Fetch AI, the startup founded and led by former DeepMind founding investor Humayun Shaikh, today announced the release of three interconnected products designed to provide the trust, coordination, and interoperability needed for a large-scale AI agent ecosystem.
The launch includes ASI:One, a personal-AI orchestration platform; Fetch Business, a verification and discovery portal for brand agents; and Agentverse, an open directory hosting over two million agents.
Together, the system positions Fetch as an infrastructure provider for the “agentic web” – a layer where consumer AI and brand AI collaborate to complete tasks rather than simply make suggestions.
The company says the tools address a central limitation in current consumer AI: models can provide recommendations but cannot reliably execute multi-step tasks that require coordination across businesses. Fetch’s approach focuses on enabling agents from different organizations to securely interoperate using verified identities and shared context to complete end-to-end workflows.
In a press release provided to VentureBeat, Humayun Shaikh, founder and CEO of Fetch AI and an early investor in DeepMind, said, “We’re building the same foundation for agents that Google built for websites.” “Instead of just finding information, your personal AI coordinates with verified brand agents to get the job done.”
Background: Setting up Fetch and DeepMind connection
Fetch AI was founded in 2017 by Humayun Shaikh, an entrepreneur whose early investment in DeepMind helped support the company’s commercial growth before its acquisition by Google. “I was one of the first five people at DeepMind and its first investor. My check came first,” Sheikh said, reflecting on a period when advanced machine learning research was still largely inaccessible outside of major technology companies.
His early experiences helped shape Fetch’s direction. “Even in 2013, it was clear to me that agentic systems were what would work. That’s where I focused on the agentic web,” Sheikh said. Fetch built on this thesis by developing an infrastructure for autonomous software agents focusing on verifiable identity, secure data exchange, and multi-agent coordination.
Over the past several years, the company has expanded to a team of 70 people in Cambridge and Menlo Park, raised nearly $60 million, and collected more than one million users interacting with its models – data that has informed the design of newly launched products.
Sheikh said that his decision to bootstrap the company initially came directly from the proceeds of the DeepMind exit, adding in the interview that while the sale to Google was “a good exit”, he believed the team could have held out for a higher valuation.
The initial self-funding period allowed Fetch to begin work in 2015 – long before Transformer architectures became mainstream – on the hypothesis that agentic infrastructure would become foundational for applied AI.
ASI:ONE – A platform for multi-agent orchestration
is at the core of the launch ASI: oneA language model interface specifically designed for coordinating multiple agents rather than addressing isolated queries. Fetch describes it as an “intelligence layer” that handles context sharing, task routing, and preference modeling.
The system stores user-level signals such as preferred airlines, dietary constraints, budget limits, loyalty program identifiers, and calendar availability. When a user requests a complex task – such as planning a trip with flight, hotel and restaurant reservations – ASI:One retrieves those preferences and assigns the task to the appropriate verified agents. Agents then return actionable outputs, including inventory and booking options, rather than general recommendations.
In practice, ASI:One acts as a workflow generator across organizational boundaries. Unlike traditional LLM applications, which often rely on APIs or RAG technologies to expose information, ASI:One is built to coordinate autonomous agents that can complete transactions. Fetch notes that personalization improves over time as the model accumulates structured preference data.
Sheikh emphasized the difference between systematic execution and traditional AI output. “It’s not exploring different options and hoping they’ll work together,” he said. “It’s orchestration.”
He said Fetch’s architecture is intentionally modular: “Our architecture is a mix of agentic and expert models. A big model is not enough – you need experts. That’s why we built ASI1, which is specifically tuned for agentic systems.”
The interview also revealed new details about ASI:One’s personalization system: The platform uses multiple user-owned knowledge graphs to store preferences, travel history, social connections, and contextual constraints.
These knowledge graphs are per-user siled and are not mixed with any fetch-driven data. Sheikh described it as a “deterministic backbone” that provides individual AI with a stable memory layer beyond the probabilistic outputs of a larger model.
ASI:One launches in beta today, with a wider release planned for early 2026. Fetch also offers ASI:One Mobile, which was released earlier this year, giving users access to the same agent-orchestration capabilities on iOS and Android. The mobile app connects directly to the agentverse and the user’s knowledge graph, enabling on-the-go task execution and real-time interactions with registered agents.
Fetch Business – Verified Identity and Brand Control
To enable trusted coordination between consumers and companies, Fetch is introducing a verification and discovery portal called Fetch Business.
