Ai Agent Wikipedia

Bonisiwe Shabane

-Dec 28, 2025, 8:47 AM

In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex... Agentic AI tools prioritize decision-making over content creation and do not require human prompts or continuous oversight.[1] AI agents possess several key attributes, including complex goal structures, natural language interfaces, the capacity to act independently of user supervision, and the integration of software tools or planning systems.[2] Their control flow is... Researchers and commentators have noted that AI agents do not have a standard definition.[3][5][6][7] The concept of agentic AI has been compared to the fictional character J.A.R.V.I.S..[8] A common application of AI agents is the automation of tasks—for example, booking travel plans based on a user's prompted request.[9][10] Prominent examples include Devin AI, AutoGPT, and SIMA.[11] Further examples of agents released... Companies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents.[21]

AI agents are autonomous intelligent software components that form the foundation of artificial intelligence (AI) systems. Agents are designed to perform specific tasks independently without the need for human intervention. Intelligent agents are conversational and can interact with other systems, such as applications and APIs, access data, perceive specific environments, exercise reasoning, make decisions, take actions to achieve defined goals and learn from prior... These capabilities help organizations become more productive by delegating repetitive and mundane tasks to these AI agents and freeing human resources for more complex and strategic activities. AI agents use machine learning (ML) and techniques such as natural language processing (NLP) to take on a range of tasks, from simple queries to complex problem-solving. Unlike traditional AI, AI agents can self-learn and continuously improve their performance.

These agents follow a cycle of perception, reasoning and action or outcome. This is sometimes expressed as sensing, thinking and acting. An agent's workflow typically defines the goal based on user input, breaks it into smaller subtasks capable of accomplishing the intended goal, and executes those subtasks using production data, such as inputs from IoT... The following are the operational steps AI agents take: An AI agent is a software component that has the agency to act on behalf of a user or a system to perform tasks. Users can organize agents into systems that can orchestrate complex workflows, coordinate activities among multiple agents, apply logic to thorny problems, and evaluate answers to user queries.

Aaron Bawcom and Nicolai von Bismarck are partners in McKinsey’s Boston office; Asin Tavakoli is a partner in the Düsseldorf office, where Holger Harreis is a senior partner; Carlo Giovine is a partner in... If you’ve ever interacted with a customer service chatbot or asked a gen AI model to write you a sonnet, then you’re likely already familiar with a rudimentary version of AI agents. And if you’ve noticed improvements in gen AI’s performance since it went mainstream with ChatGPT, you’re not wrong. While versions of AI agents have existed for years, the natural-language-processing capabilities of today’s gen AI models have unleashed a host of new possibilities, which are enabling systems of agents to plan, collaborate, and... As agents become more accurate, companies can increasingly use them to automate organizational processes and help make employees’ day-to-day work more efficient. “The development of gen AI has been extremely fast,” says McKinsey Senior Partner Lari Hämäläinen.

“Today, the joint human-plus-machine outcome can generate great quality and great productivity.” Recent developments in short- and long-term memory structures have enabled these agents to better personalize interactions with both external and internal users,... Looking ahead, they’re about to get even better; put simply, AI agents are moving from thought to action. In the past 18 months, Google, Microsoft, OpenAI, and others have invested in software libraries and frameworks to support agentic functionality. And with applications such as Microsoft Copilot, Amazon Q, and Google’s upcoming Project Astra, which are powered by large language models (LLMs), agents are making a shift from knowledge-based tools to ones that are... In the near future, agents could become as commonplace as mobile applications are today. Artificial Intelligence (AI) is reshaping how organizations operate, and at the forefront of this transformation are AI agents.

