Agent2Agent (A2A) is an open protocol that defines how artificial intelligence agents communicate with each other across different systems. It is intended to allow agents built by different vendors or frameworks to discover one another, exchange messages, and coordinate tasks. == History == The Agent2Agent protocol was announced by Google in April 2025 as an open standard for agent interoperability. In June 2025, Google transferred the protocol, its specification, and related software development kits to the Linux Foundation. The Linux Foundation established the Agent2Agent project to provide vendor-neutral governance. == Design == The A2A protocol supports communication between autonomous software agents operating across different platforms and organizations. It enables agents to discover one another and exchange structured messages without requiring shared internal state or proprietary integrations. A2A uses metadata documents, known as Agent Cards, to describe an agent's capabilities and how it can be accessed. These documents are exchanged using widely adopted web technologies such as HTTP and JSON-based messaging formats. A2A includes support for authentication and authorization to control which agents may participate in workflows. The protocol supports established security technologies including Transport Layer Security (TLS), JSON Web Tokens (JWTs), and OpenID Connect. A2A is often discussed alongside the Model Context Protocol (MCP). MCP focuses on connecting agents to tools and data sources, while A2A focuses on communication between agents themselves. == Adoption == At the time the Linux Foundation adopted the protocol, more than 100 technology companies had announced support for the Agent2Agent project. Microsoft stated that it planned to support the protocol in its AI platforms. == Reception == Technology press coverage has described A2A as an attempt to reduce fragmentation in AI agent ecosystems by providing a shared communication layer. TechRepublic characterized the protocol as part of a broader industry effort to reduce vendor lock-in for enterprise AI systems.
30 Boxes
30 Boxes is a minimalist calendaring IOS application created by 83 Degrees. Originating as a web application in March 2006, 30 Boxes was founded by Webshots cofounder Narendra Rocherolle. The website shut down some time in 2020, but relaunched for the IOS in February 2021. The original website was tailored towards "social media junkies". == Reception == Barry Collins of The Sunday Times appreciated the website's plain-language event adding feature, but did not appreciate that he was unable to see more than one month of events at a time. Collins was also unhappy that the website was not capable of warning him when he had two events scheduled at the same time. In a list of the best web-based calendar software for small businesses, Forbes ranked 30 Boxes second, after Google Calendar. They described 30 Boxes like “buying a new car with manual transmission and lots of extras—you don't just want to drive it, you want to fool around with it to see what it can do”.
CHAOS (chess)
CHAOS (Chess Heuristics and Other Stuff) is a chess playing program that was developed by programmers working at the RCA Systems Programming division in the late 1960s. It played competitively in computer chess competitions in the 1970s and 1980s. It differed from other programs of that era in its look-ahead philosophy, choosing to use chess knowledge to evaluate fewer positions and continuations as opposed to simple evaluations that relied on deep look-ahead to avoid bad moves. == Introduction == CHAOS was originally developed by Ira Ruben, Fred Swartz, Victor Berman, Joe Winograd and William Toikka while working at RCA in Cinnaminson, NJ. Its name is an acronym for 'Chess Heuristics and Other Stuff.' Program development moved to the Computing Center of the University of Michigan when Swartz changed jobs, and Mike Alexander joined the development group. Swartz, Alexander and Berman were continuously group members from that point onward in CHAOS' evolution, as others of the original authors left and new members contributed episodically. Chess Senior Master Jack O'Keefe contributed to CHAOS' development from about 1980 onwards. CHAOS was written in Fortran, except for low-level board representation manipulations written in assembly language or C. Due to this portability, it ran on RCA, Univac and IBM-compatible mainframes in its lifetime. CHAOS heralds from the mainframe computing era when only machines of that capacity were able to play at a high level. Consequently, development and testing could only take place at off-peak times for production use of the machine. In a competition, CHAOS had to run on a dedicated mainframe with a telephone link to the match venue. In its later years, CHAOS ran on computers on the machine assembly floor of Amdahl Corporation on