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

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

For all their potential, AI brokers have been slow to make an affect in the enterprise, and one new startup is betting that the purpose they haven’t is an absence of context.

Launched as a part of Y Combinator’s 2025 summer season cohort, Trace is a workflow orchestration startup aimed toward filling that hole. The company maps complicated company environments and processes so that brokers have the context they want to scale rapidly.

“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’ instruments. “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 buyers Benjamin Bryant and Kevin Moore also invested.

Trace’s system begins by constructing a information graph from a company’s present instruments — techniques like e mail, Slack, and Airtable that form the day-to-day working lifetime of the agency. With that context in place, customers can immediate the system with a high-level job — like “We need to design a new microsite” or “Lets develop our 2027 sales plan” — and Trace will come again with a step-by-step workflow, delegating some duties to AI brokers and assigning others to human staff. When the system does invoke an AI agent, it should immediate it with the particular data wanted to full its sub-task.

The concept is to automate away the delicate work of on-boarding AI brokers, certainly one of the largest blockers for precise deployment within firms.

With so many firms centered on agentic AI, Trace may have loads of competitors. Earlier this week, Anthropic launched its personal tackle enterprise brokers, centered on pre-built plugins for particular departmental capabilities. And many of the office productiveness providers Trace can be drawing from, like Atlassian’s Jira, are launching their very own brokers, which can probably compete with the startup’s system.

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But Trace’s founders imagine their knowledge-graph method can be the key to success, as they will build context engineering deep into the construction 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.”

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