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  • Google Cloud generative AI automates council planning operations

    Government ministries are deploying Google Cloud generative AI across municipal agencies to automate council planning operations.

    Public sector administration handles vast volumes of unstructured data that delay infrastructure development. The UK central government established a target to construct 1.5 million new homes by 2029. Local planning authorities encounter administrative backlogs caused by dense paperwork, delaying these development timelines.

    To address these constraints, the Ministry of Housing, Communities and Local Government (MHCLG) and the Department for Science, Innovation and Technology (DSIT) expanded two machine learning tools designed to accelerate municipal processing. Speaking at the Google Cloud Summit London, officials confirmed the nationwide deployment of the ‘Extract’ application and the progression of the ‘Augmented Planning Decisions’ (APD) prototype.

    Lila Ibrahim, Chief AI Readiness Officer at Google DeepMind, said: “The UK has an opportunity to build the homes our communities need, but local councils face a mountain of paperwork. That’s why we’re co-creating a sophisticated planning tool directly with councils to solve real-world bottlenecks.

    “This will help significantly cut decision times, freeing up planners to focus on the future to get Britain building faster.”

    Householder applications – which include routine domestic modifications such as loft conversions or property extensions – account for nearly 70 percent of all planning applications submitted annually. Evaluating these standard submissions manually requires planning officers to spend hours cross-referencing regional policy documents, historical archives, and unstructured PDF files.

    Such a repetitive evaluation process consumes administrative hours that would otherwise support major infrastructure and commercial developments. The deployment of automation targets this administrative distribution, aiming to reduce application decision timelines by 50 percent.

    Core capabilities of the Google Cloud generative AI tools

    Engineers at MHCLG and the government’s applied AI team, the Incubator for AI (i.AI), built the Extract tool internally using Gemini foundation models. Following trials across more than 20 local planning authorities, administrators expanded the application to every council in England.

    Extract parses unstructured data locked within legacy PDF records, converting hundreds of pages of historical planning documentation into structured digital datasets within minutes. Operational data from the trial phases indicates that the tool will eliminate roughly 255 hours of manual data entry per council annually. This reduction allows local authorities to reallocate personnel to complex evaluation tasks.

    Integrating large language models into public sector workflows requires enterprise-grade security environments. Local authorities process sensitive civic records, requiring strict risk management protocols to prevent data exposure.

    The government hosted the Gemini models on Google Cloud to establish a protected operating environment where data sovereignty is maintained. The cloud environment features active security controls to block malicious inputs, including prompt injection attacks. This technical framework ensures that sensitive municipal data remains secure during both testing and production computing cycles.

    The APD system, meanwhile, acts as an analytical assistant for municipal planning officers by automating four primary administrative tasks:

    1. The system consolidates incoming documentation by pre-processing data backlogs, flagging missing information gaps, and extracting core geographical site data onto a unified user interface for officer review.
    2. The software identifies relevant national and local zoning laws, assesses compliance margins, and appends precise policy citations for manual verification.
    3. The application parses public consultation letters, summarising stakeholder objections or historical legal precedents.
    4. The model generates initial drafts of final evaluation reports, including the technical rationale and recommended approval conditions.

    Protocols dictate that human planning officers retain final decision-making authority over every application. The software does not automate final approvals or rejections independently. Staff members review every line of text generated by the machine learning models, modifying the analytical reasoning before validating the report.

    To maintain regulatory accountability, the APD prototype records its internal processing steps sequentially. This mechanism establishes an auditable chain of thought, creating a verification trail for every processed application to support the officer’s final determination.

    Local council planning trials and scaling timelines

    The development of the APD prototype relies on a collaborative framework linking public sector administrators with engineering teams from Google Cloud, Google DeepMind, and Faculty.

    The alpha version undergoes live testing within three local authorities: the London Borough of Barnet, Dorset Council, and the London Borough of Camden. Testing across these distinct regional jurisdictions provides developers with varied municipal datasets to test the software against diverse local policies. 

    Central planners intend to complete the alpha phase and deploy the APD tool to all 300-plus English local authorities by 2027. Google Cloud provides the elastic computing infrastructure required to manage the thousands of concurrent inferencing queries generated during daily operations.

    Paul Maltby, Director of Public Services at Faculty, commented: “The English planning system is clogged up. Planning officers are forced to spend half their time reviewing applications to convert an attic, putting those for housing estates and warehouses on hold.

    “Built with planning officers, our AI system will take the drudgery out of reviewing simple planning applications so they can make quick decisions. It will let planning officers focus on the major developments that matter, and crucially, let families improve their homes without months of delay and uncertainty.”

    Naisha Polaine, Executive Director for Growth at Barnet Council, added: “The tool’s ability to collect relevant information, undertake a provisional assessment, and draft the foundations of a report has the potential to save significant officer time spent working on the administration of planning applications and direct this to speeding up the decision-making process for residents. In turn, this will contribute significantly to delivering our house building growth targets in the borough.”

    The coordination between MHCLG, i.AI, Google DeepMind, and Faculty establishes a structured division of labour for enterprise software engineering. Public ministries define the policy guidelines and statutory boundaries, while external technical partners engineer and deploy the underlying model architectures.