MTS. == Background == === Chess and artificial intelligence === Mathematicians Claude Shannon and Alan Turing, working separately, were the first to view playing chess as a challenge to machines. Working for AT&T / Bell Labs with its access to telephone switching equipment, Shannon built a relay-based machine that learned how to work its way through a two-dimensional, 5x5 cell maze in 1949. Shannon viewed this as an analogue of the way that organisms learn things about their natural environment. There is a random element to searching it, a memory element to benefit from the search outcome, and a reward element that reinforces learning when the global outcome is favorable to the organism. Soon afterward, Shannon wrote a mathematical analysis of the game of chess, published in 1950. Like with the maze, he broke down game play into the necessary elements for reinforcement learning. Associated with each board configuration a move will be made from, there is a numerical score. To decide what move to make, a player wants to maximize their own position's score after the move and to minimize their opponent's score (a minimax view). Since there are about 32 possible moves at each of the early stages of the game, and about 40 moves and responses in each game, then there are about 32 80 {\displaystyle 32^{80}} or about 10 120 {\displaystyle 10^{120}} possible games - an impossibly large set to evaluate completely. Therefore, there must be a way to limit the number of moves to look ahead for to find the best one. Reducing the game to these few key elements provided a way to think about human intelligence in general. Shannon became part of a wider group using computing machines to mimic aspects of human intelligence that grew into the general idea of artificial intelligence. (Other members of this group were John McCarthy, Herbert Simon, Allen Newell, Alan Kotok, Alex Bernstein and Richard Greenblatt.) The paradigm that evolved was that there was a quantification of the position on the board into a score, an evaluation method to find favorable outcomes (minimax, later alpha-beta pruning), and a strategy to manage the combinatorial explosion of the look-ahead possibilities. By the early 1960s, there were computer programs that played chess at a rudimentary level. They used very simple evaluation functions for each position and tried to search as far forward as was practical given the time constraints and available compute power. Naturally, programmers optimized their code to use the available computing resources. This led to a major philosophical divide among chess programs: those that tried to evaluate as many positions as possible, and those that tried to evaluate the most promising move sequences as deeply as possible. CHAOS was firmly in the camp believing only the most promising moves should be evaluated in depth. Said Swartz, "The 'brute force people' ... look at every (possible move) no matter what garbage it is. Most moves are just terrible, terrible moves, and most computing time is being spent on pure garbage." The program spent more time evaluating each board position in the expectation that it would find the most promising lines of play to explore in depth. In 1983, the then-fastest chess program (Belle) evaluated 110,000 positions per second, and typical programs 1000–50,000 per second, whereas CHAOS evaluated about 50-100 per second. === Machine learning and strategies to manage search === From about 1949 onward, Arthur Samuel began work for IBM on machine learning, culminating in a checkers-playing program in 1952 and publications on the topic. Concurrently, Christopher Strachey created Checkers, a program to play the board game of checkers in 1951, but it had no capacity to learn from its play. Checkers was chosen by both authors because it was simpler than chess yet contained the basic characteristics of an intellectual activity, and, in Samuel's view, was a test-bed in which heuristic procedures and learning processes could be evaluated quickly. Checker playing programs introduced the notion of the game tree and evaluating play to various depths to choose the best move. The complexity of chess, however, promoted it to the status of an analogue for human intelligence, and it attracted computer scientists' attention, who referred to it as research into artificial intelligence (AI). Like checkers, it required a numerical assessment of each arrangement of chess pieces on a board. It also required looking ahead to future moves to decide how to play the present position. Due to the enormous number of possible moves, there had to be a way to confine the look-ahead search to the most promising lines of play. From these factors, the notion of minimax score evaluation developed and, later, alpha-beta tree pruning to abandon looking at positions worse than any that have already been examined. === Chess search strategies === The AI community viewed artificial intelligence as comprising two parts: a way to symbolically quantify the knowledge in hand (a chess board position), and a set of heuristics to limit look-ahead to the consequences of a move. The early chess playing programs attempted to look forward as far as possible, perhaps to 3 moves ahead by each player, and to choose the best outcome. This led to the horizon effect, whereby a key move 4 or more moves ahead would be unexamined and therefore missed. Consequently, the programs were quite weak and heuristics to manage the search became important in their development. CHAOS used a selective search strategy with iterative widening. As chess programs evolved, they incorporated books of opening lines of play from historic sources. Nowadays, book moves are catalogued in machine-readable form, but originally programmers had to type them in. CHAOS had an extensive book for its time of around 10,000 moves that O'Keefe helped to develop. A problem with play from an opening book is the behavior of the program when the play leaves the book: the positional advantage may be so subtle that the evaluation scheme may be unable to understand it, leading to very wide and shallow searches to establish a line of play. The horizon effect again plagues move selection after leaving the book. CHAOS mitigated these problems by only using book lines that it could understand, and by relying on cached analyses of continuations out of the book made while the opponent's clock was running. == Game Play History == CHAOS played in twelve ACM computer chess tournaments and four World Computer Chess Championships (WCCC). Its debut was the ACM computer chess tournament in 1973, taking 2nd place. In 1974, it again won 2nd place in the WCCC, defeating the tournament favorite Chess 4.0 but losing to Kaissa. CHAOS was close to winning the 1980 WCCC, but lost to Belle in a playoff. The 1985 ACM computer chess tournament was CHAOS' last competition. One of CHAOS' notable victories was over Chess 4.0 at the 1974 WCCC tournament. Chess 4.0 was unbeaten by any other program up until then. Playing as white, CHAOS made a knight sacrifice (16 Nd4-e6!!) that traded material for open lines of attack and eventually won the game. CHAOS’ authors thought the move was due to a
Pedagogical agent
A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion". == History == The history of Pedagogical Agents is closely aligned with the history of computer animation. As computer animation progressed, it was adopted by educators to enhance computerized learning by including a lifelike interface between the program and the learner. The first versions of a pedagogical agent were more cartoon than person, like Microsoft's Clippy which helped users of Microsoft Office load and use the program's features in 1997. However, with developments in computer animation, pedagogical agents can now look lifelike. By 2006 there was a call to develop modular, reusable agents to decrease the time and expertise required to create a pedagogical agent. There was also a call in 2009 to enact agent standards. The standardization and re-usability of pedagogical agents is less of an issue since the decrease in cost and widespread availability of animation tools. Individualized pedagogical agents can be found across disciplines including medicine, math, law, language learning, automotive, and armed forces. They are used in applications directed to every age, from preschool to adult. == Learning theories related to pedagogical agent design == === Distributed cognition theory === Distributed cognition theory is the method in which cognition progresses in the context of collaboration with others. Pedagogical agents can be designed to assist the cognitive transfer to the learner, operating as artifacts or partners with collaborative role in learning. To support the performance of an action by the user, the pedagogical agent can act as a cognitive tool as long as the agent is equipped with the knowledge that the user lacks. The interactions between the user and the pedagogical agent can facilitate a social relationship. The pedagogical agent may fulfill the role of a working partner. === Socio-cultural learning theory === Socio-cultural learning theory is how the user develops when they are involved in learning activities in which there is interaction with other agents. A pedagogical agent can: intervene when the user requests, provide support for tasks that the user cannot address, and potentially extend the learners cognitive reach. Interaction with the pedagogical agent may elicit a variety of emotions from the learner. The learner may become excited, confused, frustrated, and/or discouraged. These emotions affect the learners' motivation. === Extraneous Cognitive Load === Extraneous cognitive load is the extra effort being exerted by an individual's working memory