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 that can pursue goals, use tools, and take actions with varying degrees of autonomy. In practice, they usually operate within human-defined objectives, constraints, and available tools. == Overview == AI agents possess several key attributes, including goal-directed behavior, natural language interfaces, the capacity to use external tools, and the ability to 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. AI agents do not have a standard definition. NIST describes agentic AI as an emerging area requiring standards for secure operation, interoperability, and reliable interaction with external systems. A common application of AI agents is task automation: for example, booking travel plans based on a user's prompted request. Companies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents. Several protocols have been proposed for standardizing inter-agent communication, with examples including the Model Context Protocol, Gibberlink, and many others. Some of these protocols are also used for connecting agents to external applications. In December 2025, Linux Foundation announced the formation of the Agentic AI Foundation (AAIF), with the goal of ensuring agentic AI evolves transparently and collaboratively. == 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. 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 such agents. == Autonomous capabilities == The Financial Times compared the autonomy of AI agents 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 == The following are some internal design options for reasoning within an agent: Retrieval-augmented generation ReAct (Reason + Act) 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. === Reference architecture === Ken Huang proposed an AI agent reference architecture, which consists of seven interconnected layers, with each layer building on the functionality of the layers beneath it: Layer 1: Foundation models - provide the core AI engines to power agent capabilities. Layer 2: Data operations - manage the complex data infrastructure required for AI agent operations, including Vector database, data loaders, RAG. Layer 3: Agent frameworks - sophisticated software and tools that simplify the development and management of the AI agents. Layer 4: Deployment and infrastructure - provide the robust technical foundation for running AI agents. Layer 5: Evaluation and observability - focus on assessing the safety and performance of AI agents. Layer 6: Security and compliance - a crucial protective framework ensuring AI agents operate safely, securely, and conform to 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. == 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 (LLMs), vision-language models (VLMs) 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 (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 have been 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 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 "[conducting] deep r
Zero-shot learning
Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to. The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. For example, given a set of images of animals to be classified, along with auxiliary textual descriptions of what animals look like, an artificial intelligence model which has been trained to recognize horses, but has never been given a zebra, can still recognize a zebra when it also knows that zebras look like striped horses. This problem is widely studied in computer vision, natural language processing, and machine perception. == Background and history == The first paper on zero-shot learning in natural language processing appeared in a 2008 paper by Chang, Ratinov, Roth, and Srikumar, at the AAAI'08, but the name given to the learning paradigm there was dataless classification. The first paper on zero-shot learning in computer vision appeared at the same conference, under the name zero-data learning. The term zero-shot learning itself first appeared in the literature in a 2009 paper from Palatucci, Hinton, Pomerleau, and Mitchell at NIPS'09. This terminology was repeated later in another computer vision paper and the term zero-shot learning caught on, as a take-off on one-shot learning that was introduced in computer vision years earlier. In computer vision, zero-shot learning models learned parameters for seen classes along with their class representations and rely on representational similarity among class labels so that, during inference, instances can be classified into new classes. In natural language processing, the key technical direction developed builds on the ability to "understand the labels"—represent the labels in the same semantic space as that of the documents to be classified. This supports the classification of a single example without observing any annotated data, the purest form of zero-shot classification. The original paper made use of the Explicit Semantic Analysis (ESA) representation but later papers made use of other representations, including dense representations. This