    The successful integration of these systems demonstrates the feasibility of hosting advanced language models within a secured public cloud infrastructure to process core administrative workloads and modernise public service delivery.

    See also: EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline

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  • Insurers pivot AI strategy toward core risk underwriting

    AI investments by insurers are now expected to generate tangible business value beyond mere efficiency.

    According to findings in the 2026 Evident AI Index, insurers are now embedding AI technologies into workflows that directly influence underwriting discipline and capital allocation.

    Christian Preece, Insurance Director at Evident, says: “For years, insurers have competed on AI ambition, but now the focus is shifting from what insurers are building to the value they’re creating. In itself, it’s a sign of AI maturity to have the internal capability to measure these figures and be confident enough to disclose them.

    “As the first industry leaders disclose hard return on investment data, they’re providing the kind of evidence that shareholders and boards have been looking for in light of increasing concerns around the costs of AI, and we can expect to see more insurers going public in the coming year.”

    While the broader insurance workforce experienced a contraction of 2.2 percent over the past year, the AI-specialist headcount expanded by 32 percent across the 30 insurers tracked in the report. This personnel shift highlights a transition from building data foundations to the integration and optimisation of business-specific AI use cases.

    Data engineering remains a component of this investment, yet its relative share of the talent stack is declining as roles focused on AI development and software implementation gain priority. AI specialists now represent one in every 50 employees at insurers included in the Index.

    Executive structures are also adapting to these requirements. Nearly 40 percent of the insurers indexed now designate a senior leader with explicit responsibility for AI. Most of these appointments occurred within the last 12 months, creating a new level of executive oversight for AI-driven growth.

    This governance is vital as firms shift from isolated point solutions toward agentic AI systems that coordinate actions across multiple stages of the policy administration and claims lifecycle. Notably, the adoption of agentic AI has surged, with one in four newly disclosed use cases now showing evidence of agentic orchestration, compared to one in twenty only six months prior.

    Zurich sets an example

    Zurich serves as an example of this transition, rising from 12th position to 4th in the global rankings by emphasising a shared platform model over decentralised experimentation.

    The insurance giant deployed ZurichIQ, a modular generative AI platform integrated into underwriting, claims, legal, and service operations. This architecture provides a unified environment for various functional tools, such as PolicyIQ for contract comparisons and GuidelinelQ for enforcing underwriting standards.

    Hurdles in such deployments typically involve maintaining oversight across diverse business lines. Zurich manages these risks through a dedicated committee that governs AI investment and model risk management. The platform approach allows the insurer to push AI capabilities into daily production while maintaining a consistent governance framework, which is reinforced by internal training programs like the £1.3m AI apprenticeship initiative.

    Ericson Chan, Group Chief Information & Digital Officer at Zurich, said: “Being recognised as the biggest AI growth insurer in the Evident AI Index is not simply a reflection of technology adoption; it signals a broader transformation from use cases to enterprise-wide execution and change.

    “This recognition reinforces our conviction in our AI360 strategy, embedding intelligence into workflows, decisions, and customer outcomes across the value chain. AI is no longer a technology initiative. It is becoming Zurich’s operating system.”

    Focus on risk selection and ROI

    With claims typically accounting for 60 to 80 percent of premium income, even minor improvements in fraud detection and risk selection produce a disproportionate financial impact compared to general administrative cost reduction.

    Insurers are now directing venture capital and internal innovation efforts toward data sources that enable more dynamic analysis of climate volatility and cyber threats. A critical marker of this maturity is the ability to quantify and disclose financial returns.

    Manulife, Generali, and Intact Financial have led this effort, publicly reporting AI-driven value. Projections indicate these three firms will generate over $1 billion in AI-driven value by the end of their respective reporting periods. This transparency provides the hard data shareholders demand regarding the costs of AI deployment, effectively mandating more rigorous performance measurement across the sector.

    Success in the next phase of industry adoption depends on the ability to translate these technical investments into better underwriting results. Market leaders Allianz (which now holds the largest AI talent pool in the industry and has registered 900 AI use cases worldwide) and AXA maintain top positions by demonstrating sustained investment across innovation, talent, and transparency pillars.

    Barbara Karuth-Zelle, Member of the Board of Management and Group COO at Allianz, commented: “AI didn’t change our ambition. It accelerates how we deliver on it at scale.

    “Behind this ranking are thousands of moments: a claim processed faster, a customer experience reimagined, a partner better connected, a colleague freed up for what truly matters. And we are determined to keep going—an inspiring, transformative journey.”

    See also: Accenture: Consumers show growing trust in AI shopping agents

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  • EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline

    The European Union has published its AI content labelling playbook, a voluntary Code of Practice meant to help companies meet transparency rules that become law across the bloc on August 2 onwards. The European Commission released the final Code on 10 June, setting out practical steps for the businesses that build and use generative AI to mark and label what their systems produce.

    The Code itself is optional. The obligations it points to are not. They sit under Article 50 of the EU AI Act, and from August 2, 2026, they apply whether or not a company signs the Commission’s guidance. Signing simply gives a business a recognised way to show it complies.