due to the way information is being presented. A pedagogical agent can increase the user's cognitive load by distracting them and becoming the focus of their attention, causing split attention between the instructional material and the agent. Agents can reduce the perceived cognitive load by providing narration and personalization that can also promote a user's interest and motivation. While research on the reduction of cognitive load from pedagogical agents is minimal, more studies have shown that agents do not increase it. == Effectiveness == It has been suggested by researchers that pedagogical agents may take on different roles in the learning environment. Examples of these roles are: supplanting, scaffolding, coaching, testing, or demonstrating or modelling a procedure. A pedagogical agent as a tutor has not been demonstrated to add any benefit to an educational strategy in equivalent lessons with and without a pedagogical agent. According to Richard Mayer, there is some support in research for pedagogical agent increasing learning, but only as a presenter of social cues. A co-learner pedagogical agent is believed to increase the student's self-efficacy. By pointing out important features of instructional content, a pedagogical agent can fulfill the signaling function, which research on multimedia learning has shown to enhance learning. Research has demonstrated that human-human interaction may not be completely replaced by pedagogical agents, but learners may prefer the agents to non-agent multimedia systems. This finding is supported by social agency theory. Much like the varying effectiveness of the pedagogical agent roles in the learning environment, agents that take into account the user's affect have had mixed results. Research has shown pedagogical agents that make use of the users’ affect have been found to increase user knowledge retention, motivation, and perceived self-efficacy. However, with such a broad range of modalities in affective expressions, it is often difficult to utilize them. Additionally, having agents detect a user's affective state with precision remains challenging, as displays of affect are different across individuals. == Design == === Attractiveness === The appearance of a pedagogical agent can be manipulated to meet the learning requirements. The attractiveness of a pedagogical agent can enhance student's learning when the users were the opposite gender of the pedagogical agent. Male students prefer a sexy appearance of a female pedagogical agents and dislike the sexy appearance of male agents. Female students were not attracted by the sexy appearance of either male or female pedagogical agents. === Affective Response === Pedagogical agents have reached a point where they can convey and elicit emotion, but also reason about and respond to it. These agents are often designed to elicit and respond to affective actions from users through various modalities such as speech, facial expressions, and body gestures. They respond to the affective state of the given user, and make use of these modalities using a wide array of sensors incorporated into the design of the agent. Specifically in education and training applications, pedagogical agents are often designed to increasingly recognize when users or learners exhibit frustration, boredom, confusion, and states of flow. The added recognition in these agents is a step toward making them more emotionally intelligent, comforting and motivating the users as they interact. === Digital Representation === The design of a pedagogical agent often begins with its digital representation, whether it will be 2D or 3D and static or animated. Several studies have developed pedagogical agents that were both static and animated, then evaluated the relative benefits. Similar to other design considerations, the improved learning from static or animated agents remains questionable. One study showed that the appearance of an agent portrayed using a static image can impact a user's recall, based on the visual appearance. Other research found results that suggest static agent images improve learning outcomes. However, several other studies found user's learned more when the pedagogical agent was animated rather than static. Recently a meta-analysis of such research found a negligible improvement in learning via pedagogical agents, suggesting more work needs to be done in the area to support any claims.
Hidden layer
In artificial neural networks, a hidden layer is a layer of artificial neurons that is neither an input layer nor an output layer. The simplest examples appear in multilayer perceptrons (MLP), as illustrated in the diagram. An MLP without any hidden layer is essentially just a linear model. With hidden layers and activation functions, however, nonlinearity is introduced into the model. In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation.