These intelligent programs are more than just automated tools; they are proactive partners designed to streamline workflows, provide deep insights, and drive business growth. But what are AI agents, and how do they function? This article will explore their definition, benefits, and real-world applications, showing how they are becoming essential for modern enterprises. In this article, we discuss:What is an AI agent?What are the key benefits of AI agents?How are AI agents transforming business processes?What examples are there of AI agents in action?AI Agents vs. Agentic AI: What’s the difference?How to build an AI agent?What do you need to consider when implementing an AI agent?More FAQs An AI agent is an advanced software program designed to perceive its environment, make autonomous decisions, and take actions that help achieve specific business objectives.

Think of an AI agent as a tireless digital employee embedded within your organization’s systems. These intelligent agents leverage sensors to collect data, process information using sophisticated machine learning and artificial intelligence algorithms and independently execute tasks—eliminating the need for constant human oversight. An artificial intelligence (AI) agent is a system that autonomously performs tasks by designing workflows with available tools. AI agents can encompass a wide range of functions beyond natural language processing including decision-making, problem-solving, interacting with external environments and performing actions. AI agents solve complex tasks across enterprise applications, including software design, IT automation, code generation and conversational assistance. They use the advanced natural language processing techniques of large language models (LLMs) to comprehend and respond to user inputs step-by-step and determine when to call on external tools.

At the core of AI agents are large language models (LLMs). For this reason, AI agents are often referred to as LLM agents. Traditional LLMs, such as IBM® Granite® models, produce their responses based on the data used to train them and are bounded by knowledge and reasoning limitations. In contrast, agentic technology uses tool calling on the backend to obtain up-to-date information, optimize workflows and create subtasks autonomously to achieve complex goals. In this process, the autonomous agent learns to adapt to user expectations over time. The agent's ability to store past interactions in memory and plan future actions encourages a personalized experience and comprehensive responses.1 This tool calling can be achieved without human intervention and broadens the possibilities for...

These three stages or agentic components define how agents operate: AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt. Their capabilities are made possible in large part by the multimodal capacity of generative AI and AI foundation models. AI agents can process multimodal information like text, voice, video, audio, code, and more simultaneously; can converse, reason, learn, and make decisions. They can learn over time and facilitate transactions and business processes.

Agents can work with other agents to coordinate and perform more complex workflows. As explained above, while the key features of an AI agent are reasoning and acting (as described in ReAct Framework) more features have evolved over time. AI assistants are AI agents designed as applications or products to collaborate directly with users and perform tasks by understanding and responding to natural human language and inputs. They can reason and take action on the users' behalf with their supervision. AI assistants are often embedded in the product being used. A key characteristic is the interaction between the assistant and user through the different steps of the task.

The assistant responds to requests or prompts from the user, and can recommend actions but decision-making is done by the user. The comprehensive guide to artificial intelligence agents Find articles, concepts, and tutorials about AI agents Introduction to artificial intelligence agents Different types of AI agents and their characteristics How AI agents are structured and designed

AI agents are autonomous systems that can independently plan, execute, and adapt to achieve goals, unlike traditional AI that simply responds to inputs. Multiple agent types exist, ranging from simple reflex agents to sophisticated learning agents, each suited for different complexity levels and use cases. Real-world applications span various industries, including customer service, healthcare, software development, and finance. An AI agent is an independent software system that can understand contexts, evaluate and make choices, and execute actions to meet a set of goals without human supervision. Software is traditionally based on programmed instructions. On the other hand, an AI agent can adjust to changes in its environment, learn from experiences, and adapt its behavior to fulfill its goals.

An AI agent combines AI capabilities with autonomy, allowing the system to work autonomously in fluid, complex environments. AI agents include systems that use LLMs andmachine learning algorithms. This technology uses natural language to understand information, refine it and then apply the outcomes and responses through human-like intelligence.

Ai Agent Wikipedia

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In The Context Of Generative Artificial Intelligence, AI Agents (also

AI Agents Are Autonomous Intelligent Software Components That Form The

These Agents Follow A Cycle Of Perception, Reasoning And Action

Aaron Bawcom And Nicolai Von Bismarck Are Partners In McKinsey’s

“Today, The Joint Human-plus-machine Outcome Can Generate Great Quality And