AI News Archive: August 20, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- We’ve never seen an Anthropic before
Anthropic’s IPO is going to leave OpenAI in the past.
Score: 44🌐 MovesAug 20, 2026https://www.ai-supremacy.com/p/we-have-never-seen-an-anthropic-before-2026-revenue-ipo - Earning and sustaining trust in the age of AI
Invesco President and CEO Andrew Schlossberg argues that AI should be part of a firm’s strategy, not on the sidelines, and that trust, once lost, is hard to win back.
- How to Effectively Align Your Intent with Claude Code
Improve your proficiency with Claude Code. The post How to Effectively Align Your Intent with Claude Code appeared first on Towards Data Science .
Score: 44🌐 MovesAug 20, 2026https://towardsdatascience.com/how-to-effectively-align-your-intents-with-claude-code/ - AI firms sharpen privacy pitch as enterprises demand control over data
Here is how OpenAI, Anthropic and Google are approaching data privacy as businesses demand greater control over sensitive information shared with increasingly capable AI models
- Qualcomm Appoints Sergio Buniac as Executive Vice President and Group General Manager, Mobile, Compute, and Personal AI
Qualcomm Appoints Sergio Buniac as Executive Vice President and Group General Manager, Mobile, Compute, and Personal AI Qualcomm
Score: 44🌐 MovesAug 20, 2026https://www.qualcomm.com/news/releases/2026/08/qualcomm-appoints-sergio-buniac-as-executive-vice-president-and- - The Next Evolution Of AI Will Rely On Context Layers
The promise of neurosymbolic AI — which combines neural network pattern recognition with rule-based reasoning — will only be possible when underpinned by trusted, governed business context. Context has become a buzzword, with terms like semantics, ontology, semantic layer, knowledge graph, and context layer being used interchangeably. Enterprises need a clearer definition of what they […]
Score: 43🌐 MovesAug 20, 2026https://www.forrester.com/blogs/the-next-evolution-of-ai-will-rely-on-context-layers/ - Foreign ownership of Japan stocks hits new record on AI boom
Foreign ownership of Japan stocks hits new record on AI boom Nikkei Asia
Score: 43🌐 MovesAug 20, 2026https://asia.nikkei.com/business/markets/equities/foreign-ownership-of-japan-stocks-hits-new-record-on-ai-boom - ICO police facial recognition audits reveal ‘mixed’ bag
UK data regulator says “significant improvements” are needed in how UK police forces are using facial recognition technologies, after official audits reveal “a mixed picture”. According to an “outcomes report” from the UK Information Commissioner’s Office (ICO), which has been published alongside its recent facial recognition audits of West Yorkshire Police and Greater Manchester Police , inconsistencies across how a sample of five forces are using the technology have revealed the need for “urgent attention” in a number of areas. The ICO said that while there are “genuine areas of assurance” (including forces generally having identified and documented a lawful basis for their facial recognition-related data processing), action is needed to ensure there is clear senior oversight, accountability and training for staff using facial recognition technologies, and that they fully understand their roles and responsibilities. It added that forces will also need to keep clear records of what personal information is being used, where it comes from, how it is used and who it is shared with, as well as take extra steps to reduce the risk of unfairness or bias and ensure systems are accurate. The ICO said that across audited forces – which also includes South Wales and Gwent , Essex , and Leicestershire – it made 107 recommendations covering both compliance and best practice, all of which were accepted or partially accepted. The report and audit results come amid a nationwide push to roll out facial recognition and artificial intelligence (AI) tools across UK policing. Prior to this, facial recognition was mostly used by the Metropolitan and South Wales Police. Further recommendations made by the ICO include forces developing their own facial recognition audit procedures, ensuring there are logging capabilities in place to understand how and why officers are using people’s personal information, and conducing iterative data protection impact assessments to ensure all risks are identified and mitigated . Compliance rates The regulator also highlighted that compliance rates were generally higher for the use of live facial recognition (LFR) than retrospective facial recognition (RFR), specifically noting that forces need to make sure images used for the latter are obtained from appropriate sources and not kept for longer than necessary. “Our audit recommendations give forces the direction they need to get data protection obligations right,” said the ICO. “The forces we have audited all have action plans in place to ensure compliance. We will follow up with forces to ensure they implement these. “At national level, we are working with the National Police Chiefs’ Council’s (NPCC’s) leads for FRT to support a consistent approach to compliance across forces in England and Wales. Drawing on the findings of our audits and the clear expectations set out in this report. We are also engaging with the Home Office on planned legislative reforms for FRT and national policing.” The ICO added that “if forces do not make improvements, or if we identify future contraventions of the law, we will not hesitate to use our regulatory tools”. An audit of how the Metropolitan Police – which first deployed LFR at Notting Hill Carnival 2016 – use facial recognition is due to be conducted later in 2026. Unlawfully held custody images The ICO was clear in its report that forces should limit the size of watchlists (in line with data protection principles requiring personal information to be adequate, relevant and not excessive for the intended law enforcement purpose), and should only use images that are accurate, verifiable and lawfully held by the police at the time of use. However, despite the High Court ruling in 2012 that millions of custody images – including those of people never even charged with or convicted of a crime – were being unlawfully retained by the Home Office in the Police National Database (PND), successive biometrics commissioners have warned that millions of these records are still being kept and could find their way into police watchlists . In February 2026, for example, it came to light that software engineer Alvi Choudhury was arrested as a result of an RFR search by Thames Valley Police , who was detained after the force used a five-year-old custody image to link him to a crime that he was 80 miles away from at the time of the incident. That custody image was taken after Choudhury was previously detained – but never charged – in Portsmouth in 2021, following an altercation between two groups. Senior officers from the Metropolitan and South Wales Police previously told a Parliamentary committee in December 2023 that, given the huge size of watchlists that can run into the thousands, images are selected based on the crime category attached to the photo, rather than context-specific intelligence about that individual. Computer Weekly contacted the ICO about whether it looked into the custody image issue, and whether it could comment on how the harms of unlawfully held images finding their way onto watchlists can be effectively mitigated given the above context. An ICO spokesperson said: “Public trust in police use of facial recognition technology depends on robust governance and accountability. Forces must ensure that images used within facial recognition systems are accurate, verifiable and lawfully held in accordance with data protection law. “They should also be able to evidence that watchlist images are necessary and relevant. We understand this is an area of ongoing concern and we are engaging with the Home Office to understand how these issues are being addressed.” Upcoming legislation In December 2025, the Home Office launched a 10-week consultation on the use of LFR by UK police , allowing interested parties and members of the public to share their views on how the controversial technology should be regulated. The department has said that although a “patchwork” legal framework for police facial recognition exists (including for the increasing use of the retrospective and “ operator-initiated ” versions of the technology), it does not give police themselves the confidence to “use it at significantly greater scale … nor does it consistently give the public the confidence that it will be used responsibly”. It added that the current rules governing police LFR use are “complicated and difficult to understand”, and that an ordinary member of the public would be required to read four pieces of legislation, police national guidance documents and a range of detailed legal or data protection documents from individual forces to fully understand the basis for LFR use on their high streets. Digital surveillance a systemic threat In June 2026, a landmark United Nations (UN) study found that the “profound” chilling effects of digital surveillance – including via facial recognition – on people’s behaviour means it can no longer be viewed as a targeted measure against specific actors, but as a systemic threat to democracy itself. It highlighted how chilling effects are amplified by the increasingly remote and asymmetrical nature of contemporary surveillance, which disproportionately harms marginalised and racialised communities , as well as those engaged in seeking accountability for human rights violations or challenging corruption. One of the major problems with the remoteness and asymmetry of modern surveillance is that those subject to it are unable to gain certainty regarding the level of scrutiny they may be placed under, in turn leaving them uncertain if they will be subject to legal action by the state. The UN study was also clear that, rather than focusing on specific tools or practices, it is more accurate to view surveillance as an interconnected ecosystem comprised of various digital infrastructures operated by both state and non-state actors. For example, from the perspectives of those subject to surveillance, the use of facial recognition at a protest , the use of spyware to target a journalist or the infiltration of digital communication channels are not seen as discrete occurrences, but instead as constituent parts of an overall surveillance ecosystem that can be leveraged against them. “The consequence is that ostensibly discrete surveillance activities in fact exist across a surveillance continuum and persist over time, leaving deep, long-term, society-wide impacts,” it said. “These impacts are enhanced with respect to marginalised and vulnerable groups, and those engaged in socio-political activities clashing with the status quo. “It is this ecosystem-related impact that plays a decisive role with respect to the degree to which chilling effects are experienced by different individuals and groups. This poses a challenge to traditional human rights law analysis as ecosystem-related chilling effects are not typical ‘cause-and-effect’ harms, whereby a specific incident gives rise to a defined harm.” Read more about police technology Metropolitan Police chief warns against law updates amid substantial tech expansion : The Metropolitan Police is to significantly expand use of AI, drones and facial recognition to ‘regain the advantage’ over criminals, but warns progress could be held back by legislation and data integration issues. Essex Police discloses ‘incoherent’ facial recognition assessment : An equality impact assessment of Essex Police live facial recognition deployments is plagued by inconsistencies and poor methodology, undermining the force’s claim that its use of the technology will not be discriminatory. How police live facial recognition subtly reconfigures suspicion : A growing body of research suggests that the use of live facial recognition is reshaping police perceptions of suspicion in ways that undermine supposed human-in-the-loop protections.
