AI agent


In the context of generative artificial intelligence, AI agents are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation and do not require human prompts or continuous oversight.

Overview

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. Their control flow is frequently driven by large language models. Agents also include memory systems for remembering previous user-agent interactions and orchestration software for organizing agent components.
AI agents do not have a standard definition. The concept of agentic AI has been compared to the fictional character J.A.R.V.I.S..
A common application of AI agents is the automation of tasks—for example, booking travel plans based on a user's prompted request. Prominent examples include Devin AI, AutoGPT, and SIMA. Further examples of agents released since 2025 include OpenAI Operator, ChatGPT Deep Research, Manus, Quark, AutoGLM Rumination, and Coze. Frameworks for building AI agents include LangChain, as well as tools such as CAMEL, Microsoft AutoGen, and OpenAI Swarm.
Companies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents.
Proposed protocols for standardizing inter-agent communication include the Agent Protocol, the Model Context Protocol, AGNTCY, Gibberlink, the Internet of Agents, Agent2Agent, and the Agent Network Protocol. Some of these protocols are also used for connecting agents with external applications. Software frameworks for addressing agent reliability include AgentSpec, ToolEmu, GuardAgent, Agentic Evaluations, and predictive models from H2O.ai.
In February 2025, Hugging Face released Open Deep Research, an open source version of OpenAI Deep Research. Hugging Face also released a free web browser agent, similar to OpenAI Operator. Galileo AI published on Hugging Face a leadership board for agents, which ranks their performance based on their underlying LLMs.
In December 2025, Linux Foundation announced the formation of the Agentic AI Foundation - a neutral, open foundation to ensure agentic AI evolves transparently and collaboratively.
Memory systems for agents include Mem0, MemGPT, and MemOS.

History

AI agents have been traced back to research from the 1990s, with Harvard professor Milind Tambe noting that the definition of an AI agent was not clear at the time either. Researcher Andrew Ng has been credited with spreading the term "agentic" to a wider audience in 2024.

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 AI agents.

Autonomous capabilities

The Financial Times compared the autonomy of AI agents to the SAE classification of self-driving cars, comparing most applications to level 2 or level 3, with some achieving level 4 in highly specialized circumstances, and level 5 being theoretical.

Cognitive architecture

The following are some possible internal design options for reasoning within an agent:
  • Retrieval-augmented generation
  • ReAct pattern is an iterative process in which an AI agent alternates between reasoning and taking actions, receives observations from the environment or external tools, and integrates these observations into subsequent reasoning steps.
  • Reflexion, which uses an LLM to create feedback on the agent's plan of action and stores that feedback in a memory cache.
  • A tool/agent registry, for organizing software functions or other agents that the agent can use.
  • One-shot model querying, which queries the model once to create the plan of action.

    Orchestration patterns

To execute complex tasks, autonomous agents are often integrated with other agents or specialized tools. These configurations, known as orchestration patterns or workflows, include the following:
  • Prompt chaining: A sequence where the output of one step serves as the input for the next.
  • Routing: The classification of an input to direct it 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, vision-language models and multimodal foundation models can be used as the basis for agents. In September 2024, Allen Institute for AI released an open-source vision-language model. 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

As of April 2025, per the Associated Press, there are few real-world applications of AI agents. As of June 2025, per Fortune, many companies are primarily experimenting with AI agents.
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 ; analytics agents, for analyzing data to create reports; software developer or coding agents ; domain-specific agents, which include specific subject matter knowledge; and web browser agents.
By mid-2025, AI agents have been used in video game development, gambling, cryptocurrency wallets and social media. In August 2025, New York Magazine described software development as the most definitive use case of AI agents. Likewise, by October 2025, noting a decline in expectations, The Information noted AI coding agents and customer support as the primary use cases by businesses.
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 use Agentforce, AI agents from Salesforce, 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, in partnership with a local business, deployed a pilot project in two Detroit districts for an AI agent to be used for customer service calls.
In February 2025, Thomas Shedd, the director of the Technology Transformation Services, proposed using AI coding agents across the United States federal government. A recruiter for the Department of Government Efficiency proposed in April 2025 to use 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, if not impossibility, 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 " deep research, documents and even 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. Agents have been included in operating systems developed by Microsoft, Apple and Google. In November 2025, Microsoft released a test software build of Windows 11 that included agents intended 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

s with integrated AI agents are sometimes called agentic browsers. Such agents can perform small tedious tasks during web browsing and potentially even perform browser actions on behalf of the user. Products like OpenAI Operator and Perplexity Comet integrate a spectrum of AI capabilities including the ability to browse the web, interact with websites and perform actions on behalf of the user.
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. Products integrating agentic web capabilities have been criticised for exfiltrating information about their users to third-party servers and exposing security issues since the way the agents communicate often occur through non-standard protocols.