Trace raises $3M to solve the AI agent adoption problem in enterprise

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 2025 summer cohort, Trace is a workflow orchestration startup aimed at filling that gap. The company maps complex corporate environments and processes so that agents have the context they need to scale quickly.

“OpenAI and Anthropic are building these brilliant interns that can be leveraged within the company,” says Trace CEO Tim Cherkasov, referring to the AI labs’ tools. “We’re building the manager that knows where to put them.”

On Thursday, the London-based company said it had 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’s system starts by building a knowledge graph from a company’s existing tools — systems like email, Slack, and Airtable that shape the day-to-day working life of the firm. With that context in place, users can prompt the system with a high-level task — like “We need to design a new microsite” or “Lets develop our 2027 sales plan” — and Trace will come back with a step-by-step workflow, delegating some tasks to AI agents and assigning others to human workers. When the system does invoke an AI agent, it will prompt it with the specific data needed to complete its sub-task.

The idea is to automate away the delicate work of on-boarding AI agents, one of the biggest blockers for actual deployment within companies.

With so many companies focused on agentic AI, Trace will have plenty of competition. Earlier this week, Anthropic launched its own take on enterprise agents, focused on pre-built plugins for specific departmental functions. And many of the workplace productivity services Trace will be drawing from, like Atlassian’s Jira, are launching their own agents, which will potentially compete with the startup’s system.

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

“2024 and 2025 was still about prompt engineering. Now we’ve moved from prompt engineering to context engineering,” says CTO Artur Romanov. “Whoever provides the best context at the right time is going to be the infrastructure on top of which the AI-first companies will be built. And we hope to be that infrastructure.”