Score: 43🌐 MovesAug 20, 2026https://www.computerweekly.com/news/366649479/ICO-police-facial-recognition-audits-reveal-mixed-bag - Alibaba shares rise 34% on AI bets ahead of earnings
Alibaba Cloud said Qwen, its AI model family, was adopted by more than 90,000 enterprises in its first year.
- Progress Software Announces New Telerik and Kendo UI Release to Accelerate AI-Powered UI Development
BURLINGTON, Mass. — Progress Software, an AI infrastructure software leader, today announced the latest Progress Telerik and Progress Kendo UI release, helping to accelerate UI development with context-aware AI while advancing a new generation of applications designed for both human and AI agent interaction. By embedding AI deeply into UI generation, application modernization and development workflows, the release enables... … continue reading The post Progress Software Announces New Telerik and Kendo UI Release to Accelerate AI-Powered UI Development appeared first on SD Times .
- AI Labels Are Big Tech’s Most Basic Responsibility, Even Those Claude Watermarks
Commentary: Anthropic’s upcoming Claude watermarks are forcing us to ask hard questions about how AI fits into our daily lives.
Score: 42🌐 MovesAug 20, 2026https://www.cnet.com/tech/services-and-software/claude-watermarks-ai-labels-commentary-2026/ - TrueFoundry debuts open-source AI agent harness, claiming up to 75% lower costs
TrueFoundry debuts open-source AI agent harness, claiming up to 75% lower costs InfoWorld
- An AI-driven, avatar-based multidisciplinary video intervention to enhance recovery after gastrointestinal cancer surgery
An AI-driven, avatar-based multidisciplinary video intervention to enhance recovery after gastrointestinal cancer surgery ndorms.ox.ac.uk
- $9 billion startup Tanium brings back its cofounder as CEO amid AI upheaval
$9 billion startup Tanium brings back its cofounder as CEO amid AI upheaval Business Insider
Score: 42🌐 MovesAug 20, 2026https://www.businessinsider.com/tanium-cofounder-orion-hindawi-returns-as-ceo-leadership-shift-2026-8 - Unitree Shares Decline as Founder Flags Limits of Humanoid Robots
Unitree Shares Decline as Founder Flags Limits of Humanoid Robots Caixin Global
- China robot makers seek to turn humanoid hype into useful work
China robot makers seek to turn humanoid hype into useful work
- Serval’s super agent Catalyst creates roving background agents to identify and fix IT issues before they’re ticketed
Serval is making Catalyst , its AI agent for building enterprise automations, generally available Thursday and enabling it by default for customers — allowing teams of AI agents to decide what should be automated and then build the automation itself. Catalyst sits above Serval’s AI-native service management platform as an admin-facing “super agent.” It can inspect ticket history, standard operating procedures or natural-language instructions, identify recurring work, and draft the workflows, skills, forms, access policies, journeys and dashboards needed to automate it. Serval is also using Catalyst to create background agents that continuously inspect connected systems for emerging problems and propose fixes before an employee files a ticket. That distinction matters because enterprise service management vendors are rapidly converging on AI-assisted workflow creation. ServiceNow’s Build Agent can already translate natural-language instructions into full-stack applications, flows, scripts and other platform metadata, while its AI Agent Advisor can analyze instance records to identify automation opportunities. Atlassian’s Rovo can generate Jira automation flows from plain-English requirements, and Freshworks offers Freddy AI Agent Studio for creating service agents that act across Freshservice workflows. So Serval’s claim to differentiation is narrower — and potentially more consequential — than simply “we use AI to build workflows.” Catalyst is designed as a single administrative layer that can move from discovering an opportunity, to assembling multiple kinds of governed automation, to creating proactive agents that keep looking for new work to automate. "You just started with a single prompt, and now you’ve got enterprise-grade workflows ready to deploy that are going to solve all password resets for the entire company," Serval co-founder and CEO Jake Stauch told VentureBeat in an interview. From ticket history to working automation Serval says Catalyst analyzes existing help desk data before an organization has decided what to automate. If it finds a repetitive category of requests, it can draft the automation required to resolve those requests and stage the result for administrator review. Users can also upload an SOP or spreadsheet and ask Catalyst to turn the documented process into an executable system. Serval’s documentation says Catalyst can build workflows, author help desk skills, create onboarding and offboarding journeys, configure access-management policies, construct dashboards, investigate operational issues and debug failed workflow runs. Unlike Serval’s earlier workflow builder, Catalyst is intended to become the primary interface for configuring the platform; the company says its long-term goal is that anything an administrator can do through the UI should also be possible through Catalyst. The actual workflows are code-backed. In a demonstration, Stauch showed Catalyst taking a request to build password-reset workflows, detecting connected systems including Okta, Google Workspace and Microsoft Entra, and generating the underlying TypeScript needed to perform those actions. Administrators could then add approvals or restrict who was allowed to run the workflow. The models underneath Catalyst are deliberately swappable Serval is not building its own foundation model. Stauch said in the interview that the company uses models from “frontier labs,” runs evaluations to determine which models work best for particular jobs, and is deliberately model-agnostic. “You can swap different models in,” he said, adding that Serval also works with enterprises that build their own models. Stauch provided more detail in a May 2026 interview with Sequoia Capital , saying Serval was using both OpenAI and Anthropic models. He said OpenAI’s GPT models had performed best for end-user interactions and tool calling, while Anthropic’s Sonnet and Opus models were producing the strongest results for the code-generation side of Serval’s automation system — the workload most directly relevant to Catalyst. Serval continuously runs evals rather than automatically moving every workload to the newest model release, Stauch said. That architecture makes the underlying LLM less central to Serval’s differentiation. The company’s own documentation now lets organization administrators supply their own OpenAI or Anthropic API keys, including a compatible custom endpoint, while Stauch said the broader architecture can accommodate different models. The materials do not, however, establish that every Catalyst user gets a self-service menu for arbitrarily choosing an individual model. Serval’s pitch is instead that its proprietary value sits in the harness around those models: enterprise context and memory, integrations, generated code, permissions, approvals and the controls governing what an agent can actually do. That code-generation model is central to Serval’s pitch against ServiceNow. Stauch argues that legacy ITSM deployments often accumulate custom tables, business rules, workflows and platform-specific expertise that make seemingly simple automation changes expensive to implement. Serval, by contrast, wants administrators and business teams to describe the outcome they need and let the model generate the implementation. But ServiceNow is no longer standing still on that front. Its current Build Agent similarly creates applications and code from natural-language prompts, supports flow design and testing, and operates inside ServiceNow’s governance framework. ServiceNow’s AI Agent Studio lets customers create agents and agentic workflows, while AI Agent Advisor is explicitly designed to analyze operational records for automation candidates. The competitive question is therefore shifting from “who has generative AI?” to how many separate tools, configuration concepts and specialists are required to get from an observed operational problem to a production automation. Serval is effectively arguing that Catalyst compresses those steps into one conversational surface and a smaller platform model. ServiceNow, by comparison, now has a powerful but broader set of AI and development surfaces spanning Build Agent, AI Agent Studio, AI Agent Advisor, Workflow Studio and AI Control Tower. That breadth is an advantage for customers already deeply invested in ServiceNow, but it also illustrates the complexity Serval is attacking. ServiceNow itself notes that Build Agent is aimed at admins and developers who understand and can support what it generates. Atlassian is moving in the same direction from a different starting point. Rovo can generate “if this happens, then that happens” automation flows from natural-language descriptions, while Jira Service Management increasingly supports agents that triage, investigate and execute service work. Freshworks ’ Freddy AI Agent Studio likewise emphasizes agents that resolve requests end-to-end, with prebuilt IT and HR agents and more than 30 workflow templates. Catalyst’s differentiator, then, is not that rivals cannot generate an automation from a sentence. It is Serval’s attempt to make the