{"id":1312,"date":"2026-07-03T09:54:57","date_gmt":"2026-07-03T09:54:57","guid":{"rendered":"http:\/\/mycrochettips.com\/?p=1312"},"modified":"2026-07-09T13:09:15","modified_gmt":"2026-07-09T13:09:15","slug":"how-ai-agents-work-architecture-core-components-explained","status":"publish","type":"post","link":"http:\/\/mycrochettips.com\/index.php\/2026\/07\/03\/how-ai-agents-work-architecture-core-components-explained\/","title":{"rendered":"How AI Agents Work: Architecture &amp; Core Components Explained"},"content":{"rendered":"<p style=\"text-align: justify\">AI agents have emerged as major drivers of large-scale enterprise automation, with successful use cases having a noticeable impact. You must have noticed that everyone in the <a href=\"https:\/\/101blockchains.com\/artificial-intelligence\/\" target=\"_blank\" rel=\"noopener\" data-wpel-link=\"internal\">AI<\/a> space wants to find out how AI agent works and understand their architecture. The growing interest in AI agents stems from the fact that they are different from basic automation and <a href=\"https:\/\/101blockchains.com\/ai-chatbot-tutorial\/\" target=\"_blank\" rel=\"noopener\" data-wpel-link=\"internal\">AI chatbots<\/a>. AI agents bring the element of autonomy and are capable of perceiving the environment, reasoning, and taking relevant actions without human intervention.<\/p>\n<ul style=\"text-align: justify\">\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Insights from Salesforce reveal that around 44% of consumers in the US don\u2019t have any problem with using AI agents as personal assistants (<\/span><a href=\"https:\/\/www.salesforce.com\/news\/stories\/consumers-ready-for-ai-agents-research\/\" data-wpel-link=\"external\" rel=\"nofollow external noopener noreferrer\"><span style=\"font-weight: 400\">Source<\/span><\/a><span style=\"font-weight: 400\">).\u00a0\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">New research by CISCO states that agentic AI will manage 68% of customer service and support interactions by 2028 (<\/span><a href=\"https:\/\/newsroom.cisco.com\/c\/r\/newsroom\/en\/us\/a\/y2025\/m05\/agentic-ai-poised-to-handle-68-of-customer-service-and-support-interactions-by-2028.html\" data-wpel-link=\"external\" rel=\"nofollow external noopener noreferrer\"><span style=\"font-weight: 400\">Source<\/span><\/a><span style=\"font-weight: 400\">).\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Almost 93% of IT executives in the US are actively looking for opportunities to implement agentic AI in their business (<\/span><a href=\"https:\/\/www.uipath.com\/resources\/automation-analyst-reports\/agentic-ai-research-report\" data-wpel-link=\"external\" rel=\"nofollow external noopener noreferrer\"><span style=\"font-weight: 400\">Source<\/span><\/a><span style=\"font-weight: 400\">).\u00a0<\/span><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">You can see that businesses and individual users acknowledge the potential of AI agents, thereby driving adoption of agentic AI. However, the reality paints a different picture as many companies are not prepared for the autonomous intelligence that comes with AI agents. This is one of the prominent reasons for which you need in-depth understanding of the architecture of AI agents and core principles that drive them. Familiarity with agentic AI architecture and the key components in AI agent systems will empower you with the confidence to adopt AI agents.\u00a0<\/span><\/p>\n<h2><b>Understanding How an AI Agent Works\u00a0<\/b><\/h2>\n<p style=\"text-align: justify\">The first thing on your mind right now must be the way in which AI agents work to provide the benefits of autonomous automation. You can pick any one of the AI agent examples and find out their utility as autonomous software systems tailored to achieve specific goals. AI agents are not designed to answer to your prompts only and they have the capabilities to take decisions on the next course of action.<\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">Contrary to traditional <a href=\"https:\/\/101blockchains.com\/best-ai-tools\/\" target=\"_blank\" rel=\"noopener\" data-wpel-link=\"internal\">AI tools<\/a> and systems, AI agents can,<\/span><\/p>\n<ul style=\"text-align: justify\">\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Work to achieve a specific objective.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Leverage different tools, including databases and APIs.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Retain context from previous interactions.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Adjust their actions on the basis of results.<\/span><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">How can AI agents do all these things? A high-level overview of the working mechanism of AI agents reveals that they work in a continuously running loop. Within the loop, AI agents observe information, implement reasoning to determine their next step, and take action on their own. On top of it, AI agents also learn from the outcomes before repeating the loop again.\u00a0<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">You can think of an AI-powered human assistant as the simplest example to understand the working of AI agents. When you ask the assistant for help, it will observe your request and uses reasoning to prepare plans for the next task. The assistant will use tools to take action on your request, such as sending emails. Based on your feedback, the assistant will make adjustments to perform the request better in the next iteration.