approach was also extended to multilingual domains, fine entity typing and other problems. Moreover, beyond relying solely on representations, the computational approach has been extended to depend on transfer from other tasks, such as textual entailment and question answering. The original paper also points out that, beyond the ability to classify a single example, when a collection of examples is given, with the assumption that they come from the same distribution, it is possible to bootstrap the performance in a semi-supervised like manner (or transductive learning). Unlike standard generalization in machine learning, where classifiers are expected to correctly classify new samples to classes they have already observed during training, in ZSL, no samples from the classes have been given during training the classifier. It can therefore be viewed as an extreme case of domain adaptation. == Prerequisite information for zero-shot classes == Naturally, some form of auxiliary information has to be given about these zero-shot classes, and this type of information can be of several types. Learning with attributes: classes are accompanied by pre-defined structured description. For example, for bird descriptions, this could include "red head", "long beak". These attributes are often organized in a structured compositional way, and taking that structure into account improves learning. While this approach was used mostly in computer vision, there are some examples for it also in natural language processing. Learning from textual description. As pointed out above, this has been the key direction pursued in natural language processing. Here class labels are taken to have a meaning and are often augmented with definitions or free-text natural-language description. This could include for example a wikipedia description of the class. Class-class similarity. Here, classes are embedded in a continuous space. A zero-shot classifier can predict that a sample corresponds to some position in that space, and the nearest embedded class is used as a predicted class, even if no such samples were observed during training. == Generalized zero-shot learning == The above ZSL setup assumes that at test time, only zero-shot samples are given, namely, samples from new unseen classes. In generalized zero-shot learning, samples from both new and known classes, may appear at test time. This poses new challenges for classifiers at test time, because it is very challenging to estimate if a given sample is new or known. Some approaches to handle this include: a gating module, which is first trained to decide if a given sample comes from a new class or from an old one, and then, at inference time, outputs either a hard decision, or a soft probabilistic decision a generative module, which is trained to generate feature representation of the unseen classes—a standard classifier can then be trained on samples from all classes, seen and unseen. == Domains of application == Zero shot learning has been applied to the following fields: image classification semantic segmentation image generation object detection natural language processing computational biology abstract reasoning
Robert Abel and Associates
Robert Abel and Associates (RA&A) was an American pioneering animation production company specializing in television commercials made with computer graphics. Founded by Robert Abel and Con Pederson in 1971, RA&A was especially known for their art direction and won many Clio Awards. Abel and his team created some of the most advanced and impressive computer-animated works of their time, including full ray-traced renders and fluid character animation at a time when such things were largely unknown. A variety of high-profile television advertisements, graphics sequences for motion pictures (including The Andromeda Strain and Tron), and work on laserdisc video games such as Cube Quest, put Abel and his team on the map in the early 1980s. The company was also originally commissioned to create the visual effects for Star Trek: The Motion Picture, but were subsequently taken off the project for mishandling funds. The company was also notable on its work for The Jacksons' 1981 music video "Can You Feel It." RA&A was on the southwest corner of Highland Avenue and Romaine in the heart of Hollywood, California. RA&A closed in 1987 following an ill-fated merger with now-defunct Omnibus Computer Graphics, Inc., a company which had been based in Toronto. Many people who worked at RA&A went on to other ground-breaking projects, including the founding of Wavefront Technologies, Rhythm & Hues and other studios. Many RA&A people went on to win Academy Awards.
Pridgen v University of Calgary