entire automation lifecycle itself agentic. Building agents that look for trouble before a ticket exists That approach becomes clearest with Serval’s background agents. Rather than waiting for a help desk request, a background agent can run on a schedule across connected systems, correlate signals and draft a remediation. In one customer example provided by Serval, an agent correlated network incidents across two offices using switch telemetry, DHCP data and historical tickets, ruled out hardware and wireless interference, traced the issue to configuration drift, and generated a remediation workflow for an administrator to approve. “Most AI agents today wait for an employee to ask a question or submit a ticket,” Stauch said. “We believe the future is AI that acts before an employee ever submits a request.” That framing also highlights a philosophical difference in Serval’s pitch. The startup does not want service management to revolve around creating, routing and tracking better tickets. It wants the system to eliminate as many requests as possible by turning repeated support work into executable automation. "A lot of the code written in enterprises has nothing to do with software engineering," Stauch explained. "It’s actually internal automations and other scripts for the company, and so we use that technology to build a better service management platform." Serval's pitch to enterprises is that it can largely automate those scripts. And the governance model is critical because Catalyst can generate code and potentially initiate changes across production systems. Serval says Catalyst inherits the permissions of the user operating it and remains scoped to that user’s team workspace. Everything it builds starts as a draft, and organizations can restrict publishing privileges or require formal review and approval before an automation becomes active. Customer data remains customer-owned, with several deployment options Those controls also extend to the enterprise data Catalyst examines. Stauch said Serval is intended to operate as the customer’s system of record and told VentureBeat that “they own all the data.” Serval’s current Master Services Agreement is more precise: customers retain rights, title and interest in both their “Customer Materials” — a category that includes records, documents, workflows, prompts, inputs and configurations — and the output Serval generates from them. Serval receives the rights necessary to process that information to provide, maintain, support and secure the service. Serval also says it does not retain or use customer materials, inputs or outputs to train, fine-tune or improve its own or third-party AI models. Its Data Processing Addendum identifies Serval as the processor of customer personal data and allows processing for operating the service, responding to support requests, diagnosing issues and protecting the platform, while authorized subprocessors can also be involved. Serval’s acceptable-use terms say it maintains a current list of AI subprocessors and model providers for customers. Where that data resides can vary by deployment. Stauch said customers can use Serval as a cloud SaaS service, run it on-premises or place it in their own VPC. Serval’s self-hosting documentation now describes two fuller options: a Serval-managed single-tenant deployment inside an AWS account owned by the customer, or a self-managed deployment on the customer’s Kubernetes cluster in any cloud or on-premises environment. In the AWS option, Serval says it operates the installation without persistent IAM access to the customer’s AWS account. There are therefore two distinct access boundaries for enterprise buyers to consider. At the Catalyst level, the agent can only reach data, integrations and automations available to the user and team workspace under which it is operating. At the platform level, Serval and authorized subprocessors necessarily process customer information to deliver and support the service, subject to the company’s contractual confidentiality and data-processing terms. That makes Stauch’s informal statement that Serval “doesn’t touch” customer data better understood as an ownership and deployment claim, rather than a literal assertion that the service never processes it. Ramp and other customers provide an early test Customer deployments provide some evidence that the faster-build thesis can translate into operational changes, although the metrics come from Serval’s own case studies. Corporate expense and financial technology firm Ramp says in a Serval case study that Catalyst has made workflow building 50% faster and helped extend Serval across roughly 10 teams, including IT, finance, facilities, people and talent, legal and business operations. In one hardware replacement program, Serval says Ramp automated 600 laptop replacements and saved 150 hours, leaving approval as the principal human step. The more telling Catalyst example may be what happened afterward. Ramp had already automated laptop replacement when Catalyst suggested splitting its shipping logic into separate office and home workflows to reduce errors. The company also says employees outside IT now use Catalyst for analytics, bulk ticket operations, workflow troubleshooting and HR process automation. Other Serval deployments show the broader operating environment Catalyst is meant to configure. Mercor says it has onboarded more than 4,000 external experts through Serval automations and expanded the platform across seven teams. Together AI says Serval automates 95% of its just-in-time infrastructure access requests, with approval and auditing controls around sensitive access. Perplexity says Serval automatically handles more than half of its incoming IT requests and all employee onboarding. Those deployments extend beyond Catalyst itself, but they demonstrate the type of cross-system automation substrate Catalyst is now being asked to build and maintain. Serval says more than 90% of customers adopted Catalyst as their starting point for automation during beta. Catalyst is generally available Aug. 20 and will be enabled by default for all Serval organizations. Pricing and the battle with ServiceNow Pricing is customized depending on the size of the deployment and is not publicly listed on Serval's website or documentation. Serval describes a single platform fee and typically runs a pilot to determine expected deployment and usage. Stauch said the software license can be similar to ServiceNow’s, but argues total cost of ownership can be substantially lower because customers require fewer implementation and maintenance services. "The total cost of ownership is going to be dramatically less — usually half as much, sometimes 10 to 20% of the total cost of ownership of ServiceNow," Stauch said. "But the actual software license fee is not necessarily going to be all that different." Serval's origin story and history Serval was founded in 2024 by Stauch and CTO Alex McLeod, former Verkada product and engineering leaders, after they repeatedly heard IT customers complain about overburdened help desks and the limitations of established IT service-management software. Serval has positioned itself as an AI-native alternative to platforms such as ServiceNow and Jira Service Management, combining help-desk ticketing, access management, asset management and workflow automation within a single system. Serval and Sequoia Capital describe the company’s goal as moving IT software beyond merely recording and routing requests toward resolving them automatically. The company can operate as an organization’s primary IT service-management system or add automation to an existing one. Its publicly identified customers include Perplexity, Mercor, Clay, Verkada and Together AI. Serval says customers can automatically resolve more than half of their incoming IT requests; its Together AI case study reports automation of 95% of that customer’s just-in-time access requests. Investor interest accelerated rapidly in late 2025. Serval announced a $47 million Series A led by Redpoint Ventures in October, bringing its funding at that point to $52 million. In December, it raised another $75 million in a Sequoia-led Series B at a $1 billion valuation , lifting total capital raised to approximately $127 million; Redpoint, Meritech Capital and General Catalyst also participated. Serval told Reuters that revenue had grown 500% since August 2025 and that it was expanding beyond IT into operational work performed by human resources, finance and legal departments. The big test for enterprise customers For enterprise buyers, Catalyst’s biggest test will be whether its compression of the automation lifecycle survives contact with large, messy, highly customized environments. ServiceNow can now generate applications and discover automation opportunities with AI. Atlassian and Freshworks are adding increasingly capable agentic automation to their own service platforms. Serval therefore cannot rely on natural-language creation alone as its moat. Its stronger wager is that an AI-native platform can make the administrative layer itself agentic: continuously finding repetitive work, building the necessary resources across the service stack, exposing generated code for review, and proposing the next automation before an administrator has opened a workflow designer. If Catalyst works at that scope, the competitive unit is no longer the ticket — or even the workflow. It is the system that keeps turning an enterprise’s operational history into new automation.
