Startups & Funding
Trace raises $3M to solve enterprise AI agent adoption
London-based startup Trace raised $3 million in seed funding to build a knowledge graph that provides the necessary context for enterprise AI agents to function effectively.
On Thursday, Trace, a London-based workflow orchestration startup—which automates and manages complex business processes—announced it has raised $3 million in seed funding. The capital is intended to address the friction of deploying artificial intelligence agents in corporate environments, which have been slow to make an impact in the enterprise due to a lack of context.
The seed round included participation from:
- Y Combinator
- Zeno Ventures
- Transpose Platform Management
- Goodwater Capital
- Formosa Capital
- WeFunder
- Angel investors Benjamin Bryant and Kevin Moore
The startup, which launched as part of Y Combinator’s 2025 summer cohort, maps complex corporate environments to provide context for AI agents. It does this by building a knowledge graph—a system that maps data and relationships between different tools—using a company’s existing software like email, Slack, and Airtable that shape daily operations.
With this context established, users can prompt the system with high-level tasks, such as designing a new microsite or developing a 2027 sales plan. Trace then generates a step-by-step workflow, delegating some tasks to AI agents and assigning others to human workers. When the system invokes an AI agent, it prompts it with the specific data needed to complete its sub-task, aiming to automate away the delicate work of onboarding AI agents.
This approach shifts the enterprise focus from prompt engineering to what the founders call “context engineering.” Trace CEO Tim Cherkasov explained the division of labor: “OpenAI and Anthropic are building these brilliant interns that can be leveraged within the company. We’re building the manager that knows where to put them.”
CTO Artur Romanov noted that while 2024 and 2025 were still about prompt engineering, the industry has now moved to context engineering. Romanov stated that whoever provides the best context at the right time will become the infrastructure on top of which AI-first companies are built.
Trace enters a highly competitive market for agentic AI, where it faces pressure from both major AI labs and established enterprise software providers. Earlier this week, Anthropic launched its own take on enterprise agents, which focus on pre-built plug-ins for specific departmental functions. Meanwhile, workplace productivity services that Trace draws from, such as Atlassian’s Jira, are launching their own agents, which will potentially compete with the startup’s system.
Why it matters
While enterprises have been slow to adopt AI agents due to a lack of operational context, Trace attempts to solve this integration bottleneck by mapping existing corporate tools. If successful, its context-engineering layer could serve as the underlying infrastructure that allows companies to deploy autonomous agents safely and effectively at scale.