Featured Article : AI Assistants Write Your Prompts And Do Your Shopping For You
Two new launches from Hero and Google show how everyday digital tasks are moving towards full automation, with prompt writing and online shopping now handled largely by AI rather than users.
A Clear Move Towards Automated Digital Tasks
AI tools have become familiar, yet many still require people to know how to phrase prompts or navigate long product pages. Now, Hero, a rising productivity startup, and Google, are both targeting these pain points with new systems designed to remove the need for manual prompting and repetitive shopping tasks altogether. It seems their latest releases aim to streamline everyday digital admin using context, automation and conversational interactions.
Who Is Hero And What Does The App Do?
Hero is a consumer productivity platform built by former engineers who have previously worked on augmented reality interfaces. The company has grown rapidly, reporting more than 300,000 users and a 4.9 rating on the Apple App Store. Its core idea is to replace multiple apps with a single daily assistant. For example, the Hero app brings together calendars, reminders, events, to-dos, notes, habit tracking, shared lists and weather updates in one continuous feed.
Users can create tasks, organise schedules, coordinate with partners or colleagues, and receive “Can’t Miss” notifications that can sound even when a phone is in silent mode. There is also a built-in grocery system that categorises items automatically and connects to Instacart ordering. Hero promotes itself as a tool to “run your life in one place”, aiming to simplify the routines and small decisions that tend to fragment across apps.
Hero’s Autocomplete SDK Now Writes Prompts For You!
It seems that Hero is now extending this philosophy to AI prompts. The company has introduced a new autocomplete SDK (Software Development Kit) that predicts and fills in the parameters an AI system will need to complete a task. This means users can begin with a short instruction, and the SDK will fill in all the other relevant fields and details, allowing the user to complete as much or as little as they like before submitting the request.
For example, starting a prompt with “Book a flight” can automatically produce fields such as departure and destination airports, dates, times and airline choices. The same applies to creative tools, where the SDK can suggest common parameters such as style, location or camera angle for image or video generation.
Uses Multiple Models Together
Hero says that the SDK uses multiple models working together to understand user intent and assemble the information the system needs. The company says the autocomplete experience reduces the number of messages required to complete an action, cutting time and effort for users and reducing computing costs for businesses that run AI-powered services.
Background In AR
It seems this idea most likely comes from the founders’ background in augmented reality, where screen space is limited and long free-form prompts are impractical. Building clear, structured actions from short starting phrases became part of their design thinking, and the new SDK continues that approach by making prompts more like guided workflows.
Funding
Hero recently secured 3 million dollars in additional funding and is already testing the autocomplete technology inside its own app, where users will be able to rely on the assistant to propose structured prompts for tasks such as finding meeting times, organising shared plans or identifying key details from photos and screenshots.
Google Redesigns Online Shopping With Agentic AI
While Hero is automating prompt writing, Google is now automating shopping. For example, the company has just announced a major upgrade to its AI shopping features across Search and the Gemini app, aimed at simplifying product discovery, comparison, stock checking and purchase.
In Google’s own announcement about the features, the company said shopping should “feel a lot more natural and easy”, noting that browsing can be enjoyable but the administrative steps often are not. The new tools are designed to let people describe what they want in everyday language while the AI organises the information needed to make decisions.
For example, through AI Mode in Search, users can now ask conversational questions such as “cosy jumpers in warm autumn colours” and receive a visual selection of products, prices, reviews and inventory information. If they are comparing items such as skincare products, AI Mode can switch to structured comparison views that highlight key differences and insights from reviews.
Google says these features are powered by Google’s Shopping Graph, which contains over 50 billion product listings, with around 2 billion refreshed every hour. This gives Google’s AI near real-time awareness of stock levels and pricing across retailers.
Shopping Inside Gemini And Automated Purchasing
Google is also making the same capabilities available inside the Gemini app. For example, instead of brief suggestions, Gemini can now respond with complete lists of ideas, curated recommendations, comparison tables and links to buy. All of this is driven by Shopping Graph data, and it is designed to help users move from brainstorming to browsing in a single conversational thread.
One of the most significant additions is agentic checkout. With its help, users can track the price of an item they want, set a maximum budget and ask Google to buy it automatically using Google Pay if the price drops within their range. Google says the system will always request confirmation before completing a purchase and will only use payment details the user has already authorised.
