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

Despite the immense potential of AI agents, their adoption in the enterprise sector has been sluggish. A promising startup, Trace, is now stepping in to address this challenge, believing that the root cause lies in the absence of contextual understanding. Emerging from Y Combinator's summer 2025 cohort, Trace focuses on workflow orchestration to bridge this gap. The company’s approach involves mapping intricate corporate environments and processes, ensuring that AI agents receive the context necessary for rapid scalability. "OpenAI and Anthropic are creating remarkable AI tools that can function within companies," stated Trace CEO Tim Cherkasov, emphasizing the need for effective management of these resources. "We are developing the manager who knows exactly where to place these agents." Recently, Trace announced it has raised $3 million in seed funding, with contributions from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, and WeFunder. Notable angel investors Benjamin Bryant and Kevin Moore also joined the funding round. Trace’s innovative system begins by constructing a knowledge graph derived from a company's existing tools, such as email, Slack, and Airtable, which are integral to daily operations. Once this context is established, users can issue high-level tasks—like designing a new microsite or formulating a 2027 sales plan. In response, Trace generates a detailed workflow, assigning tasks to both AI agents and human employees. When utilizing an AI agent, Trace ensures that it is equipped with the relevant data necessary to accomplish its designated sub-task. This strategy aims to streamline the onboarding process for AI agents, a significant hurdle in their implementation within organizations. As the focus on agentic AI intensifies, Trace faces competition from various players in the field. Just this week, Anthropic unveiled its version of enterprise agents, which emphasizes pre-built plugins tailored for specific departmental functions. Additionally, many productivity platforms, like Atlassian’s Jira, are launching their own AI agents, potentially rivaling Trace’s offerings. Nevertheless, the founders of Trace are confident that their knowledge-graph methodology will be pivotal to their success. They believe embedding context engineering deep within the framework of agentic deployment will set them apart. "The transition from prompt engineering to context engineering is crucial," remarked CTO Arthur Romanov. "The company that delivers the most effective context at the right moment will form the backbone for the AI-first enterprises of the future—and we aspire to be that backbone."