- LAYING THE GROUNDWORK FOR AI-POWERED CYBERSECURITY
The EU’s Action Plan on Cybersecurity and AI lays the groundwork for new, AI-powered approaches to IT security but organizations need to ensure their security foundations are fit for purpose.
- Tech Mahindra and ServiceNow expand partnership to take enterprise AI beyond pilots
The multi-year partnership will focus on scaling AI deployments, developing industry-specific solutions and building governance frameworks for enterprise use The post Tech Mahindra and ServiceNow expand partnership to take enterprise AI beyond pilots appeared first on Express Computer .
- HoneyBook bets on agentic AI to streamline small business operations with its new Claude connector
Autonomous artificial intelligence agents have already penetrated the offices of large, global enterprises. Now, HoneyBook is trying to bring that same capability to independent businesses with the launch of HoneyBook MCP, recently released as a connector for Anthropic’s AI assistant Claude. The move addresses a real gap. McKinsey’s recent State of AI survey found that […] The post HoneyBook bets on agentic AI to streamline small business operations with its new Claude connector appeared first on AI News .
- Why agent projects stall after the demo👨🔧
Build agents that survive production
- Oura Restructures Tech Leadership in AI Push
CEO Tom Hale says the company is only scratching the surface when it comes to the type of individualized health guidance AI can provide. Now he’s looking to double down with two key hires.
Score: 42🌐 MovesAug 20, 2026https://www.wsj.com/cio-journal/oura-restructures-tech-leadership-in-ai-push-6c85660c?mod=rss_Technology - Personalize the content you see on Search, Discover, and News
A woman looks at a phone outdoors
Score: 41🌐 MovesAug 20, 2026https://blog.google/products-and-platforms/products/search/personalize-search-discover-news/ - AI is making Africa’s cheapest smartphones harder to afford
Africans planning to buy a new smartphone this year may have to spend more as rising AI demand makes the cheap devices that helped millions get online more expensive to produce.
Score: 41🌐 MovesAug 20, 2026https://techcabal.com/2026/08/20/ai-is-making-africas-cheapest-smartphones-harder-to-afford/ - Synopsys Updates CXL IP Portfolio for AI-Era Infrastructure
Synopsys’s CXL 4.0 IP aims to help designers build faster, more flexible and secure disaggregated computing architectures as AI systems demand more memory capacity and bandwidth. The post Synopsys Updates CXL IP Portfolio for AI-Era Infrastructure appeared first on EE Times .
Score: 41🌐 MovesAug 20, 2026https://www.eetimes.com/synopsys-updates-cxl-ip-portfolio-for-ai-era-infrastructure/ - Alberta to host second town hall on AI data centres after jeers, boos at Wednesday event
Residents lined up to voice opposition, express distrust in the government
Score: 41🌐 MovesAug 20, 2026https://www.theglobeandmail.com/canada/alberta/article-alberta-ai-town-hall-data-centres/ - Alberta minister met with jeers at AI data centre town hall
Alberta's technology minister was met with jeering, badgering and boos at a town hall discussing the province's plans for artificial intelligence data centres. Nate Glubish says the province has carefully crafted a strategy to protect local water supplies, electricity bills, and land use, but attendees say they don't trust the Alberta government to regulate the industry's risks.
Score: 41🌐 MovesAug 20, 2026https://www.theglobeandmail.com/canada/video-alberta-minister-met-with-jeers-at-ai-data-centre-town-hall/ - PoliceAI outlines plans to test and assure AI for policing
Scaling the use of artificial intelligence (AI) throughout UK policing will require a slower, iterative approach to testing and assuring the technology to ensure its effectiveness and promote public trust, says the PoliceAI interim director. Initially announced by the Home Office in January 2026 alongside a raft of other policing reforms , PoliceAI was formally launched in June 2026 to act as a national delivery mechanism for the integration of AI tools into frontline policing across England and Wales. Speaking with Computer Weekly, the organisation’s interim director Alex Murray elaborates on how the creation of a centralised laboratory function and coordinating capacity for AI in policing can help to deliver a range of benefits, particularly in regard to setting standards, building consistent governance frameworks and evaluating the effectiveness of automated tools before deployment. Highlighting the current 43-force model of England and Wales, Murray says that outside of the Metropolitan Police (the Met) , the vast majority of police forces simply do not have the capacity and skills to effectively evaluate AI tools on their own. He adds that in validating the use of AI systems centrally, the organisation will play an important role in the technology’s diffusion throughout policing, by eliminating the need for costly duplication and providing a pipeline that takes tools from proof-of-concept to national delivery. Murray also stresses the importance of constant, iterative assurance of AI policing tools, which he says is needed to address concerns around bias, reliability and accuracy, as well as help to build public trust in the systems being deployed. He adds that, if used responsibly, AI-powered tools can deliver a range of benefits to policing, particularly when it comes to reducing manual processes, alleviating bureaucratic pressures and freeing up an officer’s time. “What a cop on the street has to do [is] now profoundly different because of the digital revolution we’ve been in, and people have been extracted from the streets because there is so much to do,” he says, adding that a single case can see officers working through a terabyte worth of data from phones and CCTV alone. “In many areas, AI can alleviate the pressure, and it’s a bit of a cliché, but also put the humanity back in policing, by releasing cops to do what cops are good at – which is speaking to people, human-to-human – and understanding what’s going on.” A centralised lab function For Murray, a key facet of PoliceAI’s work is how the creation of a centralised lab function can help promote consistency and accountability in how disparate police forces across the country are using new technologies. “The checks and balance framework is substantial and building all the time … We’re not interested in AI, we’re only interested in responsible AI,” he says, noting that PoliceAI – with the help of AI academics and ethicists such as Marion Oswald – have already created a “responsible AI checklist” to help inform and shape the practices of chief constables. This includes questioning the origins of data, how models have been trained, whether bias has been identified or eliminated, how officers deal with AI outputs and whether the deployment is proportional (a key legal test for UK policing). We’re not interested in AI, we’re only interested in responsible AI Alex Murray, PoliceAI Asked about the problems associated with historically biased policing data – as certain groups or demographics are over-represented in policing databases via their disproportionate contact with police – and how PoliceAI are attempting to stop those patterns from being projected into the future as a result of that data being fed into models, Murray was clear the organisation currently has no plans to evaluate or assure predictive policing tools used for the forecasting of crime . He adds while PoliceAI may approach this AI use case in the future, it would have to be done in a way “where you eliminate as much bias as possible”, including racism. “If ever we were to write a forecasting tool, or a tool that helps you decide where you’re going to put police assets, like hotspot policing tools, it’s probably number one in the lab agenda to say, ‘How are we going to eliminate that bias?’” he says. “It is a very live, sometimes emotive debate with many opinions, and we in PoliceAI and policing generally need to be very wise, accepting there is bias, doing the best to eliminate it, but still trying to prevent crime.” Accountability Murray noted while police chiefs will always be vicariously liable for technology deployments by their force, PoliceAI will be able to take accountability for the initial evaluation of the tools, or bring in outside help from bodies such as the National Physical Laboratory (NPL), to ensure there are layers of accountability. “Without PoliceAI, you’re not going to have that centralised lab function that can work on open source processes for evaluating the effectiveness of a tool, which can be like an evaluation harness that stays live with a product, which we can publish,” he says, adding that suppliers can then openly use the same tests to validate their own tools before selling into policing. “Only the massive forces would ever be able to achieve close to that, so it’s sensible to do that one in one place.” On the importance of public trust, Murray says “it is a builder for AI, not a hindrance”, and that police forces will therefore be expected to conduct a range of due diligence, including equality, community impact and data protection assessments. “We might actually slow stuff down so we can get stuff out, have focus groups, speak to community groups and say, ‘This what we’re doing’ – that’s a really strong pillar of why police AI exists,” he says, adding that while this may take longer, the potential loss of public trust or legitimacy will make the job harder in the long run. “We are actively building a public registry of AI being used by policing, so that A) police forces can see what everyone’s using and prevent duplication, and B) the public can see it and the framework that sits behind it.” Taken together, Murray says “these are all things that should assure the majority of people”. Iterative approaches and red lines Asked about the need for iterative assurance of AI tools, Murray says “we see that as absolutely necessary”, highlighting how models can drift from their original purpose or parameters when there are contextual changes to the environment they operate in. “For example, with decision-assistance tools or CCTV analytics tools, we can have a method of evaluation and a corpus of synthetic or real imagery or logs or whatever it is, which we can then point to any product that is offering a claim in that area,” he says. “Then when there is a model change or there is a change in circumstances, it’s a sort of point and shoot. We’ve got the library, we’ve got the methodology. Just keep using it and keep updating it.” He adds that in terms of rolling out new tools, there will be a clear pathway, where systems are tested in lab settings (and not operationally) , before moving into limited beta deployments with “loads of safeguards” in place. “Let’s try small and grow, grow, grow until you are absolutely sure it’s safe. And those safeguards need to be planned throughout the adoption of an AI,” he says. “If it hasn’t been proved and demonstrably been shown to be robust and transparent and explainable, we’re not interested.” Murray adds that, as it stands, the use of generative AI tools in policing would be a clear red line, because models cannot currently meet these standards: “A human in the loop for consequential decisions is [also] a red line.” Read more about police technology UK police to launch £1.4m AI call system : The system, developed by the Home Office and National Police Chiefs’ Council, will use AI software to assess why someone is calling the 101 non-emergency police line. At least six police forces have racked up contracts with Palantir worth £7.8m : A joint investigation by Computer Weekly and Good Law Project reveals six police forces have used Palantir’s data analytics software, while several other forces are believed to have access through regional taskforces. Essex Police discloses ‘incoherent’ facial recognition assessment : An equality impact assessment of Essex Police live facial-recognition deployments is plagued by inconsistencies and poor methodology, undermining the force’s claim that its use of the technology will not be discriminatory.