    What the AI content labelling rules actually require

    From August, two things must be clearly flagged. Deepfakes and AI-generated or AI-manipulated text published on matters of public interest have to carry a label. Anyone chatting with an interactive AI system, such as a customer-service bot, also has to be told they are dealing with a machine. 

    The Commission frames it as a way to help users spot AI-made or AI-altered material and narrow the space for deception. “Europeans have a right to know whether what they see, hear or read has been made or altered by AI, especially when such content can shape public debate,” said Henna Virkkunen, the Commission’s executive vice-president for tech sovereignty, security and democracy. 

    She cast the Code as a practical route to labelling that AI providers and deployers can follow before the rules bite in August. The Code splits the work between the two sides of the AI supply chain. The companies that build generative models are asked to mark their output in a machine-readable format, so it can be detected further down the line. 

    The companies that deploy those models, the ones putting AI to work in real products, handle the visible labelling, which, for public-interest AI text, applies when the content has gone out without human review or editorial control. To keep it workable, the Code leans on open technical standards and a common EU icon, meant to give users a consistent visual cue and spare businesses from inventing their own.

    None of this is the final word. The Code is now open for signatures, and the Commission is urging all providers and deployers to sign. It still needs the Commission and the AI Board to judge it adequately, and separate Commission guidelines are due to clarify the law and cover what the Code leaves out. Drawn up by six independent experts with input from more than 180 stakeholders, it is the first instrument to tackle AI content labelling under the Act.

    The timing leaves little slack. Companies serving European users have under two months to work out what they need to label and how, and to decide whether to sign. Plenty of the harder detail still rests on guidelines the Commission has yet to publish.

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  • HarmonyOS 7 steps into the AI gap Apple left open in China

    Four days after Apple confirmed that Siri AI would not launch in China, Huawei took the stage in Dongguan and declared HarmonyOS 7 the beginning of the agent era. The gap Apple could not fill, Huawei has moved into with an architecture built specifically for it.

    What HarmonyOS 7 actually changes

    The headline change is the HarmonyOS Intelligent Agent Framework 2.0, which restructures the OS around what Huawei calls an “intent-as-service” model, compressing what previously required multiple app navigation into a single natural-language command.

    At the centre of this is Xiaoyi, Huawei’s AI assistant, rebuilt from a conventional voice tool into what the company describes as a system-level intelligence agent. Xiaoyi now controls over 2,100 system-level capabilities and coordinates with more than 2,000 third-party AI agents developed across Huawei’s developer ecosystem. 

    Richard Yu, chairman of Huawei’s Consumer Business Group, framed the release as a generational inflexion point: “In 2019, HarmonyOS was born. In 2023, native HarmonyOS apps began. In 2026, HarmonyOS enters the Agent era.”

    Underneath sits openPangu 2.0, Huawei’s updated foundation model, with 505 billion parameters in its Pro version and 92 billion in the Flash variant, both supporting 512K context windows. On-device models at 30 billion parameters are due on Kirin chips by autumn 2026. HarmonyOS 7 also delivers a 15%-plus performance improvement over HarmonyOS 6.1, according to Huawei’s own benchmarks. 

    The task execution rate claimed is above 90%, though that figure is Huawei’s own and has not been independently verified.

    The market position is consolidating

    The numbers shared at HDC 2026 reflect a shift that has already happened. In Q1 2026, HarmonyOS held 19% of China’s smartphone OS market against Apple iOS at 16%, with Android at 65%. HarmonyOS first overtook iOS in China in Q2 2025, according to Counterpoint Research.

    That trajectory matters more than any single feature because China is simultaneously the market Apple cannot currently operate in at the AI level and the one Huawei has fully optimised for. The agent network Xiaoyi coordinates includes partnerships with Ctrip for travel planning and Ant Medical for health data analysis, services woven into the Chinese consumer stack that Apple’s architecture does not reach.

    Where the limits are

    The scope of the challenge to Apple needs calibrating. HarmonyOS 7 is currently in developer beta, with the stable consumer release expected this autumn. The 2,000-plus AI agents are anchored in the Chinese app ecosystem. 

    The platform counts more than 400,000 applications and services, which is significant but still a fraction of what Apple’s App Store carries. Huawei’s ambitions to take HarmonyOS international remain aspirational for now.

    There is also a design note that softens any clean divergence narrative: HarmonyOS 7 adopts the same Liquid Glass aesthetic Apple introduced with iOS 26, and Samsung brought to One UI 9. Visual language converges even as underlying architectures and regulatory environments pull in opposite directions.

    The longer arc

    HarmonyOS exists because of US sanctions. When Huawei lost access to Google’s Android in 2019, it built its own OS from necessity. By January 2026, over 90% of Huawei devices were running the fully homegrown version. That forced independence is now a structural advantage in the one market where Apple cannot currently deploy its headline AI feature.

    Sanctions built the platform. Regulatory friction cleared its path.

    See also: Siri AI arrives with Google inside, and much of the world is locked out

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  • The AI off switch: How Anthropic’s export controls sparked a global AI sovereignty scramble

    Anthropic export controls turned an abstract policy fear into a live one last week: as of June 13, 2026, one US government directive took the company’s two most powerful AI models offline for users everywhere, including, briefly, Anthropic’s own foreign-born employees, and set off alarm bells across Europe and Canada about who really controls the AI the world runs on.

