Despite their considerable promise, AI agents have struggled to gain significant traction within enterprise environments. A new startup posits that this limited adoption stems from a fundamental absence of operational context.
Introducing Trace, a workflow orchestration startup emerging from Y Combinator’s 2025 summer cohort, which aims to bridge this critical gap. The company meticulously maps intricate corporate environments and processes, thereby equipping AI agents with the essential context required for swift and effective scaling.
Tim Cherkasov, CEO of Trace, articulates the company's vision by stating, “OpenAI and Anthropic are building these brilliant interns that can be leveraged within the company.” He further elaborates on Trace's role, adding, “We’re building the manager that knows where to put them,” in reference to the powerful tools developed by leading AI labs.
This past Thursday, the London-based firm announced a successful $3 million seed funding round. Notable investors include Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, and WeFunder, alongside angel investors Benjamin Bryant and Kevin Moore.
The core of Trace’s system involves constructing a comprehensive knowledge graph, drawing data from a company’s existing operational tools such as email, Slack, and Airtable, which collectively define the firm's daily workflow. Once this rich context is established, users can initiate the system with broad objectives, like “We need to design a new microsite” or “Let's develop our 2027 sales plan.” Trace then generates a detailed, step-by-step workflow, intelligently distributing tasks between AI agents and human collaborators. Crucially, when an AI agent is engaged, the system furnishes it with precisely the data required to execute its specific sub-task effectively.
This innovative approach aims to automate the intricate process of AI agent onboarding, a task that currently stands as one of the most significant impediments to their widespread deployment within organizations.
Given the intense focus across the industry on agentic AI, Trace is poised to encounter substantial competition. Earlier this week, Anthropic unveiled its own enterprise agent solution, emphasizing pre-built plugins tailored for specific departmental functions. Furthermore, many established workplace productivity services, such as Atlassian’s Jira, from which Trace draws foundational data, are also introducing their own agent capabilities, potentially creating direct competition for the startup’s system.
Nevertheless, Trace’s founders are confident that their distinctive knowledge-graph methodology will be pivotal to their success, enabling them to embed context engineering deeply within the very architecture of agentic deployment.
Arthur Romanov, Trace’s CTO, highlights a significant paradigm shift, stating, “2024 and 2025 was still about prompt engineering. Now we’ve moved from prompt engineering to context engineering.” He further asserts the strategic importance of this transition: “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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