Trace raises $3 million to solve enterprise AI agent adoption

For all their potential, AI agents have been slow to make an impact in the enterprise, and one new startup is betting that the reason they haven’t is a lack of context.

Launched as part of Y Combinator’s Summer 2025 cohort, Tracks is a workflow orchestration startup that aims to fill this gap. The company maps complex corporate environments and operations so agents have the context they need to scale quickly.

“OpenAI and Anthropic are building these amazing interns that can be leveraged within the company,” says Tim Cherkasov, CEO of Trace, referring to its AI lab tools. “We are building a manager who knows where to put them.”

The London-based company said Thursday it has raised $3 million in seed funding from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital and WeFunder. Angel investors Benjamin Bryant and Kevin Moore also invested.

Trace starts by building a knowledge graph from a company’s existing tools — systems like email, Slack, and Airtable that make up the company’s daily working life. With that context in place, users can ask the system for a high-level task — such as “We need to design a new microsite” or “Let’s develop our 2027 sales plan” — and Trace will return a step-by-step workflow, delegating some tasks to AI agents and assigning others to human workers. When the system calls the AI ​​agent, it will prompt it for the specific data needed to complete its subtask.

The idea is to automate the precise work done by AI agents, one of the biggest barriers to actual deployment within companies.

With so many companies focusing on agentic AI, Trace will have a lot of competition. Earlier this weekAnthropic launched its own approach to enterprise agents, focusing on pre-built plugins for specific administrative functions. And many workplace productivity services that will be utilized, such as Jira atlanticthey launch their own agents, which will likely compete with the startup’s system.

But Trace’s founders believe their knowledge graph approach will be key to success, as they can build context engineering deep into the agent deployment architecture.

“2024 and 2025 were still about agile engineering. Now we’ve moved from agile engineering to context engineering,” says CTO Arthur Romanoff. “Whoever provides the best context at the right time will serve as the infrastructure on which the first AI companies will be built. We hope to be that infrastructure.”