An AI agent is an artificial intelligence program that can pursue goals, use software or other tools, and take actions with some level of autonomy. Agentic AI contrasts with tool-like AI use for narrow, specific tasks such as answering questions - as with chatbots in the non-agentic forms that were common in 2023, or with traditional machine learning algorithms.
While there is no universally agreed-upon definition of an AI agent, common attributes of AI agents include goal-directed behavior, use of external tools, the ability to interact with and modify an external environment, and the ability to autonomously perform multi-step tasks. Their control flow is frequently driven by large language models (LLMs). Agent systems may also include memory components, planning logic, tool interfaces, and orchestration software for coordinating agent components.
History
The theoretical underpinnings for artificial agents emerged in the mid 20th century, with establishment of cybernetics and artificial intelligence. Oliver Selfridge's 1958 "Pandemonium: A Paradigm for Learning" paper was an important early theoretical contribution in establishing agent oriented architecture. Practical implementations of agents for real world applications began to become widespread in the 1990s, after the introduction of the belief–desire–intention software model (BDI), and agent-oriented programming. Harvard professor Milind Tambe notes that in the 1990s, the definition of an AI agent was not clear. Pure digital agents were deployed in computer infrastructure for purposes such as monitoring, while agents connected to real-world sensors and actuators were increasingly used in industrial control systems.
Early artificial agents tended to have simple if then logic, which expanded over time into large decision tree models. By the early 2010s, products like Siri and Alexa were released, and were sometimes called AI agents, though they lacked the general purpose reasoning ability of later agents run by large language models (LLMs). The development of LLMs during the late 2010s and early 2020s introduced new approaches to AI agents. Models such as GPT-3 demonstrated the ability to generate and understand natural language at a much larger scale than many earlier language systems.
Academics began to study LLM agents from 2018. Deployment of such agents began to accelerate in late 2023 after OpenAI's "function-calling" API was made available, and especially after Anthropic's late 2024 introduction of Model Context Protocol (MCP), a standardised way for LLM agents to gain contextual awareness, and to act on the world by calling various external tools. The term "agentic" only began to be used with any frequency in 2024, popularised in part by researcher Andrew Ng.
Training and testing Researchers have attempted to build world models and reinforcement learning environments to train or evaluate AI agents. For example, video games such as Minecraft and No Man's Sky as well as replicas of company websites, have also been used for training such agents.
Autonomous capabilities The Financial Times compared AI agents' autonomy to the SAE classification of self-driving cars, likening most applications to level 2 or level 3, with some achieving level 4 in highly specialized circumstances and level 5 being theoretical.
Cognitive architecture
Ken Huang proposed an AI agent reference architecture that consists of seven interconnected layers, with each layer building on the functionality of the layers beneath it: Layer 1: Foundation models – provide the datasets that power the agent. Layer 2: Data operations – manages the data infrastructure required for AI agent operations, including vector databases, data loaders, and RAG. Layer 3: Agent frameworks – software that manages the AI agents. Layer 4: Deployment and infrastructure – the technical foundation of the AI agents. Layer 5: Evaluation and observability – the safety and performance of AI agents. Layer 6: Security and compliance – a protective framework for safe operation and compliance with regulatory boundaries. At this layer, security and compliance features embedded into all the AI agent stack layers are integrated together. Layer 7: Agent ecosystem – represents the AI agents' interface with real-world applications and users.
Agent harness
An agent harness, or agent scaffold, is the software layer surrounding a large language model that enables it to function as an AI agent. It commonly manages prompts, context, tool use, memory, execution state, operational constraints, sandboxes, permissions, and the processing of results. The harness connects the model to internal and external computer hardware, software, data files, databases, web browsers, command-line interfaces, and application programming interfaces, while controlling how the agent accesses and uses these resources to complete multi-step tasks.
Agent orchestration An agent orchestration is a layer surrounding multiple AI agents. The goal of agent orchestration is to produce a coherent group out of these AI agents, that is, a thing that achieves better results than a minimal "no orchestration" solution: running these agents in parallel without interaction, and synthesizing their outputs.
Relatedly, a workflow is a layer surrounding an AI agent, or a group of AI agents, intended to allow better performance on certain tasks that has to progress in stages, or tight integration with specialized tasks.
Examples include the following: Prompt chaining: A sequence where the output of one step serves as the input for the next. Routing: Directing an input to a specialized downstream task or tool. Parallelization: The simultaneous execution of multiple tasks. Sequential processing: A fixed, linear progression of tasks through a predefined pipeline. Planner-critic: An iterative pattern where one agent generates a proposal and another evaluates it to provide feedback for refinement.