Early rollout partners include retailers such as Wayfair, Quince, Chewy and selected Shopify stores.
Google’s AI Can Call Shops For You
Google has also introduced a tool that uses AI to call physical shops directly. For example, when people search for certain items “near me”, they may see an option marked “Let Google Call”. Selecting this enables Google’s AI to call local stores, check availability, ask about pricing and confirm whether any offers are available. The results are summarised in a follow-up message.
This feature is built on Google’s Duplex calling technology. Merchants who receive calls hear a clear disclosure that the caller is an AI acting on behalf of a customer. Google says shops can opt out at any time, and calls are limited to avoid unnecessary disruption.
Benefits
These developments highlight several benefits for consumers, business users and retailers. For example, for individuals, Hero’s autocomplete SDK removes the need to learn how to write prompts, lowering the barrier to using AI tools. Google’s agentic shopping features reduce time spent checking prices, comparing products or phoning shops, which can support faster decision-making during busy periods such as the holiday season.
For businesses, the real appeal is efficiency and cost reduction. Hero’s SDK shortens user interactions, which reduces the number of model calls required, lowering server costs. Google’s automated shopping tools can bring back hesitant buyers, help retailers reach local customers and streamline the customer journey from discovery to purchase.
There are also broader implications for professionals and business users. For example, automated prompts can speed up research tasks, planning, scheduling and customer support workflows. Automated shopping and stock checking can simplify procurement, reduce manual checks and help teams stay within budgets more easily.
Challenges And Criticisms
Despite all the obvious benefits, it should be noted that there are some also important considerations. For example, the influence of automated suggestions raises questions about visibility and fairness. If autocomplete systems prioritise certain parameters or products, users may only see a narrow band of options. This is particularly sensitive where sponsored listings appear alongside AI-generated recommendations.
Also, privacy is a central concern. Hero brings together large volumes of personal information, including calendars, notes, reminders, grocery lists and shared tasks. Google’s agentic shopping tools collect signals about purchase timing, price sensitivity and product intent. Both companies provide assurances about data handling, yet users may still question how much insight these systems can gain over daily routines and buying habits.
There are also challenges for smaller businesses. Retailers that do not integrate with larger shopping ecosystems may become less visible inside AI-driven recommendations, placing pressure on them to engage with platforms they might otherwise avoid.
It’s also worth noting that this shift from advisory to agentic AI means systems are not only suggesting options but taking actions on behalf of users. This means that the level of comfort people feel with automated purchasing, prompt completion and real-world calling is likely to shape how widely these features are adopted and how deeply automated digital life becomes in the years ahead.
What Does This Mean For Your Business?
The combined direction of these developments suggests that everyday digital tasks are becoming less about active decision making and more about approving actions that AI systems have already prepared. Hero’s approach shows how this can simplify workflows that would normally require careful prompt writing, while Google’s agentic shopping tools reveal how much of the purchase journey can be handled without the user having to search, compare or chase information themselves. The result is a growing expectation that these systems will assemble the context, gather the details and present the decisions in a form that requires minimal input.
This transformation has particular relevance for UK businesses. For example, teams that once spent time on procurement checks, research tasks or repetitive customer queries may find that agentic systems remove much of the manual effort, freeing staff to focus on higher value work. The same applies to smaller organisations that struggle with capacity peaks during busy seasons. Automated comparison, stock checking and structured prompting could help these companies stay responsive even with limited resources, although they will need to weigh this against concerns about visibility and reliance on third party platforms.
There is also a wider shift for retailers, service providers and other stakeholders who will now find themselves interacting not only with customers but with AI agents acting on their behalf. Features such as automated shop calls or price triggered purchases may change how demand appears, how stock is managed and how customer expectations evolve. This presents opportunities to reach customers more consistently, though it also places new pressure on businesses to ensure their information remains accurate across the systems that feed these AI tools.
It’s likely, therefore, that the next stage of adoption is really going to depend on trust. For example, users will need confidence that the suggestions offered are balanced, that privacy safeguards work as intended and that automated actions remain transparent. Businesses will want reassurance that they are not disadvantaged if they choose not to integrate with large ecosystems. What is clear from both launches is that AI is moving steadily from a tool that responds to instructions to one that anticipates what users want and prepares the steps in advance. How people and organisations respond to this will determine how quickly these ideas actually become part of everyday life or not.
