CopilotKit Introduces Enterprise Intelligence Platform That Gives Agentic Applications Persistent Memory Across Sessions and Devices

Seattle-based CopilotKit has closed a $27 million Series A led by Glilot Capital, NFX, and SignalFire, backing its mission to move AI beyond chatbots and into the fabric of applications themselves. Co-founders Atai Barkai and Uli Barkai argue that text-based AI interfaces fall short, and that agents should instead generate dynamic, interactive UIs tailored to […]

The Seattle-based startup's Series A round was led by Glilot Capital, NFX and SignalFire, TechCrunch has exclusively learned.

This week in New York, my Oracle team ran workshops for enterprise developers on building retrieval-augmented generation and agentic applications. Interest was so strong that we quickly had to figure out how to double the room’s capacity (much to the fire marshal’s chagrin). Interest in AI was clearly off the charts. But AI fluency was not. It was a different vibe (and audience) from what we’ve seen in a course we built with DeepLearning.ai, which attracts a more advanced audience ready to build memory-aware agents. I recently argued that enterprise AI is arriving unevenly across companies and even across teams within the same company. But after watching developers plow through these different workshops, I believe this uneven adoption points to something even more telling: uneven engineering capability. Put differently, the real divide in enterprise AI isn’t just between companies moving fast and companies moving slow. It’s between teams treating AI as a prompt-driven demo and teams le