    The mechanics were startling in their speed. The reaction abroad has been louder still.

    Launch to lockdown in four days

    On June 9, 2026, Anthropic made Claude Fable 5 and Claude Mythos 5 generally available, the public face of a model class the company had developed under controlled access since April through a programme called Project Glasswing. Fable 5 was described as a Mythos-class model made safe for general use, state-of-the-art on nearly all tested benchmarks, with strong performance in software engineering, scientific research, and autonomous work. 

    Mythos 5, the more capable sibling, stayed restricted to Glasswing partners and selected biology researchers. Four days later, it was gone. Anthropic said it received an export control directive to suspend access to Fable 5 and Mythos 5 at 5:21 pm ET on June 12, with the letter not explaining the specific security concern in detail. 

    Unable to filter users by nationality in real time, the company said it had to “abruptly disable” access for all customers to comply. The order, issued by Commerce Secretary Howard Lutnick in a letter to CEO Dario Amodei, called for suspending all access by any foreign national, whether inside or outside the United States. 

    The jailbreak at the centre of it

    Washington cited national security, specifically, a method for “jailbreaking” Fable 5, or getting around its safety guardrails. Anthropic disputed the severity, saying the technique amounted to a limited capability to review programme code and identify errors, something rival models, including OpenAI’s GPT-5.5, can also do. 

    The government’s account is sharper. David Sacks, co-chair of the President’s Council of Advisers on Science and Technology, said on X that the administration asked Amodei to either fix the vulnerability or pull the model from deployment, and that Amodei refused. Sacks pressed the contradiction directly: “In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the US government believe; nor is that kind of minimising language consistent with Anthropic’s brand as the AI safety company.

    The Wall Street Journal reported the move was also shaped by Amazon CEO Andy Jassy, who told Treasury Secretary Scott Bessent and other officials that Amazon researchers had used Fable 5 prompts to obtain information that could aid cyberattacks. Amazon is one of Anthropic’s largest investors. A spokesperson said it is “not uncommon for governments to seek our counsel on potential security risks,” but declined to share details. 

    A fight that started months before

    None of this began last week. The dispute erupted earlier this year after Anthropic insisted its technology should not be used for mass surveillance or fully autonomous weapons systems, infuriating Pentagon chief Pete Hegseth. President Trump ordered every federal agency to stop using Anthropic’s technology, and Hegseth designated the company a “Supply-Chain Risk to National Security“, a label, the company’s lawsuit notes, usually reserved for foreign adversary firms like Huawei. 

    Anthropic sued to reverse the blacklisting, warning it could jeopardise “hundreds of millions of dollars” in revenue. The result is a company simultaneously deemed too dangerous for the US government’s own use and too dangerous for foreign use, a contradiction not lost on observers. Dean Ball, an AI policy expert who briefly served in the Trump administration, called the order “simply cartoonish,” noting that an administration willing to export advanced AI chips to China now wants to ban Britain and every other non-American from using Anthropic’s best models.

    The export controls heard around the world

    Outside the US, the response went straight past the jailbreak debate and landed on a single, uncomfortable realisation: a tool embedded in companies, research institutions, and public services worldwide had been switched off by a foreign government, with an email, in an afternoon.

    The European Commission confirmed it is examining the fallout. Spokesperson Thomas Regnier said the new generation of highly capable AI models offers real benefits, including for cyber-defence, but raises serious cybersecurity concerns that need addressing, adding that “contingency measures taken in this light should not be discriminatory against partners.” 

    European politicians were blunter. French commentary framed the decision as an accelerator of the geopolitical battle over AI, with the argument that “Europe cannot settle for being an open market dependent on technologies designed, funded, and controlled elsewhere.” Finnish MEP Aura Salla said Europe “cannot continue to increase its technical potential by relying on access that can be turned off by a foreign government overnight.” The timing sharpened the point: the Commission had published its Technological Sovereignty Package — including a Cloud and AI Development Act — on June 3, just nine days before the shutdown. euronews + 2

    The unease crossed the Atlantic. Speaking in Ireland ahead of the G7 summit, Canadian Prime Minister Mark Carney said the restrictions show the dangers of overreliance on a limited number of American providers, framing it as a lesson in diversification. “The situation we’re in collectively right now with Mythos and Fable is something that can happen with overreliance on certain models,” Carney said, flagging AI as a major topic for the summit. In Britain, AI and Online Safety Minister Kanishka Narayan said the episode should drive deeper investment in the country’s own AI industry. 

    What happens next

    Anthropic’s position has not moved. It maintains that applying this standard across the industry “would essentially halt all new model deployments for all frontier model providers.” The route back runs through the Commerce Department’s Bureau of Industry and Security, where a licence is now required for export, re-export or domestic transfer of the two models, with individually validated licences needed for reinstatement and civil penalties for non-compliance. 

    Sacks framed the off-ramp plainly: fix the jailbreak, lift the control. “The ball is in Anthropic’s court,” he wrote. For the governments now watching from outside, the patch is almost beside the point. The lesson many of them have already drawn is that access to frontier AI is no longer purely a matter of price or product; it is a matter of whose jurisdiction holds the switch. Last week, the answer turned out to be Washington’s, and a lot of capitals didn’t like how that felt.