<\/span><\/p>\n<blockquote>\n<p>Get\u00a0<a href=\"https:\/\/101blockchains.com\/certification\/certified-ai-agents-manager\/\" target=\"_blank\" rel=\"noopener\" data-wpel-link=\"internal\">Certified AI Agents Manager (CAIAM)<img src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" alt=\"\u2122\" class=\"wp-smiley lazyload\" style=\"height: 1em;max-height: 1em\" data-src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/72x72\/2122.png\"><\/a>\u00a0Certified \u2014 Gain in-demand skills to manage agentic AI workflows across the full AI agent lifecycle and lead the future of intelligent automation<\/p>\n<\/blockquote>\n<h3><b>Unraveling the Core Principles Driving AI Agents<\/b><\/h3>\n<p style=\"text-align: justify\">Agentic AI leverages a set of specific principles that defines AI agent behavior and how they operate and interact with each other. You can find the answers to \u201cWhat does AI agent work?\u201d by identifying the core principles that serve as building blocks of agentic AI architectures. Learning about the core principles of AI agent systems can help you easily understand the layers in agentic AI architecture.<\/p>\n<ul>\n<li>\n<h4><b>Autonomy\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">AI agents can work with complete autonomy without depending on constant human intervention.<\/span><\/p>\n<ul>\n<li>\n<h4><b>Goal-centric Behavior<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The working of every AI agent revolves around the objectives it has been designed to achieve. AI agents pursue their goals and evaluate how their actions will help in achieving the specified goals.<\/span><\/p>\n<ul>\n<li>\n<h4><b>Perception\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The ability of AI agents to perceive the environment around them empowers them to interact with their environments. AI agents can collect data about their environment from sensors or other digital inputs and external systems.<\/span><\/p>\n<ul>\n<li>\n<h4><b>Rationality\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">You must know that AI agents have reasoning capabilities, which make them rational entities. AI agents can combine data from the environment with context retained from past conversations and domain knowledge to take decisions.\u00a0<\/span><\/p>\n<ul>\n<li>\n<h4><b>Proactivity\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">AI agents don\u2019t react to inputs and have the capability to take initiative on the basis of forecasts and models for future states. Rather than reacting to events, AI agents can anticipate changes and respond accordingly.\u00a0<\/span><\/p>\n<ul>\n<li>\n<h4><b>Continuous Learning<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\">The most prominent highlight in AI agent architecture draws attention towards the ability of AI agents to learn from past interactions and improve continuously. AI agents identify different patterns, outcomes and feedback to optimize their decision-making and behavior, something you won\u2019t find in static tools.<\/p>\n<ul>\n<li>\n<h4><b>Adaptability\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The core principle of adaptability in AI agents makes them capable of adjusting their strategies as responses to new events. Flexibility of AI agents is an unavoidable requirement to manage uncertainty, incomplete information or completely new situations.<\/span><\/p>\n<ul>\n<li>\n<h4><b>Collaboration\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">AI agents can also work with human agents and other AI agents to achieve the same goals. In multi-agent systems, AI agents can communicate with each other and ensure coordination to perform different tasks in unison.<\/span><\/p>\n<blockquote>\n<p>Enroll now in the\u00a0<a href=\"https:\/\/101blockchains.com\/course\/mastering-generative-ai-with-llms\/\" target=\"_blank\" rel=\"noopener\" data-wpel-link=\"internal\">Mastering Generative AI with LLMs Course<\/a>\u00a0to discover the different ways of using generative AI models to solve real-world problems.<\/p>\n<\/blockquote>\n<h3><b>What are the Components in Agentic AI Architecture?<\/b><\/h3>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The best way to learn about the architecture of AI agents will require an understanding of the different components. You can pick the three-tier intelligence model to understand how enterprises can build and scale up agentic systems.\u00a0<\/span><\/p>\n<h4><b>1. Foundation Tier<\/b><\/h4>\n<p style=\"text-align: justify\">The first layer of AI agent components is the foundation tier, which defines the core intelligence base of the system. You will find two crucial components in the foundation tier: the state &amp; memory component and the knowledge layer.<\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The state component tracks the goals that an agent pursues, the actions it takes, dependencies, and the outcomes. As a result, the agent always has a context to act with rather than starting from scratch for everything.<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The memory component provides continuity with agents relying on two types of memory, short and long. Short-term memory is essential to maintain the flow during a specific task or conversation. On the other hand, long-term memory offers durable knowledge, which you can find in examples of business rules or customer history.