Score: 41🌐 MovesAug 20, 2026https://www.computerweekly.com/news/366648764/PoliceAI-outlines-plans-to-test-and-assure-AI-for-policing - Can AI Draft Discovery Requests?
Explores whether AI can generate accurate discovery requests for legal teams.
- Six in 10 Leaders Bet Big on Robots. Only Four in 10 Are Ready.
NEWS HIGHLIGHTS Six in 10 senior business and IT leaders, robotics specialists, government and healthcare officials expect their organizations to operate robot fleets within five years. Leaders predict full-scale robotics deployment could double operational output. 67% believe they will be ready to manage a mixed human-robot workforce by 2030. 74% believe workforce planning and robotics … The post Six in 10 Leaders Bet Big on Robots. Only Four in 10 Are Ready. appeared first on Newsroom .
Score: 41🌐 MovesAug 20, 2026https://newsroom.intel.com/artificial-intelligence/6-in-10-leaders-bet-big-on-robots-only-4-in-10-are-ready - Zero Networks expands Palo Alto Networks integration to AI agent control
Zero-trust security startup Zero Networks Ltd. today expanded its integration with Palo Alto Networks Inc., adding automated threat containment and a set of controls aimed at artificial intelligence agents. The two companies first integrated their products in February 2025. Zero Networks discovers and tags assets without agents. Palo Alto Networks firewalls take that context in […] The post Zero Networks expands Palo Alto Networks integration to AI agent control appeared first on SiliconANGLE .
Score: 41🌐 MovesAug 20, 2026https://siliconangle.com/2026/08/20/zero-networks-expands-palo-alto-networks-integration-to-ai-agent-control/ - AI startup Micro1 wants to challenge Google's winning bid for Spirit Airlines data with a higher offer
AI startup Micro1 wants to challenge Google's winning bid for Spirit Airlines data with a higher offer Business Insider
Score: 40🌐 MovesAug 20, 2026https://www.businessinsider.com/micro1-challenges-google-bid-spirit-airlines-data-2026-8 - China puts robocops on traffic duty, minus the arrest powers
China puts robocops on traffic duty, minus the arrest powers Reuters
Score: 40🌐 MovesAug 20, 2026https://www.reuters.com/technology/china-puts-robocops-traffic-duty-minus-arrest-powers-2026-08-20/ - Agentic AI in government just hit the hard part: deciding what a machine may decide
The United Arab Emirates (UAE) has been early in adopting artificial intelligence for 9 years. It published a national AI strategy in October 2017 and, days later, created a ministerial post to run it, making Omar Sultan Al Olama the world’s first minister of state for artificial intelligence at 27. In the years since, it […] The post Agentic AI in government just hit the hard part: deciding what a machine may decide appeared first on AI News .
Score: 40🌐 MovesAug 20, 2026https://www.artificialintelligence-news.com/news/agentic-ai-in-government-uae-classification/ - SoftBank Corp. and Ericsson Conduct Japan’s First Trial of Ericsson AI in RAN on a 5G Commercial Network
SoftBank Corp. and Ericsson Conduct Japan’s First Trial of Ericsson AI in RAN on a 5G Commercial Network ソフトバンク
- One in five enterprises can't stop a runaway AI agent's spending in real time
Enterprise AI teams have stopped betting on a single orchestration platform. The median enterprise now runs three at once — not by accident, but because none of them fully trusts a single vendor to run the show, according to VB Pulse data . This is not just to avoid vendor lock-in and retain flexibility (although that’s a big part of it). There’s still a lot of uncertainty, even distrust, in vendors’ security and permissioning capabilities. Enterprises want the ability to impose their own. Microsoft leads on primary usage today, while Anthropic leads by a wide margin in what enterprises are considering next. But enterprises still struggle with many challenges, notably around token usage and visibility into agent spending. These findings are from an ongoing analysis of how enterprises are actually deploying and using AI: Their platforms of choice, what guides their decision-making, what they prioritize, their AI expectations, how they control costs, and whether their AI is actually agentic or still a chatbot in an "agent" label. VB Intelligence is getting feedback from builders actually in the trenches: software and machine learning (ML) engineers, product and program managers, and data/AI/analytics VPs and directors. Concerns around retaining visibility and control Across 107 enterprises, agentic orchestration has become decidedly plural. The survey found that the majority of enterprises are not committing themselves to any one model: 85% are using two or more orchestration tools; 64% are using three. Just 15% run a single orchestration platform. Microsoft AI Foundry/Copilot Studio shows up in 70% of stacks, OpenAI’s Agents SDK in 68%, and Anthropic’s Claude Platform in 47%. Builders surveyed are also to some extent using Google’s Enterprise Agent Platform, LangChain/LangGraph, Salesforce Agentforce, Amazon Bedrock, and LlamaIndex. Augmenting vendor tools, 22% of builders run custom in-house orchestration. This trend of hybridability is only expected to continue. More than half of respondents (53%) said the primary control plane will be hybrid by the end of 2026. Fourteen percent expect to use a provider-managed service, 13% plan on a custom in-house control plane, and 11% are betting on external platforms that are abstracted away from model providers. Dovetailing with this, more than two-thirds of respondents plan to change platforms within the year: 15% in the next three months (or sooner), 24% in three to six months, and 28% in six to 12 months. Claude Agent SDK is a top tool under consideration; 43% of builders are exploring the Anthropic-built model. Roughly one-third are looking at Google’s Enterprise Agent Platform, another 31% are focused on custom in-house orchestration, and 25% are investigating OpenAI’s options. Perhaps learning from the lock-in of the early cloud days, enterprises aren’t choosing one “winner.” They are deliberately building for a future where multiple orchestration platforms, models, and agents work with each other across a hybrid control plane. Generally speaking, respondents are pleased with the platforms they’ve been running, rating them 4.17 out of 5 for overall satisfaction. But they are less satisfied with ease of implementation (rating it 3.91 out of 5) and value for the money (3.63 out of 5). Keep an eye on these ratings as orchestration platforms and AI roadmaps mature. Where enterprises are putting their money Enterprise buying logic is now based on a mix of several factors. Beyond flexibility (cited by 29% of respondents), top considerations include security and permissions (17%), production reliability (15%), and control over agent execution (15%). Just one out of 10 identify model gravity — native alignment with a state-of-the-art base model — as important in purchasing decisions; 8% name ease of development, 4% cite total cost of ownership, and just 2% cite latency and memory performance. Spending also reflects enterprise priority on visibility, security, and control. Builders are investing the most in agent monitoring and debugging (31%) and security and permissions enforcement (30%). Workflow