    See also: Anthropic IPO filing marks AI maturing into enterprise utility

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  • Accenture: Consumers show growing trust in AI shopping agents

    Consumers are showing a willingness to let AI agents take on more shopping-related tasks, according to new research from Accenture.

    The company’s 2026 Consumer Pulse Research, based on a survey of 25,590 consumers across 16 countries, found that 74% of respondents would trust a personal AI agent more than their best friend to make a purchase on their behalf.

    The report described this as a move beyond the use of chatbots or search tools. In this context, an AI agent refers to software that can act on a consumer’s behalf within set permissions. It can shop, negotiate, resolve complaints, manage subscriptions, and, in some cases, complete purchases.

    Consumers are ready to delegate

    The survey found that 74% of consumers would allow an AI agent to handle routine tasks. These include deal negotiation, complaint resolution, subscription renewals, and product reorders.

    Accenture said this level of delegation does not mean consumers are ready to hand over every decision. Instead, the findings suggest that consumers are more open to delegating parts of shopping that feel repetitive, time-consuming, or low-risk.

    The report also found that 32% of consumers would ask an AI agent to make a purchase decision on their behalf within defined limits. These limits could include budget and brand preferences, with other conditions set by the user.

    In that scenario, the AI agent would choose the best available option, but the consumer would still review and approve the purchase before payment. The report categorised this as delegated decision-making, separate from task execution and autonomous purchasing.

    Autonomy still has limits

    A smaller group of consumers is open to AI agents completing purchases without final approval. The report found that 9% of respondents would allow an agent to initiate and complete purchases within defined boundaries.

    The payment stage recorded lower openness to autonomous agent decisions. Accenture said only 12% of consumers are open to agents making purchase decisions autonomously at the payment stage.

    The report identified several conditions that affect consumer willingness to delegate more control. These include data safeguards, configurable permissions, and instant override options. Clear recourse, platform reputation, and perceived neutrality also affect trust.

    Consumers are more comfortable with AI agent autonomy in parts of the journey where effort is high and emotional stakes are lower. The report pointed to negotiation and post-purchase support as areas where consumers showed greater openness.

    The report said recurring services ranked highest across stages of delegation, while lifestyle and travel purchases showed a sharper drop as autonomy increased.

    It also said consumers are more likely to keep control over choices linked to identity or personal enjoyment. A consumer may delegate routine grocery restocking but still want to choose a hotel room, clothing item, or experience directly.

    What it means for brands

    The report said AI-assisted shopping requires brands and retailers to make product information clear and machine-readable. If consumers use agents to compare options, pricing, availability, policies, and claims will also need to be easy for agents to assess.

    AI agents can compare brands using structured attributes and verified claims. They can also weigh price-to-value ratios and fulfilment records. The report said this affects how brands appear across digital channels, including search engines, marketplaces, and social platforms.

    The report found that 56% of all consumers would tell their AI agent which brands to consider. Among behaviorally loyal consumers, 37% said they would allow an agent to switch brands if it found a better fit.

    The report linked brand switching to factors such as fit, price, availability, and service performance.

    Accenture also found that consumers are interested in agents that can work across providers. The report said 61% want an agent that can shop across multiple grocery retailers on their behalf, while 71% want an agent that can plan and book a complete trip across airlines, hotels, and activities.

    Brands and retailers need product data, pricing, availability, policies, and claims to be readable by the systems agents use to evaluate options, according to the report.

    The main reasons cited were existing knowledge of shopping preferences, trust built through service and support, and access to a broad selection of products and services.

    The report listed several possible roles for brands and retailers in AI-assisted commerce. Some may build their own agents, while others may integrate data, inventory, and services into platforms that consumers already use.

    The report cited verified information, clear inventory, transparent pricing, and reliable fulfilment data as factors that can help agents evaluate brands more easily.

    It also found that 71% of consumers expect generative AI to influence at least half of their spending decisions over the next 12 months.

    The report also found that 63% of consumers want agents to shop for their “idealised self.” Examples include helping them make healthier choices or stay within budget. Some respondents also want agents to support more intentional upgrades.

    Among active generative AI users, 26% said they had already bought a more expensive item because AI increased their confidence in the decision. The same proportion said AI had led them to increase their basket size.

    Stores still matter

    The survey also asked consumers how AI could affect stores. It found that 87% believe AI will affect the role of stores. Another 31% said stores will become more important for creating moments of enjoyment.

    The findings show lower openness to full automation than to routine task delegation. It shows a more selective pattern, with consumers delegating routine or lower-risk tasks while retaining control over purchases that involve personal preference, risk, or emotional value.

    The report said some brand evaluation could take place inside agent-led comparison systems before consumers visit a website, app, or store.

    (Photo by Growtika)

    See also: Visa ChatGPT integration enables AI agent retail purchasing

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  • Coinbase for Agents: Automating portfolio trading with AI

    Coinbase for Agents connects AI to financial execution channels to automate trading and payments directly from user portfolios.

