AI agent platforms raise tens of millions as enterprise adoption accelerates

Two AI agent companies have completed substantial funding rounds in quick succession: Owner raised $240 million in a Series D, while Multiplier closed a $6 million seed round. At the same time, ServiceNow has announced that the number of enterprise customers actively deploying agentic AI in production has grown ninefold over the past nine months. Taken together, these developments paint a picture of a segment shifting from experimentation to concrete deployment.

Whereas AI agents were long tested primarily in proof-of-concept settings, both the investment amounts and usage figures now show that companies and investors are placing larger bets on systems that autonomously execute tasks and orchestrate multiple models or tools.

Owner targets local businesses, Multiplier targets wealth management

Owner is building an AI agent platform specifically for local business owners, think restaurants, hair salons, and other physical service providers. The company received $240 million in a Series D round. No information about the valuation or the names of the investors has been disclosed based on available source material.

The size of the round is notable for a platform targeting a relatively defined audience. Vertical AI applications, in which an agent is deeply configured for the workflows of a single sector or customer type, appear to inspire more confidence among investors than broad, horizontal solutions. The reasoning is that an agent that precisely understands how a hospitality business handles reservations, orders, and customer enquiries will deliver real value faster than a generic alternative.

Multiplier operates at the opposite end of the target audience spectrum. The company develops tools for investment professionals and asset managers and closed a $6 million seed round. The funding is intended to further develop the platform for asset management firms. Here, too, the sector-specific approach applies: financial professionals have their own workflows, data systems, and compliance requirements for which generic assistants are less well suited.

ServiceNow sees production deployments multiply ninefold in under a year

ServiceNow, which provides enterprise software to large organisations worldwide, reports a ninefold increase in the number of customers actually running agentic AI in production. That growth occurred over a period of nine months. The company indicates that adoption is also having a measurable commercial impact: the agentic AI portfolio is contributing to revenue in an increasing measure.

In concrete terms, this involves automated workflows in which AI agents independently make decisions, route or complete tasks, and communicate with other systems, without a human employee approving every step. Use cases include IT service management, HR processes, and customer service.

The growth figures come from ServiceNow itself and have not been independently verified. Nevertheless, they are illustrative of a broader trend: enterprise software vendors are embedding agentic AI ever more deeply into existing platforms, meaning adoption increasingly requires an extension of familiar environments rather than a separate implementation.

Meta and Cloudflare add models and infrastructure

On the model side, Meta introduced Llama Spark 1.1, a multimodal reasoning model designed for agentic tasks. According to Meta, the model is better able to coordinate multiple agents and execute instructions across longer chains of steps. Llama Spark 1.1 builds on Meta's open-model strategy and is intended as a foundation on which developers can build their own agent systems. Further technical specifications or benchmark results were not available based on the source material.

Cloudflare, meanwhile, is attracting more enterprise customers to its Workers developer platform, specifically because of its capabilities for AI agents. Workers offers a distributed runtime that allows developers to run agent logic close to the end user, with low latency and without managing centralised server infrastructure. For companies looking to run agent applications at scale, the underlying infrastructure layer is a relevant consideration.

What this means for those building with or investing in AI

The combination of large funding rounds, rising production figures, and new model releases shows that the ecosystem around AI orchestration, directing multiple models or agents within a single workflow, is maturing rapidly. Several directions are emerging that are relevant for builders and investors.

• Verticalisation is gaining ground. Owner and Multiplier are both deeply configured for a single sector. Investors appear willing to commit larger sums to platforms with thorough knowledge of a specific domain, rather than funding broad assistants.
• Existing enterprise software is becoming the access channel. Through platforms such as ServiceNow, AI agents are reaching large organisations without those organisations having to introduce an entirely new software layer. This lowers the barrier and accelerates adoption.
• Infrastructure and models as prerequisites. Without reliable orchestration infrastructure (such as Cloudflare Workers) and suitable foundation models (such as Llama Spark 1.1), agent software remains vulnerable to latency and reliability issues. Investments at those layers are just as consequential as the application layer itself.

Whether the current growth figures will be sustained depends in part on how quickly organisations build trust in autonomous decisions made by AI systems, a challenge that is both technical and organisational in nature.