tooling accounts for another 19%. That's a shift from VentureBeat's prior wave a month earlier , when workflow tooling led orchestration spending outright. Enterprises are largely optimizing for task completion reliability (30%), multi-step workflow management (27%), developer productivity (23%), and operational stability (13%). Just 7% of respondents name end-user experience as a top priority at this point, indicating that many are still focused on orchestration at this point rather than UX. Essentially, enterprises are signaling that workflow succeeds when it carries multiple steps to completion. Simplifying development and end-user experiences could become a larger concern when platforms are actually in place. The visibility problem Builders’ biggest concerns when choosing platforms center around control and oversight. They don’t want vendors to constrain their ability to see what their agents are doing on a given platform. Factors top of mind include security and permissioning limitations (37%), vendor lock-in (23%), limited visibility and observability (22%) and inflexibility around models and tools (16%). Meanwhile, in these early days of AI agents, enterprises still struggle to control agent token use; one in five still can’t stop a runaway agent’s spending in real time. Builders are using various strategies to try to keep agent spending in line: 30% rely on native platform controls (built-in budget caps or throttling) and 25% have built custom gateway plumbing (proxy middleware to intercept runaway agents). A quarter of respondents use dynamic routing to offload heavy work to low-cost models, and 21% still rely solely on reactive monitoring, such as post-hoc logs; these enterprises have no real-time kill switches. One interesting finding: unlike the prior wave, organization size makes little difference in fiscal control maturity — 18% of enterprises with 10,000-plus employees exercise only reactive control, compared to 23% of smaller ones. Clearly, while enterprises recognize the problem with spend, many have not yet instrumented their stacks to rein it in. Most enterprises still aren't running true multi-step agents Builders polled were asked to honestly assess their tech stacks; the consensus seems to be that ‘agents’ are slowly but surely progressing beyond chatbots wrapped in that fancier label. Here’s how the numbers break down: A small number of respondents (2%) report that 76 to 100% of their systems are advanced and largely autonomous; 14% say 51 to 75% of their systems are complex, multi-agent pipelines; and 47% report that 26 to 50% of their systems are true orchestration. On the other end of the spectrum, 35% say just 1 to 25% of their systems are true orchestration; most deployments remain basic assistants, and 3% are still only deploying chatbots. This is in line with VB’s June Pulse survey: 71% of respondents said a quarter or fewer of their deployed “agents” can autonomously complete multi-step work, and just one-tenth say they have deployed agents at scale. There’s no doubt that enterprises are building control planes and infrastructures for agents; but for many of them, the true agentic wave is still off on the horizon.
- BNY's Barker on what really concerns clients about AI and payments
Jennifer Barker, the bank's global head of payments & trade and depository receipts, says people are more worried about the outcome than how transactions occur. That creates a greater bank role for intelligent routing or payment facilitation, where customers automatically get the best option with little work on the front end.
Score: 40🌐 MovesAug 20, 2026https://www.americanbanker.com/payments/news/bnys-barker-on-what-really-concerns-clients-about-ai-and-payments - EXCLUSIVE: How a Texas student blew the whistle on a rogue AI hacking attempt
EXCLUSIVE: How a Texas student blew the whistle on a rogue AI hacking attempt Reuters
Score: 40🌐 MovesAug 20, 2026https://www.reuters.com/world/how-texas-student-blew-whistle-rogue-ai-hacking-attempt-2026-08-20/ - Japan left behind by US and China in AI era patent race
Japan left behind by US and China in AI era patent race Nikkei Asia
Score: 39🌐 MovesAug 20, 2026https://asia.nikkei.com/business/technology/japan-left-behind-by-us-and-china-in-ai-era-patent-race - LG CNS deploys robot security guard at Seoul luxury complex
LG CNS will bring a four-legged security robot to Tower Palace, the luxury residential-commercial complex in southern Seoul, marking the first time the company has moved its physical AI platform from factories and warehouses into a residential setting. The IT services and digital transformation arm of LG Group signed an agreement on Wednesday with Tower PMC, a property manager that oversees roughly 100 high-end complexes, including Hannam The Hill, Acro Seoul Forest and Raemian One Bailey. The t
- Humanoid robot trial for directions at Little India MRT station
Humanoid robot trial for directions at Little India MRT station The Straits Times
- How Generative Recommenders Are Redefining RecSys at Scale
Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and...
Score: 39🌐 MovesAug 20, 2026https://developer.nvidia.com/blog/how-generative-recommenders-are-redefining-recsys-at-scale/ - Anthropic’s Opus language problems may be creating a hidden cost for AI coding
Anthropic’s Opus language problems may be creating a hidden cost for AI coding InfoWorld
- Shield AI’s X-BAT named official autonomous aircraft of the Army-Navy Game
RENO, Nev. (August 20, 2026) — Shield AI, the defense-tech company building the world’s best AI pilots and next-generation aircraft, today announced its X-BAT has been named the Official Autonomous Aircraft of the Army-Navy Game presented by USAA, one of the most storied rivalries in American sports. Shield AI also joins as an Associate Sponsor […]
Score: 39🌐 MovesAug 20, 2026https://shield.ai/shield-ais-x-bat-named-official-autonomous-aircraft-of-the-army-navy-game/ - How AI Could Hollow Out the U.S. Military
CSET’s Emelia Probasco shared her expert insight in an op-ed published by Foreign Affairs. In her piece, she examines how the U.S. military’s growing use of AI could affect military decision-making and human judgment, particularly as the Pentagon works to integrate increasingly capable AI systems into operations and training. The post How AI Could Hollow Out the U.S. Military appeared first on Center for Security and Emerging Technology .
Score: 39🌐 MovesAug 20, 2026https://cset.georgetown.edu/article/how-ai-could-hollow-out-the-u-s-military/ - AI skills in cybersecurity job postings doubled as junior hiring stalls
The AI Workforce Consortium today reported that the share of cybersecurity job postings across the Group of Seven economies that ask for artificial intelligence skills doubled over the year, and junior hiring has not moved with them. This was the first in a new series of spotlight reports, this one on the impact of AI […] The post AI skills in cybersecurity job postings doubled as junior hiring stalls appeared first on SiliconANGLE .
Score: 39🌐 MovesAug 20, 2026https://siliconangle.com/2026/08/20/ai-skills-in-cybersecurity-job-postings-doubled-as-junior-hiring-stalls/ - Stampli cuts launch hours by 68% using ChatGPT Work
With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days.