Multimodal AI agents In addition to large language models (LLMs), vision-language models (VLMs) and multimodal foundation models can be used as the basis for agents. Allen Institute for AI released an open-source vision-language model in 2024. Nvidia released a framework for developers to use VLMs, LLMs and retrieval-augmented generation for building AI agents that can analyze images and videos, including video search and video summarization. Microsoft released a multimodal agent model – trained on images, video, software user interface interactions, and robotics data – that the company claimed can manipulate software and robots.
Applications The Information divided AI agents into seven archetypes: business-task agents, for acting within enterprise software; conversational agents, which act as chatbots for customer support; research agents, for querying and analyzing information (such as OpenAI Deep Research); analytics agents, for analyzing data to create reports; software developer or coding agents (such as Cursor); domain-specific agents, which include specific subject matter knowledge; and web browser agents (such as OpenAI Operator).
By mid-2025, AI agents were being used in video game development, gambling (including sports betting), cryptocurrency wallets (including cryptocurrency trading and meme coins) and social media. In August 2025, New York Magazine described software development as the most definitive use of AI agents. The Information noted AI coding agents and customer support as the primary uses of AI by businesses by October 2025, although a decline in the expectations of AI capabilities was also noted.
In November 2025, The Wall Street Journal reported that few companies that deployed AI agents have received a return on investment.
Applications in government Several government bodies in the United States and United Kingdom have deployed or announced the deployment of agents at the local and national level. The city of Kyle, Texas deployed an AI agent from Salesforce in March 2025 for 311 customer service. In November 2025, the Internal Revenue Service stated that it would deploy Salesforce AI agents for the Office of Chief Counsel, Taxpayer Advocate Services, and the Office of Appeals. That same month, Staffordshire Police announced that they would trial Agentforce agents for handling non-emergency 101 calls in the United Kingdom starting in 2026. In December 2025, the Department of Neighborhoods in Detroit, Michigan, partnered with a local business to deploy a customer service AI agent in two city districts.
In February 2025, Thomas Shedd, the director of the Technology Transformation Services, proposed using AI coding agents across the United States federal government. In April 2025, a recruiter for the Department of Government Efficiency proposed using AI agents to automate the work of about 70,000 United States federal government employees as part of a startup with funding from OpenAI and a partnership agreement with Palantir. This proposal was criticized by experts for its impracticality and the lack of corresponding widespread adoption by businesses.
In December 2025, the Food and Drug Administration announced that it would offer "agentic AI capabilities" to its staff for "meeting management, pre-market reviews, review validation, post-market surveillance, inspections and compliance and administrative functions." That same month, the United States Department of Defense launched GenAI.mil, an internal platform for American military personnel to use generative AI-based applications based on Google Gemini, including "intelligent agentic workflows". Defense Secretary Pete Hegseth listed applications such as "[conducting] deep research, [formatting] documents and even [analyzing] video or imagery at unprecedented speed." In December 2025, the United States Immigration and Customs Enforcement agency signed a contract with a company for its Enforcement and Removal Operations department to use AI agents for skip tracing.
Operating systems AI agents have also been integrated into operating systems developed by Microsoft, Apple, ByteDance, and Google. In November 2025, Microsoft released a test software build of Windows 11 that included agents to run background tasks, with the ability to read and write personal files. In December 2025, ByteDance released Doubao, an AI agent that can be integrated into smartphone operating systems, particularly the Nubia M153 by ZTE. Several apps in China blocked or restricted the agent, citing privacy and security concerns, including WeChat, Alipay, Taobao, Pinduoduo, Ele.me, and local banks.
Web browsing Web browsers with integrated AI agents are sometimes called agentic browsers. Such agents can perform tasks and browser actions on the user's behalf.
In 2025, Microsoft launched NLWeb, an agentic web search replacement that would allow websites to use agents to query content from websites by using RSS-like interfaces that allow for the lookup and semantic retrieval of content. Within weeks of release, NLWeb was found to have created security issues and expose information about users to third-party servers.
Applications in marketing and advertising Industry standards now cover AI agents that plan, negotiate, and buy digital media. In 2026, the technology arm of the Interactive Advertising Bureau, IAB Tech Lab, published the Agentic Advertising Management Protocols (AAMP), an open framework for agents that discover inventory, negotiate, and complete media transactions on behalf of buyers and sellers. This differs from platform-native automation such as Google Ads Smart Bidding and Meta Advantage+, which optimize bids and delivery inside a single advertising platform. Cross-channel agents instead recommend budget changes across media, typically with a human in the loop.
Proposed benefits