Managed private cloud
Managed private cloud (also known as "hosted private cloud" or "single-tenant SaaS") refers to a principle in software architecture where a single instance of the software runs on a server, serves a single client organization (tenant), and is managed by a third party. The third-party provider is responsible for providing the hardware for the server and also for preliminary maintenance. This is in contrast to multitenancy, where multiple client organizations share a single server, or an on-premises deployment, where the client organization hosts its software instance. Managed private clouds also fall under the larger umbrella of cloud computing. == Adoption == The need for private clouds arose due to enterprises requiring a dedicated service and infrastructure for their cloud computing needs, such as for business-critical operations, improved security, and better control over their resources. Managed private cloud adoption is a popular choice among organizations. It has been on the rise due to enterprises requiring a dedicated cloud environment and preferring to avoid having to deal with management, maintenance, or future upgrade costs for the associated infrastructure and services. Such operational costs are unavoidable in on-premises private cloud data centers. == Advantages and challenges of managed private cloud == A managed private cloud cuts down on upkeep costs by outsourcing infrastructure management and maintenance to the managed cloud provider. It is easier to integrate an organization's existing software, services, and applications into a dedicated cloud hosting infrastructure which can be customized to the client's needs instead of a public cloud platform, whose hardware or infrastructure/software platform cannot be individualized to each client. Customers who choose a managed private cloud deployment usually choose them because of their desire for efficient cloud deployment, but also have the need for service customization or integration only available in a single-tenant environment. This chart shows the key benefits of the different types of deployments, and shows the overlap between these cloud solutions. This chart shows key drawbacks. Since deployments are done in a single-tenant environment, it is usually cost-prohibitive for small and medium-sized businesses. While server upkeep and maintenance are handled by the service provider, including network management and security, the client is charged for all such services. It is up to the potential client to determine if a managed private cloud solution aligns with their business objectives and budget. While the service provider maintains the upkeep of servers, network, and platform infrastructure, sensitive data is typically not stored on managed private clouds as it may leave business-critical information prone to breaches via third-party attacks on the cloud service provider. Common customizations and integrations include: Active Directory Single Sign-on Learning Management Systems Video Teleconferencing == Deployment strategies and service providers == Software companies have taken a variety of strategies in the Managed Private Cloud realm. Some software organizations have provided managed private cloud options internally, such as Microsoft. Companies that offer an on-premises deployment option, by definition, enable third-party companies to market Managed Private Cloud solutions. A few managed private cloud service providers are: Adobe Connect: Adobe Connect may be purchased for on-premises deployment, multi-tenant hosted deployment, managed private cloud as ACMS, or managed by third-party managed private cloud provider ConnectSolutions. Rackspace CenturyLink Microsoft licenses for Lync, SharePoint and Exchange may be purchased for on-premises deployment, a multi-tenant hosted deployment via Office 365, or managed by third-party cloud hosting from Azaleos, ConnectSolutions and others.
Robotic process automation
Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. RPA should not be confused with artificial intelligence as it is based on automation technology following a predefined workflow. It is sometimes referred to as software robotics (not to be confused with robot software). In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application's graphical user interface (GUI) and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to the use of automation in products that might not otherwise feature APIs for this purpose. RPA tools have strong technical similarities to graphical user interface testing tools. These tools also automate interactions with the GUI, and often do so by repeating a set of demonstration actions performed by a user. RPA tools differ from such systems in that they allow data to be handled in and between multiple applications, for instance, receiving email containing an invoice, extracting the data, and then typing that into a bookkeeping system. == Historic evolution == As a form of automation, the concept has been around for a long time in the form of screen scraping, so long that to early PC users the reminder of it often blurs with the idea of malware infection. Yet compared to screen scraping, RPA is much more extensible, consisting of API integration into other enterprise applications, connectors into ITSM systems, terminal services and even some types of AI (e.g. machine learning) services such as image recognition. It is considered to be a significant technological evolution in the sense that new software platforms are emerging which are sufficiently mature, resilient, scalable and reliable to make this approach viable for use in large enterprises (who would otherwise be reluctant due to perceived risks to quality and reputation). == Use == The hosting of RPA services also aligns with the metaphor of a software robot, with each robotic instance having its own virtual workstation, much like a human worker. The robot uses keyboard and mouse controls to take actions and execute automations. Normally, all of these actions take place in a virtual environment and not on screen; the robot does not need a physical screen to operate, rather it interprets the screen display electronically. The scalability of modern solutions based on architectures