London-based Trace raised $3 million in seed funding to tackle what CEO Tim Cherkasov calls the enterprise AI agent adoption crisis, where brilliant AI capabilities meet corporate complexity and consistently fail to scale.

The fundamental bottleneck isn’t agent capability—it’s context. While OpenAI and Anthropic have built “brilliant interns,” most enterprises struggle to provide these agents with the organizational knowledge they need to operate effectively beyond proof-of-concept demonstrations.

The Enterprise Context Gap

Enterprise AI agent deployment faces a systematic failure rate exceeding 95%, according to industry estimates. The core issue isn’t technical sophistication but rather the delicate work of on-boarding agents into complex corporate environments where critical context spans email, Slack, Airtable, and dozens of other interconnected systems.

Union.ai closed a $38.1 million Series A round led by NEA, with participation from Nava Ventures and new investor Mozilla Ventures, targeting the pilot-to-production infrastructure gap that has become a critical bottleneck for enterprise AI deployment.

This funding addresses a fundamental architectural challenge: legacy software infrastructure designed for deterministic processes struggles with AI workflows that adapt and make runtime decisions. Union’s orchestration platform, built around the open-source Flyte framework with 80+ million downloads, enables dynamic AI workflows that can recover from failures and make decisions at runtime—exactly what autonomous agents require for production deployment.

Nimble raised $47 million in Series B funding led by Norwest to solve a critical bottleneck in enterprise AI agent deployment: reliable access to real-time, structured web data that agents can actually trust for business decisions.

The New York-based company addresses what CEO Uri Knorovich calls the core enterprise AI failure mode: “Most production AI fails aren’t because the models are not good enough — it’s because of a data failure.” While AI agents excel at web search and analysis, they typically return unstructured text prone to hallucinations and unreliable sourcing, creating an insurmountable trust gap for enterprise deployment.