Pridgen v University of Calgary was freedom of speech case which took place in Alberta, Canada, in 2010. The case deals with two university students, Keith and Steven Pridgen, who were found guilty and punished by the University of Calgary in 2008, on grounds of "non-academic misconduct". The University of Calgary defines "non-academic misconduct" as:(a) conduct which causes injury to a person and/or damage to University property and/or the property of any member of the University community; (b) unauthorized removal and/or unauthorized possession of University property; and (c) conduct which seriously disrupts the lawful educational and related activities of other students and/or University staff.The Court of the Queen's Bench of Alberta found the University of Calgary to be wrong in prosecuting ten students, including the Pridgen brothers, in regards to comments made about a professor on Facebook. The key ruling in this case was that the universities are not exempt from, and that these students were in fact protected under, section 2(b) of the Charter of Rights and Freedoms. This case is notable as it highlights the jurisdiction of the Charter in terms of both new media technologies and university institutions in Canada. == Background == Keith and Steven Pridgen were undergraduate students at the University of Calgary in 2008. The twin brothers shared a Law and Society class being taught by Aruna Mitra. Professor Mitra was teaching this class for the first time in her career, and many of the students were very critical of her knowledge of the course. A Facebook page entitled “I NO Longer Fear Hell, I Took a Course with Aruna Mitra” was created, and many students began posting comments. In particular, Steven Pridgen's comment on November 13, 2007, read: “Somehow I think she just got lazy and gave everybody a 65....that's what I got. Does anybody know how to apply to have it remarked?” Many students had similar concerns to Pridgen's and after having their work re-marked, a number of them did in fact receive higher grades. Keith Pridgen also commented on August 26, 2008: “Hey fellow LWSO. Homees.. So I am quite sure Mitra is NO LONGER TEACHING ANY COURSES WITH THE U OF C !!!!! Remember when she told us she was a long-term professor? Well, Actually she was only sessional and picked up our class at the last moment because another prof wasn't able to do it ...lucky us. Well, anyways I think we should all congratulate ourselves for leaving a Mitra-free legacy for future students!” On September 4, 2008, Aruna Mitra complained about the Facebook page to the Interim Dean of the Faculty of Communication and Culture at the University of Calgary. Dean Tettey called a meeting for the ten students who posted material about Mitra on the Facebook page. The meeting took place on September 18, 2008, and included four professors from the department as well as the Dean. At this meeting, all ten students, including the Pridgen brothers, were found guilty of non-academic misconduct. On November 20, 2008, the Appellant's received a letter from Dean Tettey advising them that their comments “clearly caused unwarranted professional and personal injury to Prof. Mitra and clearly meets the criteria for non-academic misconduct as outlined in the University of Calgary Calendar”. Keith Pridgen was put on probation for 24 months, and both brothers were required to write a letter of apology to Prof. Mitra and refrain from posting or circulating defamatory material regarding any faculty members of the University of Calgary. The Pridgen brothers appealed the decision to the University of Calgary Review Committee and later to the Board of Governors of the University of Calgary however neither of these attempts succeeded in having the decision overturned. == Opinion of the Court == Eight main issues to be determined were laid out by the Honourable Madam Justice J. Strekaf: (a) Does the Charter apply to the disciplinary proceedings taken by the Respondent; (b) If, so were the Applicants' Charter rights infringed; (c) Were the actions taken by the University ultra vires the jurisdiction of the Province of Alberta; (d) Did the Board of Governors err in refusing to hear the Applicants appeals; (e) Were the Applicants' denied a fair hearing; (f) Did the Review Committee provide adequate reasons for its decisions; (g) Did the Review Committee err in concluding that the activities of the Applicants constituted non-academic misconduct; and (h) What, if any, remedy should be granted to the Applicants. The Court determined from previous cases that "a non-government entity may still be subject to the Charter of Rights and freedoms when implementing a specific government policy or program". Justice Strekaf distinguished that the University was acting as agent of the provincial government in providing accessible post-secondary education services to students in Alberta pursuant to the provisions of the PSL Act. Justice Strekaf felt there was sufficient evidence to show that universities in Alberta have some level of reliance on government funds and therefore they are not a "Charter free zone". Justice Strekaf concluded that comments made by Keith and Steven Pridgen, regarding Professor Mitra, on Facebook did not constitute academic misconduct and the Pridgen brothers' right to freedom of expression, under section 2(b) of the Charter, was infringed by the University of Calgary Review Committee.