such as these owes much to the advent of virtualization technology, without which the scalability of large deployments would be limited by the available capacity to manage physical hardware and by the associated costs. The implementation of RPA in business enterprises has shown dramatic cost savings when compared to traditional non-RPA solutions. === RPA actual use === Banking and finance process automation Mortgage and lending processes Customer care automation eCommerce merchandising operations Social media marketing Optical character recognition applications Data extraction process Fixed automation process Manual and repetitive tasks automation Voice recognition and digital dictation software linked to join up business processes for straight through processing without manual intervention Specialised remote infrastructure management software featuring automated investigation and resolution of problems, using robots for the first line IT support Chatbots used by internet retailers and service providers to service customer requests for information. Also used by companies to service employee requests for information from internal databases Presentation layer automation software, increasingly used by business process outsourcers to displace human labour Interactive voice response (IVR) systems incorporating intelligent interaction with callers == Impact on employment == According to Harvard Business Review, most operations groups adopting RPA have promised their employees that automation would not result in layoffs. Instead, workers have been redeployed to do more interesting work. One academic study highlighted that knowledge workers did not feel threatened by automation: they embraced it and viewed the robots as team-mates. The same study highlighted that, rather than resulting in a lower "headcount", the technology was deployed in such a way as to achieve more work and greater productivity with the same number of people. Conversely, however, some analysts proffer that RPA represents a threat to the business process outsourcing (BPO) industry. The thesis behind this notion is that RPA will enable enterprises to "repatriate" processes from offshore locations into local data centers, with the benefit of this new technology. The effect, if true, will be to create high-value jobs for skilled process designers in onshore locations (and within the associated supply chain of IT hardware, data center management, etc.) but to decrease the available opportunity to low-skilled workers offshore. On the other hand, this discussion appears to be healthy ground for debate as another academic study was at pains to counter the so-called "myth" that RPA will bring back many jobs from offshore. === Impact on society === Academic studies project that RPA, among other technological trends, is expected to drive a new wave of productivity and efficiency gains in the global labour market. Although not directly attributable to RPA alone, Oxford University conjectures that up to 35% of all jobs might be automated by 2035. There are geographic implications to the trend in robotic automation. In the example above where an offshored process is "repatriated" under the control of the client organization (or even displaced by a business process outsourcer) from an offshore location to a data centre, the impact will be a deficit in economic activity to the offshore location and an economic benefit to the originating economy. On this basis, developed economies – with skills and technological infrastructure to develop and support a robotic automation capability – can be expected to achieve a net benefit from the trend. In a TEDx talk hosted by University College London (UCL), entrepreneur David Moss explains that digital labour in the form of RPA is likely to revolutionize the cost model of the services industry by driving the price of products and services down, while simultaneously improving the quality of outcomes and creating increased opportunity for the personalization of services. In a separate TEDx in 2019 talk, Japanese business executive, and former CIO of Barclays bank, Koichi Hasegawa noted that digital robots can be a positive effect on society if we start using a robot with empathy to help every person. He provides a case study of the Japanese insurance companies – Sompo Japan and Aioi – both of whom introduced bots to speed up the process of insurance pay-outs in past massive disaster incidents. Meanwhile, Professor Willcocks, author of the LSE paper cited above, speaks of increased job satisfaction and intellectual stimulation, characterising the technology as having the ability to "take the robot out of the human", a reference to the notion that robots will take over the mundane and repetitive portions of people's daily workload, leaving them to be used in more interpersonal roles or to concentrate on the remaining, more meaningful, portions of their day. It was also found in a 2021 study observing the effects of robotization in Europe that, the gender pay gap increased at a rate of .18% for every 1% increase in robotization of a given industry. == Unassisted RPA == Unassisted RPA, or RPAAI, is the next generation of RPA related technologies. Technological advancements around artificial intelligence allow a process to be run on a computer without needing input from a user. == Hyperautomation == Hyperautomation is the application of advanced technologies like RPA, artificial intelligence, machine learning (ML) and process mining to augment workers and automate processes in ways that are significantly more impactful than traditional automation capabilities. Hyperautomation is the combination of technologies that allow faster application authorship (like low-code and no-code) with automation technologies that coordinate different worker types (i.e. human and artificial) for intelligent and strategic workflow optimization. Gartner's report notes that this trend was kicked off with robotic process automation (RPA). The report notes that, "RPA alone is not hyperautomation. Hyperautomation requires a combination of tools to help support replicating pieces of where the human is involved in a task." == Outsourcing == Back office clerical processes outsourced by large organisations