- YouTubers worry that a new policy will lead to more AI slop
YouTubers worry that a new policy will lead to more AI slop USA Today
Score: 38🌐 MovesAug 20, 2026https://www.usatoday.com/story/entertainment/tv/2026/08/20/youtube-view-count-policy-ai-slop/91371488007/ - NanoClaw comes to Slack, letting you create persistent AI agent teams and colleagues from a single message
Adding an AI agent to Slack sounds appealing to many enterprises — but, as VentureBeat has experienced ourselves first hand — the reality is often far more complex and clunkier than it first seems. Now NanoCo ., the company behind the hit open source, enterprise-friendly, autonomous AI agent harness NanoClaw (a more sandboxed, lower code version of OpenClaw), is hoping to make it just as easy as typing a Slack message. To go one step further: the company's new NanoClaw Slack integration lets human users spin up entire teams of agents with their own specialized skills, workflows, and even custom avatars, all from a single Slack prompt. "In the next 12 to 18 months, everyone on a team will be a manager of agents," NanoCo CEO and co-founder Gavriel Cohen told VentureBeat in an exclusive interview. Furthermore, the NanoClaw agents can work together in channels and shared Slack Canvases, and can even be messaged outside of Slack on other platforms like Telegram or WhatsApp, letting their human colleagues ping them across messaging platforms, just as they would their fellow humans. “I think this is agents arriving natively in Slack for the first time,” Cohen added. “In the past, you had to do all these weird things to try to have multiple different agents behind the scenes using the same bot, and now every agent gets its own identity in Slack — its own avatar, its own face, its own name. You can tag them. They can tag each other.” For enterprise teams, the more consequential part is persistence and separation. NanoClaw is not presenting the additional workers as invisible subagents that disappear after one task. Each can be given its own role, memory context, instructions and permissions, creating a structure closer to a small digital department than a single chatbot with a long prompt. As with the original open source version of NanoClaw released in January 2026 , developers and enterprises can further choose whichever underlying large language model (LLM) they wish to power their NanoClaw agents, optimizing for performance, cost, or other combinations of factors. From a single NanoClaw Slack agent to a whole specialized team For a new installation, NanoClaw’s current setup process starts by cloning the project and running its nanoclaw.sh installer, which walks the user through dependencies, credentials, building the agent container and pairing a first messaging channel. NanoClaw’s website says the installer takes a user “from a fresh machine to a named agent you can message,” with Slack among the supported channels. Cohen described the Slack-specific flow to VentureBeat as a significant simplification over building a traditional Slack bot. Previously, he said, a user would have to navigate Slack’s administrative and developer interfaces, create an app, collect secrets, API keys and tokens, and then move those credentials into wherever the bot was running. With the new integration, the NanoClaw setup instead offers a Connect Slack option. The user names the agent, authenticates, chooses the NanoClaw Add to Slack option and goes through Slack’s installation and authorization flow. Once authorized, the first agent can appear in Slack and begin communicating with the user. The important distinction is that this initial authorization is largely a one-time workspace connection. Slack’s Marketplace listing says users “connect a workspace once,” after which NanoClaw can provision each additional agent as its own Slack bot, complete with its own name, generated avatar and identity. Those agents continue running on the customer’s infrastructure and connect to Slack over Socket Mode. NanoCo says it does not store the agents’ Slack tokens; according to the Marketplace listing, those tokens remain on the user’s machine. Slack’s standard administrative controls still sit around that system. Organizations can apply their normal app-approval policies to the NanoClaw integration, while NanoClaw’s Marketplace listing says the app’s Home tab displays the agents provisioned in a workspace and lets users revoke individual agents or disconnect the workspace entirely. The result is less a one-click replacement for NanoClaw’s underlying infrastructure than a one-time bridge between that infrastructure and Slack: users still own and operate the agent runtime, but once the bridge is authorized, the agents themselves can create and coordinate additional Slack-native colleagues without sending the user back through manual app configuration each time. Behind the scenes, Cohen said, the lead agent has a Model Context Protocol (MCP) tool that can create new agents and define their instructions, personas, skills and tools; another tool can place them into shared rooms. The agents come prepared to work with Slack Canvas and can communicate with every human user on the Slack Channel, and with one another. The interaction itself is deliberately simple. Rather than opening a separate agent builder every time a new role is needed, Cohen said users can tell the agent they already have what kind of colleague or team they want. “Your agent in Slack, you can say, ‘Create me another agent to handle my code reviews. Create another agent to review the contributor articles. Create a team of agents that reviews contributor articles from different perspectives.’ And then your agent can create new agents, and they just pop up in the sidebar and send you messages.” That means a developer could ask for a product manager, architect, implementation agent, code reviewer and testing agent, then give each a different toolset and have them hand work between one another. Cohen said the testing agent, for example, could have access to a testing environment while the review agent carries code-review-specific skills and the product agent monitors user feedback. Cohen argues that this division of labor is more than cosmetic role-playing. “There are advantages in terms of giving each one specific skills, instructions, and tools for different tasks,” he said. “I can have, let’s say, a code review agent, a code testing agent, a code writing agent, and I can have them in a loop.” If the implementation agent runs into an ambiguity, he added, it can tag the product or architecture agent for clarification rather than forcing one general-purpose model to hold every responsibility and tool in the same context. Agents work together with humans on a share Slack Canvas A supplied demo screenshot shows the same pattern applied to marketing: a lead agent named Nano creates Atlas for strategy, Sage for content, Echo for social, Scout for outreach and Compass for SEO and analytics. The agents introduce themselves in the same Slack conversation and begin coordinating work, with Atlas noting that it had added an item to Canvas so the task would not get lost. Users do not have to specify every detail up front. Cohen said someone could give the lead agent exact review procedures, priorities and required tools, or leave more of the configuration to the agent based on its existing context and memory. The design also tries to avoid a familiar multi-agent failure mode: bots endlessly triggering one another. NanoCo says the agents reply only when tagged, while comments left on work in Canvas can be routed back to the agent responsible for that piece. And the model can extend beyond teams of task-specific bots created by one person. Cohen described a workplace where individual employees each have persistent agents that can communicate with one another under human-defined policies. “Each person having their own agent means that I could have my agent and you have your agent in Slack, and your agent can ask my agent questions,” he said. “Maybe I’m out of the office for the day. Your agent can ping my agent and ask a question about availability, and I can set some policies about whether my agent can answer or if I need to give approval.” That pushes the concept closer to organizational delegation: some agents specialize by function, while others effectively represent individual employees and the context they have accumulated. Cohen said the agents can be equipped with browser and internet access, memory, coding capabilities and other tools, while newly created agents arrive with built-in support for Canvas work, agent-to-agent communication and spawning still more agents. Slack is opening the door to more third-party agents The underlying Slack change is broader than NanoClaw. In April, Slack, a Salesforce product, announced the ability to add external AI agents to the messaging platform directly, initially pointing to Vercel and Lovable and saying those integrations were coming in late May. Slack said the deployment mechanism automates OAuth, manifest configuration and environment setup so an externally built agent can be brought into the workspace without being rebuilt specifically for Slack. Salesforce’s newly published Slack Code page now names NanoClaw alongside Lovable, Hyperagent, Superhuman, n8n, Vercel, ChatGPT, LangChain, Runlayer and Skydive, and says Add to Slack can bring agents from those platforms into Slack in a few clicks with their own identity. Slack is already crowded with AI assistants. OpenAI, for example, lets ChatGPT workspace agents be deployed into Slack channels, where they can answer questions, perform tasks through connected systems and output files. Slack also supports Claude and custom Agentforce agents. NanoClaw’s differentiation is therefore not simply “AI in Slack.” It is the ability for an already-running agent to create additional, independently addressable teammates from inside the conversation itself. NanoCo calls that a first for Slack; that specific market-first claim is the company’s. “Add to Slack means one message can spin up a full team of NanoClaw agents, working right alongside people in Slack,” Josh Milas, director of product management at Slack, said in the supplied announcement. How NanoClaw differs from Claude Tag, ChatGPT agents and Agentforce in Slack NanoClaw is not alone in trying to turn AI from a sidebar chatbot into something resembling a persistent Slack colleague. Anthropic’s Claude Tag , which began rolling out in beta to Claude Team and Enterprise customers in June, may be the closest conceptual comparison. Administrators can give @Claude access to selected channels, tools, data