<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">AI agents leverage the knowledge layer in the foundation tier to gain access to domain context and enterprise data. The notable tools used in this layer are RAG, vector databases, and enterprise search. The knowledge layer combines structured and unstructured information to create a shared context for AI agent reasoning.<\/span><\/p>\n<blockquote>\n<p>Unlock your potential with the\u00a0<a href=\"https:\/\/101blockchains.com\/certification\/certified-ai-professional\/\" target=\"_blank\" rel=\"noopener\" data-wpel-link=\"internal\">Certified AI Professional (CAIP)<img src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" alt=\"\u2122\" class=\"wp-smiley lazyload\" style=\"height: 1em;max-height: 1em\" data-src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/72x72\/2122.png\"> Certification<\/a>. Gain expert-led training and the skills to excel in today\u2019s AI-driven world.<\/p>\n<\/blockquote>\n<h4><b>2. Workflow Tier<\/b><\/h4>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The workflow tier transforms the understanding developed in the foundation tier into action. You must know that components in the workflow tier determine how different agents will work together, manage sequencing, and ensure that agents work on the right tasks. The two notable components in the workflow tier are the planner and orchestrator.<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The planner in the workflow tier of agentic AI architecture breaks complex business goals into smaller tasks. It primarily focuses on designing dependencies, sequencing tasks, and determining what should happen with clear explanation of all agentic actions.\u00a0<\/span><\/p>\n<p style=\"text-align: justify\">The orchestrator plays a major role in how an AI agent works by deciding which agents should perform a specific task. In addition, the orchestrator also determines how results can be combined to offer a clear outcome. The other responsibilities of the orchestrator revolve around routing tasks on the basis of complexity, monitoring progress, ensuring smoother handoffs, and resolving conflicts.<\/p>\n<h4><b>3. Autonomous Tier<\/b><\/h4>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The final layer of components in agentic architecture is the autonomous tier, which primarily deals with actions. You will find two core components in this layer: the AI agents and tools and APIs used by agents.\u00a0<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The AI agents work as the core components in the agentic framework with their autonomous reasoning and capabilities to use the right tools and APIs. Even though they work independently, the orchestrator and planner guide the actions of AI agents.<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The utility of AI agents depends significantly on the ability to interact with enterprise systems. This is where APIs help agents in triggering transactions, updating workflows, fetching data, and connect with different enterprise systems. AI agents also use other tools to perform tangible actions and showcase enterprise readiness.<\/span><\/p>\n<h3><b>Final Thoughts\u00a0<\/b><\/h3>\n<p style=\"text-align: justify\"><span style=\"font-weight: 400\">The overview of key principles and core components in the architecture of AI agents reveals that agents don\u2019t work alone. If the hype around autonomous reasoning and decision-making capabilities of AI agents is growing, then it is possible due to the components underlying agentic architectures. You can clearly notice that the core principles of agentic AI provide the ideal foundation for long-term adoption of AI agents. With comprehensive understanding of agentic AI architecture and related components, you can find the ideal roadmap to adopt AI agents for your business. Learn more about agentic AI and how it works now.<\/span><\/p>\n<p>The post <a href=\"https:\/\/101blockchains.com\/ai-agents-work\/\" data-wpel-link=\"internal\">How AI Agents Work: Architecture &#038; Core Components Explained<\/a> appeared first on <a href=\"https:\/\/101blockchains.com\" data-wpel-link=\"internal\">101 Blockchains<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI agents have emerged as major drivers of large-scale enterprise automation, with successful use cases having a noticeable impact. You must have noticed that everyone in the AI space wants to find out how AI agent works and understand their architecture. The growing interest in AI agents stems from the&#8230;<\/p>\n<p><a class=\"more-link\" href=\"http:\/\/mycrochettips.com\/index.php\/2026\/07\/03\/how-ai-agents-work-architecture-core-components-explained\/\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[14],"tags":[],"_links":{"self":[{"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/posts\/1312"}],"collection":[{"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/comments?post=1312"}],"version-history":[{"count":1,"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/posts\/1312\/revisions"}],"predecessor-version":[{"id":1313,"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/posts\/1312\/revisions\/1313"}],"wp:attachment":[{"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/media?parent=1312"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/categories?post=1312"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/mycrochettips.com\/index.php\/wp-json\/wp\/v2\/tags?post=1312"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}