HomeTrace raises $3M to tackle enterprise AI agent adoption hurdles
Despite their potential, AI agents have struggled to gain traction in the enterprise. One emerging startup believes the core issue is a lack of context.
Launched as part of Y Combinator’s 2025 summer cohort, Trace is a workflow orchestration startup designed to bridge that gap. The company maps complex corporate environments and processes, providing agents with the context they need to scale quickly.
“OpenAI and Anthropic are building brilliant interns that companies can leverage,” explains Trace CEO Tim Cherkasov, referring to the AI labs’ tools. “We’re building the manager who knows where to place them.”
On Thursday, the London-based company announced it had secured $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 participated.
Trace’s system begins by constructing a knowledge graph from a company’s existing tools — platforms like email, Slack, and Airtable that shape daily operations. With that context established, users can input a high-level task — such as “We need to design a new microsite” or “Let’s develop our 2027 sales plan” — and Trace responds with a step-by-step workflow, delegating some tasks to AI agents and assigning others to human team members. When the system activates an AI agent, it supplies the specific data needed for that subtask.
The goal is to automate the delicate process of onboarding AI agents, one of the biggest obstacles to real enterprise deployment.
Given the intense focus on agentic AI, Trace faces substantial competition. Earlier this week, Anthropic launched its own enterprise agent solution, centered on pre-built plug-ins for specific departmental functions. Meanwhile, many workplace productivity tools Trace relies on — like Atlassian’s Jira — are introducing their own agents, potentially competing with Trace’s system.
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Save up to $300 or 30% on TechCrunch Founder Summit
1,000+ founders and investors gather at TechCrunch Founder Summit 2026 for a full day of growth, execution, and real-world scaling. Learn from founders and investors who have shaped the industry. Connect with peers navigating similar growth stages. Walk away with actionable tactics you can apply right away.
Offer ends March 13.
Boston, MA|June 9, 2026REGISTER NOWHowever, Trace’s founders believe their knowledge-graph approach will be decisive, as they embed context engineering deeply into the structure of agentic deployment.
“2024 and 2025 were still about prompt engineering. Now we’ve moved from prompt engineering to context engineering,” says CTO Artur Romanov. “Whoever delivers the best context at the right time will become the infrastructure on which AI-first companies are built. We hope to be that infrastructure.”
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Despite their potential, AI agents have struggled to gain traction in the enterprise. One emerging startup believes the core issue is a lack of context.
Launched as part of Y Combinator’s 2025 summer cohort, Trace is a workflow orchestration startup designed to bridge that gap. The company maps complex corporate environments and processes, providing agents with the context they need to scale quickly.
“OpenAI and Anthropic are building brilliant interns that companies can leverage,” explains Trace CEO Tim Cherkasov, referring to the AI labs’ tools. “We’re building the manager who knows where to place them.”
On Thursday, the London-based company announced it had secured $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 participated.
Trace’s system begins by constructing a knowledge graph from a company’s existing tools — platforms like email, Slack, and Airtable that shape daily operations. With that context established, users can input a high-level task — such as “We need to design a new microsite” or “Let’s develop our 2027 sales plan” — and Trace responds with a step-by-step workflow, delegating some tasks to AI agents and assigning others to human team members. When the system activates an AI agent, it supplies the specific data needed for that subtask.
The goal is to automate the delicate process of onboarding AI agents, one of the biggest obstacles to real enterprise deployment.
Given the intense focus on agentic AI, Trace faces substantial competition. Earlier this week, Anthropic launched its own enterprise agent solution, centered on pre-built plug-ins for specific departmental functions. Meanwhile, many workplace productivity tools Trace relies on — like Atlassian’s Jira — are introducing their own agents, potentially competing with Trace’s system.
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Offer ends March 13.
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Boston, MA|June 9, 2026REGISTER NOWHowever, Trace’s founders believe their knowledge-graph approach will be decisive, as they embed context engineering deeply into the structure of agentic deployment.
“2024 and 2025 were still about prompt engineering. Now we’ve moved from prompt engineering to context engineering,” says CTO Artur Romanov. “Whoever delivers the best context at the right time will become the infrastructure on which AI-first companies are built. We hope to be that infrastructure.”
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