Fetch AI, a startup based and led by former DeepMind founding investor, Humayun Sheikh, on Wednesday introduced the discharge of three interconnected merchandise designed to supply the belief, coordination, and interoperability wanted for large-scale AI agent ecosystems.
The launch contains ASI:One, a personal-AI orchestration platform; Fetch Enterprise, a verification and discovery portal for model brokers; and Agentverse, an open listing internet hosting greater than two million brokers.
Collectively, the system positions Fetch as an infrastructure supplier for what it calls the “Agentic Internet”—a layer the place shopper AIs and model AIs collaborate to finish duties as a substitute of merely suggesting them.
The corporate says the instruments handle a central limitation in present shopper AI: fashions can present suggestions however can’t reliably execute multi-step actions that require coordination throughout companies. Fetch’s method facilities on enabling brokers from completely different organizations to interoperate securely, utilizing verified identities and shared context to finish end-to-end workflows.
“We’re creating the identical basis for brokers that Google created for web sites,” mentioned Humayun Sheikh, Founder and CEO of Fetch AI, and an early investor in DeepMind, in a press launch supplied to VentureBeat. “As a substitute of simply discovering data, your private AI coordinates with verified model brokers to get issues completed.”
Fetch’s founding and DeepMind connection
Fetch AI was based in 2017 by Humayun Sheikh, an entrepreneur whose early funding in DeepMind helped help the corporate’s business improvement earlier than its acquisition by Google. “I used to be one of many first 5 folks at DeepMind and its first investor. My test was the primary one in,” Sheikh mentioned, reflecting on the interval when superior machine studying analysis was nonetheless largely inaccessible outdoors main expertise corporations.
His early expertise helped form Fetch’s course. “Even in 2013, it was clear to me that agentic programs had been going to be those that labored. That’s the place I targeted—on the agentic internet,” Sheikh famous. Fetch constructed on this thesis by growing infrastructure for autonomous software program brokers, specializing in verifiable identification, safe information alternate, and multi-agent coordination.
Over the previous a number of years, the corporate has expanded to a 70-person group throughout Cambridge and Menlo Park, raised roughly $60 million, and collected multiple million customers interacting with its mannequin—information that knowledgeable the design of the newly launched merchandise.
Sheikh added that his determination to bootstrap the corporate initially got here straight from the proceeds of the DeepMind exit, noting within the interview that whereas the sale to Google was “an excellent exit,” he believed the group might have held out for a better valuation.
The early self-funding interval allowed Fetch to start work in 2015—effectively earlier than transformer architectures went mainstream—on the speculation that agentic infrastructure would change into foundational to utilized AI.
ASI:One is a platform for multi-agent orchestration
On the core of the launch is ASI:One, a language mannequin interface designed particularly for coordinating a number of brokers fairly than addressing remoted queries. Fetch describes it as an “intelligence layer” that handles context sharing, job routing, and choice modeling.
The system shops user-level indicators akin to favored airways, dietary constraints, funds ranges, loyalty program identifiers, and calendar availability. When a person requests a fancy job — akin to planning a visit with flights, motels, and restaurant reservations — ASI:One retrieves these preferences and delegates work to the suitable verified brokers. The brokers then return actionable outputs, together with stock and reserving choices, fairly than generic suggestions.
In follow, ASI:One features as a workflow generator throughout organizational boundaries. Against this with typical LLM purposes, which frequently depend on APIs or RAG strategies to floor data, ASI:One is constructed to coordinate autonomous brokers that may full transactions. Fetch notes that personalization improves over time because the mannequin accumulates structured choice information.
Sheikh emphasised the excellence between orchestrated execution and conventional AI output. “This isn’t looking for choices individually and hoping they work collectively,” he mentioned. “It’s orchestration.”
He added that Fetch’s structure is deliberately modular: “Our structure is a mixture of agentic and professional fashions. One massive mannequin isn’t sufficient — you want specialists. That’s why we constructed ASI1, tuned particularly for agentic programs.”
The interview additionally revealed new particulars about ASI:One’s personalization programs: the platform makes use of a number of user-owned information graphs to retailer preferences, journey historical past, social connections, and contextual constraints.
These information graphs are siloed per person and never co-mingled with any Fetch-operated information. Sheikh described this as a “deterministic spine” that offers the private AI a secure reminiscence layer past the probabilistic output of a single massive mannequin.
ASI:One launches in Beta right this moment, with a broader launch deliberate for early 2026. Fetch additionally provides ASI:One Cellular, launched earlier this yr, giving customers entry to the identical agent-orchestration capabilities on iOS and Android. The cell app connects on to Agentverse and the person’s information graphs, enabling on-the-go job execution and real-time interplay with registered brokers.
Fetch Enterprise provides verified identification and model management
To allow dependable coordination between customers and firms, Fetch is introducing a verification and discovery portal known as Fetch Enterprise.