sources and codebases; everyone in the channel can then delegate work to it by tagging it. Claude remembers relevant information from the channels it inhabits, can work asynchronously over hours or days, and, when administrators enable its “ambient” behavior, can proactively flag information or revive unresolved work without waiting for another prompt. Anthropic says separate Claude identities can also be scoped to different use cases so that, for example, a sales Claude does not share its memories or tools with an engineering Claude. The difference is in how those digital coworkers are provisioned and organized . Claude Tag’s documented workflow is administrator-led: admins pair Claude with Slack, decide which channels, tools and information each Claude identity can access, set spending limits and then expose those identities to employees. Within a given channel, Anthropic describes “one Claude that interacts with everyone.” Its public documentation does not describe an end user asking that Claude to create several new, independently named Slack bots on demand. NanoClaw’s model is almost inverted. After an organization connects its NanoClaw installation to Slack once, NanoClaw says an existing agent can itself provision additional agents from a conversational request, with each new worker receiving its own Slack bot identity, name, generated avatar and token and running back on the customer’s infrastructure. OpenAI’s ChatGPT Workspace Agents occupy another point on that spectrum. Business, Edu and Enterprise customers can build reusable agents in ChatGPT, give them instructions, models, files, apps, custom MCP connections and schedules, and then attach those agents to Slack channels. Builders assign each agent a unique Slack handle and can configure it either to respond only when mentioned or to respond automatically to relevant messages in a channel. But the construction still happens primarily through ChatGPT’s agent builder: OpenAI’s setup documentation tells users to create the agent first and then add Slack as a channel. Under the hood, the Slack handles rely on Slack user groups managed by the ChatGPT Agents app, rather than NanoClaw’s model in which every provisioned agent is itself a separate Slack bot. Salesforce’s Agentforce similarly allows organizations to create multiple specialized agents that employees can DM or @mention inside Slack, and it arguably provides the most conventional enterprise administration model of the group. Companies build the agents in Agentforce Builder, often starting from Slack-specific templates for jobs such as customer insights, employee help or onboarding, and can add subagents and actions that let them search information, create Canvases or perform other work. Once configured and activated in Salesforce, administrators bring those agents into Slack for employees to use. That makes Agentforce powerful for organizations already centering identity, data and workflows on Salesforce, but again places agent creation before deployment rather than making creation itself something an existing Slack agent can perform during a conversation. That distinction helps clarify what NanoClaw is actually adding to an increasingly crowded market. Slack itself now provides an Agent Kit for developers and a deployment standard for agents built on outside platforms, automating pieces such as OAuth, manifests and environment configuration. Claude Tag, ChatGPT Workspace Agents and Agentforce all demonstrate that persistent, specialized AI teammates inside Slack are no longer novel on their own. NanoClaw’s more unusual bet is recursive provisioning: Slack becomes not merely the place where workers invoke agents, but a place where an existing agent can assemble additional named agents, assign them roles and put them together in a channel as a working team. There are tradeoffs to the different approaches. Claude Tag comes with Anthropic-managed models and centralized administrative controls, including channel-specific permissions, audit logs and token-spending limits, while also offering proactive “ambient” behavior that NanoClaw’s supplied materials do not claim in the same way. ChatGPT Workspace Agents offer a managed agent builder, schedules, app connections and organization-level publishing and access controls. Agentforce ties agents closely to Salesforce permissions, enterprise data and predefined business actions. NanoClaw instead emphasizes self-hosting, open-source modification and separate agent identities , shifting more control — and more operational responsibility — to the organization running it. The result is less a direct replacement for those systems than a different answer to the same emerging question: whether enterprises want a small number of centrally configured AI assistants, or an environment in which employees and existing agents can continuously create specialized digital colleagues as new work appears. How NanoClaw got here NanoClaw began far from the enterprise collaboration market. Cohen, a former Wix engineer, launched it under the MIT License on Jan. 31, 2026, as a deliberately small, security-focused alternative to OpenClaw. The original pitch was that a personal agent with access to messages, files and tools should run inside an OS-isolated container rather than directly on the host, and that the orchestration layer should remain small enough for a developer or security team to understand — an initial core of roughly 500 lines of TypeScript and a design centered on container isolation and a minimal single-process architecture. The project then moved steadily toward enterprise infrastructure. In March, NanoClaw partnered with Docker to run agents inside Docker Sandboxes, using stronger MicroVM-backed isolation for workloads that may install packages, modify files and launch processes. In April, NanoClaw 2.0 added Vercel’s Chat SDK and OneCLI’s credential gateway, allowing organizations to define policies around sensitive actions and require human approval before credentials are injected for protected requests. By May, Cohen and his brother Lazer Cohen had formed NanoCo around the project and raised a $12 million seed round led by Valley Capital Partners, with Docker, Vercel, monday.com and others participating. The commercial strategy is to keep NanoClaw open source while selling managed, organization-wide deployments and “professional assistant” infrastructure to enterprises. The company now says NanoClaw has surpassed 250,000 downloads and 30,000 GitHub stars. That open-source structure remains central to Cohen’s pitch as NanoClaw moves deeper into workplace infrastructure. “You’re really able to now integrate an open-source agent into Slack that you fully control,” he said. “You can change all those configurations. Plus, you can fork NanoClaw and completely rewrite or change behaviors — create your own memory system, your own coding harness, agent harness. Whatever you want to do, you can do. Total freedom.” Persistent agents, but infrastructure stays under the user’s control Cohen said NanoClaw remains self-hosted: an organization can run it on a local machine or its own cloud VM, with agent data stored there. The same agent can also appear across Slack, WhatsApp or Telegram while retaining the same memory, workspace and tools, although each messaging surface uses a separate session. NanoClaw can pull recent context across those sessions so the agent can maintain continuity without merging every chat history into one stream. NanoClaw’s documentation likewise describes a multi-channel architecture in which the same agent can retain one workspace and memory while maintaining separate per-channel sessions. “This is all self-hosted,” Cohen said. “You’d be running this on your computer or on your virtual machine in the cloud, and that data is stored on your computer or on your [virtual machine] VM. This could be an open-source model running on your Mac Mini, and your data isn’t going anywhere besides your Mac Mini and then into Slack.” The cross-channel continuity is also intended to make an agent feel less like a Slack-specific bot and more like a persistent colleague that happens to be reachable through Slack. Cohen said the same agent could exist in Telegram, WhatsApp and Slack with access to the same memory, files and tools. The conversations remain separate sessions, but they share a workspace and persistent context so the agent can carry knowledge from one surface to another. That architecture matters when an organization starts creating many agents. Cohen said one agent can see its own sessions across channels, but not another agent’s private sessions by default. NanoClaw’s current documentation likewise describes agents running in their own sandboxes and configurable model providers, with Claude Code as the default and Codex, OpenCode and local Ollama models available as alternatives. There is one cloud dependency for the new Slack flow. Cohen said NanoCo operates a small service that handles Slack provisioning requests and avatar generation. He said it does not receive users’ messages or agent memory. Continued commitment to open source NanoCo is not charging for this community Slack capability, according to Cohen, and is absorbing the provisioning-service and avatar-generation costs. Users can still incur their own model inference and hosting expenses, so that does not make a deployed agent team cost-free in practice. NanoCo says the integration is available through the Slack Marketplace, subject to normal workspace app approval and governance. Slack says workspace owners and administrators can require apps to be approved before installation. Cohen framed that decision as part of NanoCo’s broader open-source strategy rather than a standalone monetization play. “We’re not making any money off this one. This one is for the community, really,” he said. “We know that in the long run that’s going to benefit NanoCo as a company. As NanoCo grows and builds out capabilities, those go back to the open source. I think that’s the new model of open source, where we’re not trying to monetize every bit of value we bring to the community.” Whether companies get there that quickly will depend less on how easily agents can be created than on whether IT teams can govern their permissions, memory, spending and failure modes at the same pace. NanoClaw is betting that the next problem is managing the digital coworkers that appear once that barrier is gone.
- AI is becoming a financial engineering business
AI is becoming a financial engineering business Fortune
Score: 38🌐 MovesAug 20, 2026https://fortune.com/2026/08/20/ai-becoming-financial-engine-amit-joshi-imd/