Trace $3M Context Engineering Infrastructure Tackles AI Agent Enterprise Adoption Crisis
London-based Trace raised $3 million in seed funding to tackle what CEO Tim Cherkasov calls the enterprise AI agent adoption crisis, where brilliant AI capabilities meet corporate complexity and consistently fail to scale.
The fundamental bottleneck isn’t agent capability—it’s context. While OpenAI and Anthropic have built “brilliant interns,” most enterprises struggle to provide these agents with the organizational knowledge they need to operate effectively beyond proof-of-concept demonstrations.
The Enterprise Context Gap
Enterprise AI agent deployment faces a systematic failure rate exceeding 95%, according to industry estimates. The core issue isn’t technical sophistication but rather the delicate work of on-boarding agents into complex corporate environments where critical context spans email, Slack, Airtable, and dozens of other interconnected systems.
Traditional approaches rely heavily on prompt engineering—crafting the right instructions for AI agents. But as Trace CTO Artur Romanov explains, “2024 and 2025 was still about prompt engineering. Now we’ve moved from prompt engineering to context engineering.” The shift represents a fundamental infrastructure requirement: agents need structured access to organizational knowledge, not just better prompts.
Current enterprise workflows lose approximately 40% of productivity to context switching and information gathering. When AI agents lack this contextual foundation, they become expensive automation experiments rather than productive workforce multipliers.
Knowledge Graph Orchestration Architecture
Trace’s infrastructure begins by building comprehensive knowledge graphs from existing enterprise tools—mapping the relationships between systems like email, Slack, project management platforms, and document repositories that shape day-to-day operations.
This knowledge graph serves as the foundation for intelligent task routing. Users can prompt the system with high-level requests like “We need to design a new microsite” or “Let’s develop our 2027 sales plan,” and Trace responds with step-by-step workflows that delegate specific tasks to AI agents while assigning complementary work to human team members.
When invoking AI agents, the system provides precisely the contextual data needed for each sub-task, eliminating the manual context-gathering that typically bottlenecks agent deployments. The platform essentially creates an organizational memory layer that makes institutional knowledge accessible to both human workers and AI agents.
The architecture positions Trace as infrastructure—what Cherkasov describes as “building the manager that knows where to put them,” referring to the sophisticated AI capabilities emerging from major labs.
Early Enterprise Validation
More than 30 companies are already using Trace in production to automate repetitive workflows, representing early validation of the context engineering approach. The startup emerged from Y Combinator’s Summer 2025 cohort, indicating institutional backing for their infrastructure thesis.
The $3 million seed round included investment from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, and WeFunder, along with angel investors Benjamin Bryant and Kevin Moore. This diverse investor base suggests confidence in Trace’s approach to solving a widespread enterprise problem.
Founded by CEO Tim Cherkasov and CTO Artur Romanov, both experienced in enterprise software infrastructure, the company has positioned itself at the intersection of workflow automation and agent orchestration—two rapidly converging categories.
Market Infrastructure Consolidation
Trace enters a competitive landscape where established players are racing to capture the enterprise agent orchestration market. Anthropic recently launched enterprise-focused agent plug-ins for departmental functions, while workplace productivity platforms like Atlassian’s Jira are developing native agent capabilities that could compete with third-party orchestration layers.
The key differentiator lies in Trace’s knowledge graph approach, which builds context engineering deep into the infrastructure stack rather than treating it as an application-layer feature. This architectural choice positions the platform as foundational infrastructure for AI-first companies rather than another workflow automation tool.
The enterprise agent deployment crisis represents a broader infrastructure challenge: as AI capabilities advance rapidly, the supporting systems for organizational integration lag significantly. Context engineering infrastructure addresses this gap by making institutional knowledge systematically accessible to autonomous systems.
Looking Forward: Infrastructure Specialization
Over the next 12-18 months, context engineering infrastructure will likely emerge as a distinct category within the broader AI infrastructure ecosystem. Companies that can solve the organizational knowledge problem will become essential infrastructure for enterprise AI deployment.
The shift from prompt to context engineering signals a maturation in how enterprises approach AI agent integration. Rather than focusing on better instructions for agents, successful deployments will require systematic approaches to organizational knowledge management and intelligent task routing.
Trace’s early traction suggests that enterprises recognize context engineering as a fundamental infrastructure requirement rather than a nice-to-have feature. As agent capabilities continue advancing, the companies that control organizational context will likely become the platforms on which AI-first enterprises are built.
The enterprise AI agent adoption crisis represents one of the most significant infrastructure challenges in modern automation. While capabilities advance rapidly, deployment infrastructure remains fragmented. Platforms like Overclock complement context engineering infrastructure by providing execution orchestration for complex multi-step agent workflows, enabling enterprises to bridge the gap between proof-of-concept demonstrations and production-scale AI deployment.