    Large language models process vast quantities of data but lack direct integration with active financial portfolios. Individuals frequently employ these models to evaluate market developments or research investment opportunities. These software tools possess the capacity for complex reasoning but cannot execute financial transactions on behalf of the user.

    Coinbase for Agents enables autonomous digital entities to execute trades, process payments, and manage balances within user-defined parameters.

    Terminal-based systems use command-line interfaces to manage the connection. This route fits development environments such as Claude Code, Codex, or OpenClaw. The command-line architecture integrates directly into established local development toolchains. This implementation path lowers token expenditure during high-frequency tasks and accommodates extensive local customisation. Setting up this configuration involves installing specific skill packages via the Coinbase Developer Platform documentation and generating dedicated API keys.

    Web-centric software arrangements, meanwhile, rely on the Model Context Protocol. MCP establishes a direct integration path for web-based agent environments like ChatGPT or Claude Web. It permits a rapid connection via a single account login procedure. This method functions without requiring manual API key creation or complex local configuration files. A remote MCP option will become available in the near future that will allow individuals to link their financial profiles using standard single sign-on features without writing code.

    Portfolio allocation and execution

    Account holders can program specific distribution rules, instructing an automated agent to establish or maintain targeted asset ratios.

    As an example, a portfolio manager might select a target distribution consisting of 60 percent Bitcoin, 20 percent Ethereum, and 20 percent Solana. The agent executes this directive over extended timeframes spanning multiple months. It assesses real-time pricing data and positions limit orders to purchase assets when market valuations decline by five, ten, or fifteen percent. The software captures these brief market pullbacks to accumulate assets automatically.

    Coinbase’s current system supports spot and derivatives trading but is working on expanding the protocol to include index funds, standard corporate equities, commodities, and prediction markets.

    The autonomous assistant monitors available cash balances around the clock to keep funds productive. It distributes idle capital to generate rewards or highlights specific asset positions that require direct human attention.

    Integrating the x402 protocol allows these agents to interact with external commercial systems. Coinbase introduced this agentic payment protocol last year to provide software agents with a standard method for economic interaction. Agents deploy capital via this protocol to purchase computing resources, analytical models, and proprietary market data to inform their decisions. Upcoming x402 integrations will standardise these automated purchases across web services.

    Data collection determines the efficacy of automated trading logic. An agent assigned to execute a dollar-cost averaging plan into Ethereum uses historical metrics to optimise market entry. The system retrieves thirty days of hourly pricing statistics to pinpoint historical low points during the day and can then establish a recurring daily market purchase of $20 timed precisely to those optimal windows. The automated routine executes daily for two weeks based on a single initial command.

    Security controls and compliance

    Agents operate exclusively inside isolated portfolios to safeguard broader financial holdings. This design prevents the autonomous entity from viewing or accessing unauthorised balances.

    Users already retain total control over the operational boundaries. However, upcoming platform updates will introduce explicit rulesets for fine-tuned governance. Users will dictate maximum transaction volumes, specific permitted assets, and absolute spending limits.

    The platform subjects all agent-initiated payments to standard transaction monitoring and “Know Your Transaction” validation. Users receive automated compliance verification without building internal monitoring systems.

    Coinbase’s latest product launch marks the expansion of a broader consumer product suite that began with the 2024 launch of AgentKit, which provided tools for embedding crypto wallets into software systems. The subsequent introduction of the x402 protocol and the release of Coinbase for Agents finalises the financial execution layer.

    Alternative connection options exist for everyday investors who prefer simple interfaces. Coinbase Advisor operates natively inside the primary consumer application. This integrated agent provides automated recommendations and financial guidance directly to users. The assistant holds formal registrations with both the SEC and the CFTC as a financial advisor. For retailers, commercial merchants can deploy Coinbase Payments to accept automated transfers from these autonomous systems.

    See also: Visa ChatGPT integration enables AI agent retail purchasing

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  • Visa ChatGPT integration enables AI agent retail purchasing

    Visa has linked its payment infrastructure to ChatGPT, enabling AI agents to recommend retail products and execute financial transactions.

    The deployment removes human intervention from the final stages of the retail funnel. Autonomous agents will now process user prompts, evaluate merchant catalogues, and complete the checkout process using Visa’s payment rails at any supporting merchant.

    Previous retail AI integrations restricted automated purchasing to single-vendor environments. Retailers built proprietary chatbots confined entirely to their own inventory. Visa’s integration bypasses closed-loop architecture.

    The payment giant connects the open-web reasoning capabilities of a large language model directly to a universal transaction network. Users simply command the agent to procure an item, and the model handles the vendor selection, product comparison, and financial settlement.

    Enterprises should be aware that commercial transactions will increasingly execute without a human buyer ever seeing a retailer’s website, digital advertisement, or promotional email.

    Restructuring retail data for AI agent buyers

    Marketing departments design campaigns around human psychology, emotional triggers, and visual merchandising. AI agents operate on pure data evaluation.

    When ChatGPT receives a mandate to purchase a specific product type, it parses technical specifications, aggregated sentiment scores, and pricing structures. Display ads and user interface optimisations hold zero weight in the model’s selection criteria.