IDN Times
IDN Times is a digital multi-platform media outlet that provides news and entertainment for Millennials and Gen Z in Indonesia. IDN Times is one of IDN’s business units under the Digital Media pillar, founded by Winston Utomo and William Utomo on June 8, 2014. Currently, senior journalist Uni Zulfiani Lubis serves as the Editor-in-Chief of IDN Times. == History == IDN Times was initially known as Indonesian Times, a blog featuring articles written by Winston Utomo while he was working at Google Singapore. As interest and readership grew, Indonesian Times evolved into IDN Times, a digital multi-platform media company focused on delivering relevant content for Indonesia’s younger generations. == Bureau == IDN Times has a representative bureau that has spread over 12 provinces in Indonesia: == Events == === Indonesia Millennial and Gen Z Summit === The Indonesia Millennial and Gen-Z Summit (IMGS) is an annual event organized by IDN. This event aims to empower Indonesia’s younger generations through discussions and interdisciplinary collaborations. IMGS features inspirational figures, professionals, and leaders from various fields who share insights and drive positive change. The event hosts dozens of discussion sessions in collaboration with eight prominent communities. Topics covered include politics, economics, technology, and pop culture. === Indonesia Writers Festival === The Indonesia Writers Festival is an independent writing festival organized by IDN Times. The event seeks to empower Indonesians through writing by inviting experts and literacy activists from various backgrounds. == Duniaku.com == Duniaku.com is a multi-platform digital media part of IDN Times which presents content about geek culture ranging from video games, anime, comics, films, technology and gadgets. Duniaku.com was officially launched on September 6, 2019 by the Minister of Communication and Informatics Rudiantara together with CEO of IDN Media Winston Utomo and IDN Times and Editor-in-Chief of Duniaku.com Uni Lubis. == Awards == 2019 IDN won WAN-IFRA Asia Digital Media Awards 2019 as the Best Digital Project to Engage Younger and/or Millennial Audiences for IDN Times’ #MillennialsMemilih program 2020 IDN Times (IDN Times Community) won WAN-IFRA Asia Digital Media Awards 2019 in The Best in Audience Engagement category. 2021 IDN Times journalists won awards at the Subroto Award, Ministry of Energy and Mineral Resources (ESDM) on 28 September 2021. 2024 IDN Times won WAN-IFRA event at both the Asia and Global levels in Best Use of AI in Revenue Strategy. === #Interconnected22 by Pulitzer Center === One of the IDN Times journalists, Dhana Kencana, was the speaker at the #Interconnected22 conference held from June 9 to June 10, 2022, in Washington DC, United States of America. Dhana Kencana is also a grant recipient Pulitzer Center through the Rainforest Journalism Fund (RJF) program, a funding program for journalists that makes a number of coverage of the rainforest.
Softwarp
Softwarp is a software technique to warp an image so that it can be projected on a curved screen. This can be done in real time by inserting the softwarp as a last step in the rendering cycle. The problem is to know how the image should be warped to look correct on the curved screen. There are several techniques to auto calibrate the warping by projecting a pattern and using cameras and/or sensors. The information from the sensors is sent to the software so that it can analyze the data and calculate the curvature of the projection screen. == Usage == The softwarp can be used to project virtual views on curved walls and domes. These are usually used in vehicle simulators, for instance boat-, car- and airplane simulators. To make it possible to cover a dome with a 360 degree view you need to use several projectors. A problem with using several projectors on the same screen is that the edges between the projected images get about twice the amount of light. This is solved by using a technique called edge blending. With this technique a “filter” is inserted on the edge that fades the image from 100% light strength (luminance) to 0% (the lowest luminance depends on the contrast ratio of the projector). == History == The first warping technologies used a hardware image processing unit to warp the image. This processing unit was inserted between the graphics card and the projector. The problem with this technique is that it depends on the type of signal and the quality of the signal from the graphics card to warp it correctly. The process unit also needs several lines of image information before it can start sending out the warped image. This adds a latency to the display system that could be a problem in simulators that need fast response time, for instance fighter jet simulators. Softwarping eliminates the latency.
Personal media
Personal media are media of communication which are used by an individual rather than by a corporation or institution. They are generally contrasted with mass media which are produced by teams of people and broadcast to a general population. In other words, personal media allow individuals, as opposed to corporate entities, to contribute knowledge and opinion to the public. The term dates from the 1980s. New technologies such as social media and self-publishing are creating a variety of modes for modern media. Marika Lüders suggests a two-dimensional model for classifying such media with one dimension being the degree of interaction between the senders and receivers; and the other dimension being the level of institutionalisation and professionalism. Katherine Nashleanas links the concept of personal media to the notion of 'control' by an individual as opposed to a centralised authority. She argues that although personal media including the fax have been available to the general public since the 1960s, more recent technologies such as the smartphone confer greater control over content production and distribution to their users.