The platform allows organizations to verify their identity and claim an official brand agent handle – for example, @Hilton or @Nike – regardless of what device they use to create the underlying agent.
Fetch positions the product as an analog to the ICANN domain registration and SSL certificate system for websites. Verified status is intended to protect consumers from interacting with fake or untrustworthy agents, with the company describing this problem as a major barrier to the widespread adoption of agents.
The system includes low-code tools for small businesses to create an agent in a few steps and connect real-time APIs like inventory, booking systems or CRM platforms.
“With Fetch, you can create an agent in a minute. It gets a handle, like a Twitter username, and you can completely personalize it — even give it permission to post to your social media on your behalf,” Sheikh said. Once a brand claims its namespace, its agent becomes discoverable to consumer AI and other agents inside the Agentverse.
The company has pre-reserved thousands of brand namespaces in anticipation of demand. Verification status persists across any platform that integrates with Agentverse, creating a portable identity layer for business agents.
The interview highlighted that Fetch Business directly inherits web-trust primitives: domain owners verify their identity by inserting a small code snippet into their existing website backend, allowing the system to pass a cryptographic challenge and provide an authenticity badge similar to a “blue check” to the agent’s identity. Sheikh defined this as “reusing the layer of trust that the Web has already taken decades to build.”
Companies can now start claiming agents business.fetch.ai,
Agentverse – an open directory of over two million agents
The final component of the release is Agentverse, an open directory and cloud platform that hosts agents and enables cross-ecosystem discovery capability. Fetch says millions of agents have already registered, spanning travel, retail, entertainment, food service and enterprise categories.
The AgentVerse provides metadata, capability descriptions, and routing logic that ASI:One uses to identify appropriate agents for specific tasks. It also supports secure communication and data exchange between agents. The company notes that the directory is platform-agnostic: agents built with any framework can join and interoperate.
According to Sheikh, the lack of a search layer is one reason why most AI agents see little or no use. “Ninety percent of AI agents are never used because there is no discovery layer,” he said.
He defined the role of AgentVerse in more technical terms: “Right now, if you create an agent, there’s no universal way for others to find it. That’s what AgentVerse solves – it’s like DNS for agents.” He also described the system as an essential component of the emerging agent economy: “Fetch is building the Google of agents. Just as websites need discovery, agents need discovery, trust, and interaction – Fetch provides all that.”
The interview further underlined that Agentverse is cloud-agnostic by design. Shaikh compared this with competing agent ecosystems tied to specific cloud providers, arguing that a universal registry is only viable if independent of proprietary cloud environments. He said the open architecture enables LLM to query any agent “within a minute of deployment”, turning agent publishing into a nearly instantaneous process, similar to registering a domain.
Agentverse also integrates payment routes, enabling agents to execute purchases using partners like Visa, Skyfire, and supported stablecoins. Consumers can set spending limits or require explicit approval for transactions.
Industry context and implications
The launch of Fetch comes at a time when consumer AI platforms are exploring the shift from static chat interfaces to autonomous agents capable of completing tasks. However, most agent systems remain limited by siled architectures, limited interoperability, and weak validation standards.
Fetch deploys its infrastructure as a response to these limitations by providing a cross-platform coordination layer, identity system, and directory service. The company argues that an agent ecosystem requires consistent verification mechanisms to ensure that consumers interact with authentic brand representatives rather than fake ones. By establishing namespace control and portable trust indicators, Fetch Business aims to fill the same gap as initial web domain validation.
Additionally, ASI:One strives to centralize user preference data in a way that enables more efficient personalization and multi-agent coordination. This approach differs from generalist LLM applications, which often lack consistent preference architectures or direct access to brand-controlled agents.
The interview also made it clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term vision. Sheikh referenced Coinbase’s integration with protocols like 402 and AP2, describing these capabilities as essential for autonomous agents to complete end-to-end tasks, including financial execution.
take away
The joint release of Fetch’s ASI:One, Fetch Business, and Agentverse introduces an interconnected stack designed to support the large-scale deployment and use of AI agents. The company envisions this system as the foundational infrastructure for an agentic ecosystem, where consumers can coordinate with verified brand agents to complete AI tasks reliably and securely. The additions to its detection, discovery, and orchestration layers reflect Fetch’s long-standing thesis – partly rooted in the lessons of DeepMind’s early development – that intelligence is only meaningful when paired with the ability to act.