    Retailers will need to expose machine-readable inventory data. Search engine optimisation transitions into language model optimisation. The algorithms driving ChatGPT rely on structured data feeds, clear API documentation, and explicitly-formatted product attributes to evaluate whether an item meets the user’s parameters. Merchants failing to maintain high-quality, structured metadata will find their products invisible to the autonomous agent.

    Personalisation occurs entirely on the user’s device or within the user’s secure LLM profile. The AI retains the consumer’s past preferences, sizing requirements, budget constraints, and brand affinities. Instead of the retailer attempting to guess the consumer’s needs through tracking cookies and site behaviour, the agent arrives at the digital storefront with a highly-specific procurement mandate.

    Completing a transaction without human intervention requires a secure, automated handshake between the reasoning engine and the payment gateway. Visa provides the financial layer necessary to establish trust in an inherently untrusted agentic environment. Traditional checkout flows require manual data entry, CAPTCHA verification, and two-factor authentication loops. These mechanisms block autonomous agents.

    Visa implements programmatic tokenisation to solve the authentication problem. The user pre-authorises the ChatGPT environment with specific spending parameters. When the LLM decides on a purchase, it generates a single-use payment token through the Visa network. The agent transmits this token via API to the merchant’s backend systems. The transaction settles exactly like a standard digital wallet payment, bypassing the visual user interface completely.

    A digital storefront requiring multi-page navigation or mandatory account creation introduces failure points for the agent. Enterprises actively deploying headless commerce architectures possess an advantage. They can process the agent’s payload, confirm stock levels, and execute the payment token in milliseconds.

    Enterprises track bounce rates, session durations, and cart abandonment to understand consumer behaviour. An AI agent does not browse—it queries an endpoint, extracts the necessary data, and either executes the payment or terminates the connection.

    Retailers must develop new telemetry to measure agent interactions. Tracking the frequency of API queries from known LLM IP addresses replaces tracking unique human visitors. Understanding why an agent selected a competitor’s product will require analysing the structural differences in product data feeds rather than running A/B tests on website layouts.

    Customer retention strategies also need adjustment. An autonomous agent evaluates the market fresh with every prompt unless explicitly instructed by the user to reorder a specific brand. Loyalty programmes must be engineered into the payment token or the user’s LLM profile. If the AI cannot automatically apply a loyalty discount during its background calculation, the merchant loses the pricing advantage intended to secure the repeat purchase.

    Managing and securing the agentic AI supply chain

    Prompt injection attacks could theoretically manipulate an agent into purchasing from malicious vendors or authorising inflated transactions. Visa’s network acts as the final validation layer, applying fraud detection models to the incoming token requests.

    Businesses face the secondary challenge of managing automated returns and customer service queries initiated by the AI. If the delivered product fails to meet the parameters defined in the original prompt, the user can instruct the agent to reverse the transaction.

    In this scenario, the AI will autonomously navigate the merchant’s return policy, initiate the refund request, and generate the necessary shipping labels. Retail customer service operations must deploy their own automated systems capable of negotiating directly with the consumer’s agent.

    Visa’s ChatGPT integration confirms the enterprise transition from human-operated software interfaces to autonomous digital proxies. The customer is no longer necessarily a human navigating a web browser, but an algorithm executing a script.

    See also: Aviva deploys AI to stop £230M in sophisticated insurance fraud

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  • McDonald’s tests Google-backed AI drive-thru ordering system

    McDonald’s is testing a new AI system that can take drive-thru orders and support restaurant operations.

    The system, called ArchIQ and nicknamed “Archy,” was introduced during the company’s Worldwide convention, according to Restaurant Business. It is being tested at five McDonald’s locations in the United States, though the company has not named the restaurants involved.

    A video shared on X by a McDonald’s franchise owner showed the system greeting customers, processing order changes, displaying the final total, and asking customers to pull ahead for pickup.

    A demonstration shared on X by the franchisee account McFranchisee showed the system taking orders in English and Spanish. The account said the system has processed more than one million transactions, with about 90% of orders completed without being escalated to staff.

    The same account said ArchIQ can respond when repeat customers ask for their usual order. McDonald’s has not provided technical details on how that feature works.

    ArchIQ is being developed with Google. According to McFranchisee, McDonald’s restaurants in the US are receiving Google Edge Cloud blades ahead of the rollout.

    McDonald’s previous AI ordering test

    ArchIQ is McDonald’s latest AI test for drive-thru ordering. The company previously worked with IBM on an automated ordering system across more than 100 restaurants.

    McDonald’s ended that pilot in 2024 after customer complaints over order errors. The earlier IBM test was followed by customer videos showing incorrect orders, including one case in which the system reportedly added more than $250 worth of chicken nuggets.

    After ending the IBM partnership, McDonald’s said it would continue exploring voice ordering technology.

    Restaurant operations support

    ArchIQ is not limited to customer ordering. McFranchisee said it can monitor restaurants and alert managers to possible issues.

    According to McFranchisee, the system can alert managers if a freezer is down. It can also flag kitchen bottlenecks or other problems that need attention.

    McFranchisee described ArchIQ as both an ordering tool and a management-support tool.

    The test forms part of McDonald’s new growth plan, called “McDonald’s > NEXT.” The company said the plan is intended to improve restaurant operations and unit economics.