The platform permits organizations to confirm their identification and declare an official Model Agent deal with — for instance, @Hilton or @Nike — no matter which instruments they use to construct the underlying agent.
Fetch positions the product as an analogue to ICANN area registration and SSL certificates programs for web sites. Verified standing is meant to guard customers from interacting with counterfeit or untrusted brokers, an issue the corporate describes as a significant barrier to widespread agent adoption.
The system contains low-code instruments for small companies to create brokers in just a few steps and join real-time APIs akin to stock, reserving programs, or CRM platforms.
“With Fetch, you may create an agent in a single minute. It will get a deal with, like a Twitter username, and you’ll personalize it utterly—even give it your social media permissions to publish in your behalf,” Sheikh mentioned. As soon as a model claims its namespace, its agent turns into discoverable to shopper AIs and different brokers inside Agentverse.
The corporate has pre-reserved hundreds of brand name namespaces in anticipation of demand. Verification standing persists throughout any platform that integrates with Agentverse, creating a conveyable identification layer for enterprise brokers.
The interview highlighted that Fetch Enterprise inherits web-trust primitives straight: area homeowners confirm their identification by inserting a brief code snippet into their present web site backend, permitting the system to go a cryptographic problem and grant the agent an authenticity badge much like a “blue test” for agent identities. Sheikh framed this as “reusing the belief layer the net already spent a long time constructing.”
Firms can start claiming brokers now at enterprise.fetch.ai.
Agentverse is an open listing of extra yhan 2 million brokers
The ultimate element of the discharge is Agentverse, an open listing and cloud platform that hosts brokers and permits cross-ecosystem discoverability. Fetch states that thousands and thousands of brokers have already registered, spanning journey, retail, leisure, meals service, and enterprise classes.
Agentverse supplies metadata, functionality descriptions, and routing logic that ASI:One makes use of to establish applicable brokers for particular duties. It additionally helps safe communication and information alternate between brokers. The corporate notes that the listing is platform-agnostic: brokers constructed with any framework can be part of and interoperate.
Based on Sheikh, the shortage of a discovery layer is one cause most AI brokers see little or no utilization. “Ninety p.c of AI brokers by no means get used as a result of there’s no discovery layer,” he mentioned.
He framed the position of Agentverse in additional technical phrases: “Proper now, should you construct an agent, there’s no common method for others to find it. That’s what AgentVerse solves—it’s like DNS for brokers.” He additionally described the system as a vital part of the rising agent financial system: “Fetch is constructing the Google of brokers. Identical to web sites wanted search, brokers want discovery, belief, and interplay—Fetch supplies all of that.”
The interview additional underscored that Agentverse is cloud-agnostic by design. Sheikh contrasted this with competing agent ecosystems tied to particular cloud suppliers, arguing {that a} common registry is just viable if impartial of proprietary cloud environments. He mentioned the open structure permits an LLM to question any agent “inside one minute of deployment,” turning agent publication right into a near-instantaneous course of much like registering a site.
Agentverse additionally integrates cost pathways, enabling brokers to execute purchases utilizing companions akin to Visa, Skyfire, and supported stablecoins. Customers can configure spending limits or require express approval for transactions.
Business context and implications
Fetch’s launch comes at a time when shopper AI platforms are exploring the shift from static chat interfaces towards autonomous brokers able to finishing actions. Nevertheless, most agent programs stay restricted by siloed architectures, restricted interoperability, and weak verification requirements.
Fetch positions its infrastructure as a response to those limitations by offering a cross-platform coordination layer, identification system, and listing service. The corporate argues that an agent ecosystem requires constant verification mechanisms to make sure that customers work together with genuine model representatives fairly than imitations. By establishing namespace management and moveable belief indicators, Fetch Enterprise goals to fill a niche much like early internet area verification.
On the similar time, ASI:One makes an attempt to centralize person choice information in a method that permits extra environment friendly personalization and multi-agent coordination. This method differs from generalist LLM purposes, which frequently lack persistent choice architectures or direct entry to brand-controlled brokers.
The interview additionally made clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term imaginative and prescient. Sheikh referenced integrations with protocols akin to Coinbase’s 402 and AP2, positioning these capabilities as important for autonomous brokers to finish end-to-end duties that embody monetary execution.
Fetch’s mixed launch of ASI:One, Fetch Enterprise, and Agentverse introduces an interconnected stack designed to help large-scale deployment and utilization of AI brokers. The corporate frames the system as foundational infrastructure for an agentic ecosystem, the place shopper AIs can coordinate with verified model brokers to finish duties reliably and securely. The additions to its identification, discovery, and orchestration layers mirror Fetch’s long-standing thesis — rooted partly in classes from DeepMind’s early improvement — that intelligence turns into significant solely when paired with the capability to behave.
