Akamai Technologies has announced that it has entered into a definitive agreement to acquire Israeli browser-based AI usage control and secure enterprise browser company LayerX for $205 million

CopilotKit first came to our attention not through a pitch, but through something we pay close attention to as a fund: organic developer adoption. We regularly track emerging open-source projects gaining traction in developer communities, and CopilotKit’s growth curve stood out, developers were pulling it into their stacks, raving about the simple yet powerful new layer for agent-to-user interactions, and the project’s growth pace suggested it was solving a real and urgent problem.

When we met co-founders (and brothers) Atai and Uli Barkai, we were immediately struck by their clarity of vision for agentic-human interfaces in an AI-powered world. But what truly set CopilotKit apart was the speed at which they turned open-source momentum into a commercial business, always the hardest challenge for developer-first companies. Within months, Fortune 500 enterprises were paying meaningful dollars for CopilotKit’s early enterprise offering, and revenue was growing rapidly. It’s one thing to build something developers love. It’s another entirely to make that love monetizable. CopilotKit did both, remarkably fast.

Agents Need a New User-Interface Layer

For the past decade, software interfaces followed a simple model: a frontend talks to a backend, the backend replies with data, the frontend renders the reply. A stateless ping-pong, no persistence, no learning, no adaptation. AI agents break that model entirely. They stream responses, pause for human input, resume across sessions, generate dynamic UI elements on the fly, and orchestrate complex multi-step workflows. They produce continuous, stateful, interactive experiences, and the old frontend stack simply wasn’t built for any of that.

Every company embedding AI agents into its products today is discovering the same thing: the hardest part isn’t building the agent, it’s building the interaction layer between the agent and the human user, while avoiding lock-in to a specific agentic backend framework. How does the agent surface its work? How does the user steer, correct, or approve? How does this all persist across sessions, devices, and workflows? And how does the organization track and analyze user interactions with agents? Before CopilotKit, teams were forced to reinvent this layer from scratch – every time, for every frontend, and with every agentic backend they use, at enormous cost and with limited results.

AG-UI and the CopilotKit Stack

What we loved about CopilotKit was their insight that the agent-to-user interface is not an application problem ,it’s an infrastructure problem. Just as MCP became the universal standard for connecting agents to external tools, CopilotKit is building the equivalent standard for connecting agents to users.

At the core of this is AG-UI, an open protocol created by CopilotKit that defines how agentic backends communicate with modern frontends. AG-UI is the third leg of what is becoming the agentic protocol stack: MCP connects agents to existing software tools, A2A interconnects agents between themselves, and AG-UI connects these agents to human users. Instead of every application building its own bespoke agent-user interface, AG-UI provides a universal language, each backend framework implements the protocol once, and all applications can then work independently of the backend with a rich agentic user experience.

The ecosystem’s response was extraordinary. Google, Microsoft, Amazon, and Oracle have all integrated AG-UI into their own agent frameworks, and officially endorsed CopilotKit for enterprises building agentic products. Leading open-source agent frameworks, LangChain, LlamaIndex, Mastra, PydanticAI, Agno, have aligned to and implemented the AG-UI protocol. In a fragmented, fast-moving ecosystem where standards rarely emerge this early, CopilotKit has become the neutral, trusted layer for agent-user interaction that everyone is building on. That kind of ecosystem convergence is rare, and it doesn’t happen by accident. We were sold.

Why Glilot+? Why Now?

At Glilot, we’ve spent over a decade investing in the infrastructure layers that define how enterprises build and secure software, from cybersecurity and cloud infrastructure to developer tools and DevOps. CopilotKit sits squarely at the intersection of these themes. It’s not an AI application; it’s the infrastructure that enables every AI application to interact with its users. That distinction matters, because infrastructure compounds in ways that applications don’t.

We also made a deliberate bet on timing. The agentic era is arriving faster than most enterprises are prepared for, and CopilotKit is giving them the building blocks to ship agent-powered products without reinventing a critical layer of the new agentic stack. The rare combination of rapid open-source adoption and an enterprise offering that converted into meaningful revenue almost immediately signals genuine product-market fit, not just developer enthusiasm, making this investment a perfect fit for Glilot+.

We’re proud to lead CopilotKit’s $27M Series A alongside our colleagues from NFX and SignalFire, and support the company’s fast growth across the US and globally. Atai, Uli, and the entire CopilotKit team are building what we believe will become a foundational infrastructure layer of the software stack for years to come, and we couldn’t be more excited to be part of this journey.

ScaleOps, an autonomous cloud and AI infrastructure resource management platform, has raised $130 million at a valuation of more than $800 million.

For the past year, the same massive challenge has been looming over the AI revolution: the exponential growth of AI compute is on a collision course with the physical limits of the power grid.

As data centers race to scale, the gap between how AI software behaves and what physical infrastructure can handle is widening rapidly. Historically, the industry has treated power as a static constraint and compute as a separate world entirely. But as this ecosystem grows into a multi-trillion-dollar market, we can no longer afford the disconnect between energy and compute.

That disconnect was hard to ignore as an investor.

When I first encountered Niv-AI, what stood out immediately was what they were not trying to do. They weren’t building just another monitoring dashboard or adding bulky hardware to an already stressed supply chain. Instead, they recognized that to solve a problem this large, the energy layer and the compute layer desperately need a shared dialect to communicate.

That framing mattered. It shifted the conversation away from treating power as a limitation, and toward treating it as an intelligent, software-defined ecosystem. Niv-AI acts as a critical control plane sitting exactly at the intersection of energy and compute. By doing so, they aren’t just solving a point-in-time issue; they are building the foundational infrastructure required for the global AI ecosystem to scale without breaking the physical grid.

True consolidation and category creation rarely come from staying within traditional industry silos. They come from operating directly in the white space between them.

The technology alone would have been compelling, but what ultimately sealed my conviction was the team. Tomer and Eddie bring a rare advantage to this problem. Drawing on their deep operational and technical backgrounds in elite intelligence units, they possess the exact bare-metal and systems-level expertise needed to orchestrate complex challenges at the microsecond level. They were unusually clear about what it takes to solve this from first principles, with no trend-chasing and no noise.

Glilot Capital co-led Niv-AI’s $12M Seed round together with Lior Handelsman from Grove Ventures. Lior, a co-founder of SolarEdge, is a world-class expert who shares our conviction in this vision. We chose to invest because we believe the company brings the physical infrastructure of AI closer to reality – both technically and culturally. The AI revolution can only fulfill its promise if it is grounded in the reality of physics.

Niv-AI represents exactly that kind of breakthrough. We believe these are the moments that create lasting companies and drive meaningful category shifts.