    McDonald’s reported a large digital customer base in its 2025 results. The company said systemwide sales to loyalty members across 70 markets rose 20% to nearly US$37 billion in 2025, while 90-day active loyalty users rose 19% to nearly 210 million at year-end.

    McDonald’s CEO Chris Kempczinski said in a press release that the strategy is aimed at the company’s next phase of growth and productivity.

    The company has also referenced restaurant upgrades and possible menu changes under the same plan, but has not provided detailed information.

    Automation and service

    In a company memo, Kempczinski said more of the customer journey is becoming automated, leaving fewer chances for guests to interact with crew members. He said that it raises the standard for hospitality when customers interact with staff.

    QSR Magazine’s 2025 Drive-Thru Report, citing Revenue Management Solutions, said drive-thru traffic remained negative month after month and hovered between minus 5% and minus 8% in 2025.

    Other fast-food chains have also announced AI-powered drive-thru ordering systems, including Taco Bell and Wendy’s.

    Jonathan Maze, editor-in-chief of Restaurant Business, told ABC News that companies often present drive-thru automation as a way to free employees for other tasks. The McFranchisee account said the system could reduce the need for workers to take orders in noisy drive-thru lanes.

    Some X users responding to the ArchIQ demonstration said they preferred interacting with human workers. Others supported a more automated ordering process.

    McDonald’s has not said when ArchIQ could be expanded beyond the five test locations. The company has said the system is intended to improve speed and accuracy while supporting customers and crew.

    The company’s AI drive-thru system remains in limited testing.

    (Photo by Boshoku)

    See also: Walmart’s AI workflows meet the realities of the balance sheet

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  • Weis Markets adds Instacart AI-powered shopping carts to stores

    Weis Markets is adding Instacart’s AI-powered shopping carts, Caper Carts, to select stores in Pennsylvania, bringing digital coupons, loyalty features, and repeat-purchase recommendations into the grocery aisle.

    The Pennsylvania-based grocery chain is working with Instacart to deploy the smart carts, which include cameras, certified scales, location systems, and a touchscreen.

    According to Instacart, Caper Carts use basket-facing camera sensors, outward-facing cameras, certified scales, and location-tracking systems to support item recognition and checkout functions. The system combines edge computing on the carts with cloud AI trained on more than 1.6 billion online grocery orders.

    Shoppers can use the cart screen to monitor spending during their trip. They can also access location-based digital coupons directly from the cart.

    Weis customers can sign up for a Weis Rewards account through the cart and redeem loyalty benefits while shopping. Customers who link their accounts can also use a Buy It Again feature, which shows items they have previously purchased.

    Weis and Instacart already work together on online grocery services. In 2023, Weis partnered with Instacart to offer same-day delivery from 133 locations in Pennsylvania, New York, and Delaware.

    Instacart expands Caper Cart rollout

    The Weis rollout adds to Instacart’s wider Caper Cart deployment. The company says the carts now span more than 100 cities across 15 states.

    Caper Carts are available across more than a dozen retail banners, including Kroger, Schnucks, and Wakefern banners such as ShopRite and Fairway Market.

    Earlier deployments have produced some store-level usage data. Retail Dive reported that Schnucks data showed Caper Carts handled more than 10% of sales on busy days at one store. That store had 10 Caper Carts and around 160 traditional carts, according to the report.

    Greg Zeh, senior vice president and chief information officer at Weis Markets, described the carts as part of the company’s effort to improve the shopping process. He pointed to real-time spend tracking and on-cart coupons as key features.

    Instacart described the partnership as an extension of Weis Markets’ use of digital tools inside stores. David McIntosh, Instacart’s chief connected stores officer, said Caper Carts bring together in-store and online data.

    Weis adds AI to checkout operations

    Weis has also been adding AI to self-checkout. Toshiba Global Commerce Solutions said Weis completed a chainwide deployment of its ELERA Security Suite across self-checkout lanes.

    The system includes produce recognition and loss prevention tools. Toshiba says the technology uses edge AI for on-device processing.

    At the time of Toshiba’s December 2025 announcement, the system was operational across self-checkout lanes in all 199 Weis locations. Weis also reported that more than 94% of customers selected the produce recognition feature at self-checkout.

    Grocers test AI beyond checkout

    Albertsons Companies has also introduced an AI-based quality control tool for produce inspection. The system is designed to help identify moldy or damaged fruit before it reaches store shelves.

    The tool initially focuses on strawberries and red and green grapes. Albertsons says it is intended to improve quality rating consistency and support faster decision-making.

    The company also says the tool expands quality data and helps align inspections with company standards.

    Albertsons operates more than 2,000 stores, including Safeway, Jewel-Osco, and ACME. The system supports quality inspectors working in its distribution centres.

    The quality control system uses computer vision to support produce inspections across Albertsons’ store brands. It was developed in-house by the company’s technology and supply chain teams.

    Albertsons built the tool on Google Cloud’s Gemini Enterprise platform, including Vision AI and Gemini models. Google Cloud said it advised on the AI component used in the supply chain process.

    (Photo by Franki Chamaki)

    See also: Amazon brings AI shopping assistant to retailers with Kate Spade

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