservices for AI applications brings many benefits, such as better scalability and flexibility. This approach allows teams to work on different components simultaneously, leading to faster development and easier maintenance. When designing AI applications using microservices, it's essential to identify distinct functionalities, such as data ingestion, processing, model training, and serving. This modular approach allows developers to focus on one aspect at a time.

Effective communication between AI microservices is vital. Options include:

Agentic Ai Architecture Geeksforgeeks

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