Many companies today provide AI simply as a chatbot inside their apps: You type in (or dictate) what you want it to do, and the AI bot goes and tries to do it. Still, the experience tends to feel clunky. A text-based UI doesn’t always translate to a smooth experience; for example, if you want to use a travel app to book an entire itinerary but have to scan through reams of text.

According to the founders of CopilotKit, that approach doesn’t make the most of what AI agents and LLMs can do. The company’s co-founders, Atai Barkai (pictured above, right) and Uli Barkai (pictured above, left), believe the way forward is to enable agents to live inside applications, understand what users are doing, take actions, and show useful interfaces instead of just returning long blocks of text.

The company’s popular AG-UI protocol is aimed at the first part of that solution. The widely adopted, open source protocol standardizes how AI agents connect to and communicate with user interfaces (like a web browser or an app), providing features such as streaming chat, front-end tool calls, and state sharing to enable human-in-the-loop functionality. Essentially, AG-UI gives devs the framework and tools needed to deploy AI agents within their apps.

CopilotKit is also building an enterprise toolkit on top of AG-UI, adding support, self-hosted deployment features, and other must-have offerings for businesses thinking of building agents into their product. To bring that toolkit to market, the Seattle-based startup has raised $27 million in a Series A round led by Glilot Capital, NFX, and SignalFire, TechCrunch has exclusively learned.

The flexible user interface is a particular selling point. CEO Atai Barkai told TechCrunch developers can use the startup’s framework to provide the specifications and building blocks for dynamic user interfaces, which an AI agent can then use to generate UIs to fit the context.

“The agent can reply to you, not just with blocks of text, but with interactive UIs that are defined by your own company,” Atai explained. “If, for example, a user asks for breakdown of revenue by category, instead of getting this kind of big, impenetrable paragraph, you get a pie chart, and it’s your own design of the pie chart that the user can interact with […] So all of your agents can, very trivially, speak to a UI and use these catalog of components and show that to users.”

Atai also noted that CopilotKit’s toolkit gives developers full control over how much their AI agent can change the UI, to the point where they can choose to have the interface be “pixel-perfect” or just provide broad building blocks that the AI can put together as required.

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The funding follows a period of strong adoption both for AG-UI and CopilotKit. The protocol, which works alongside the widely adopted Model Context Protocol (MCP) and Agent2Agent (A2A) protocol, is today supported by major AI infrastructure providers like Google, Microsoft, Amazon, and Oracle, as well as popular frameworks like LangChain, Mastra, PydanticAI, and Agno.

Atai said CopilotKit and AG-UI (the company’s strongest claim to ecosystem relevance) see millions of installs per week, and that a large portion of Fortune 500 companies are using the protocol and the startup’s tools in production. Meanwhile, CopilotKit counts enterprise bigwigs like Deutsche Telekom, Docusign, Cisco, and S&P Global as enterprise customers.

To tap that growing interest, the company is also launching CopilotKit Enterprise Intelligence, a self-hostable offering that bundles a number of infrastructure features to fully deploy agents within apps.

CopilotKit faces heated competition in the market for enterprise agents tools. Cloud platform Vercel’s open source AI SDK helps developers build AI applications with similar capabilities, and assistant-ui offers components for building AI chat interfaces. Meanwhile, OpenAI’s Apps SDK is also an option for building richer interfaces, though only inside ChatGPT.

Atai argues that CopilotKit is different from those offerings because it takes a horizontal, enterp

CopilotKit raises $27M to help devs deploy app-native AI agents