Despite their potential, AI agents have been slow to make an impact in the enterprise. A new startup, Trace, is betting the reason for this 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 AI agents have the context they need to scale quickly.
Trace CEO Tim Cherkasov explains the vision by comparing AI tools from labs like OpenAI and Anthropic to brilliant interns. He says Trace is building the manager that knows where to put them to work.
The London-based company recently announced it has raised three million dollars in seed funding. The round included investment 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 building a knowledge graph from a company’s existing tools, such as email, Slack, and Airtable. These systems shape the day-to-day working life of the firm. With that context established, users can prompt the system with a high-level task like designing a new microsite or developing a sales plan for 2027. Trace then returns 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 provides the specific data needed to complete its sub-task.
The goal is to automate the delicate work of onboarding AI agents, which is one of the biggest blockers for actual deployment within companies.
With many companies focused on agentic AI, Trace will face significant competition. Earlier this week, Anthropic launched its own take on enterprise agents, focused on pre-built plugins for specific departmental functions. Furthermore, many of the workplace productivity services Trace will draw from, like Atlassian’s Jira, are launching their own agents, which could potentially compete with the startup’s system.
However, Trace’s founders believe their knowledge-graph approach will be the key to success. They aim to build context engineering deep into the structure of agentic deployment. Trace CTO Arthur Romanov states that the industry has moved from prompt engineering to context engineering. He believes whoever provides the best context at the right time will become the infrastructure on which AI-first companies are built, and Trace hopes to be that infrastructure.