The Context Problem in Enterprise AI
For all their potential, AI agents have been slow to make an impact in the enterprise, and one new startup is betting that the reason they haven’t 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 agents have the context they need to scale quickly.
"OpenAI and Anthropic are building these brilliant interns that can be leveraged within the company," says Trace CEO Tim Cherkasov, referring to the AI labs' tools. "We're building the manager that knows where to put them."
Enterprises today are rich with data and tools, yet the disconnect between AI agents and the company's internal context remains a significant barrier. AI agents excel at reasoning and task execution, but without a clear understanding of where information resides, which systems are relevant, and how processes interconnect, their effectiveness is limited. This context vacuum leads to several challenges:
- Fragmented Data Access: Agents must navigate multiple platforms—CRM, ERP, email, chat—without a unified view, resulting in delays and errors.
- Process Misalignment: Automated workflows often fail because agents lack awareness of existing business processes and approval hierarchies.
- Tool Silos: Each tool operates as an island; agents cannot easily orchestrate actions across disparate systems.
As a result, many enterprises report pilot fatigue, where initial AI agent experiments do not translate into sustained productivity gains. The missing piece is a contextual layer that can translate generic AI capabilities into enterprise-specific actions.
Trace's Knowledge Graph Approach
Trace's system starts by building a knowledge graph from a company's existing tools — systems like email, Slack, Salesforce, SharePoint, and internal databases. By ingesting metadata, API endpoints, and usage patterns, Trace creates a relational map that captures entities, their attributes, and the relationships between them.
This knowledge graph serves as a contextual substrate for AI agents. When an agent needs to perform a task—such as generating a sales forecast—it can query the graph to locate the relevant CRM records, associated email threads, and historical performance metrics, all without manual navigation. The graph also encodes process flows, enabling agents to understand dependencies and triggers within the organization.
Key components of Trace's approach include:
- Automated Ingestion Pipelines: Secure, scalable connectors that pull data from SaaS and on-premise systems, preserving privacy and compliance.
- Semantic Enrichment: AI-driven tagging that assigns meaningful labels to data elements, making the graph interpretable by natural language queries.
- Dynamic Relationship Mapping: Continuous updates that reflect changes in tool usage, ensuring the graph remains current as businesses evolve.
- Agent Orchestration Layer: A middleware that translates the graph's structure into actionable context for AI agents, allowing them to "see" the enterprise landscape.
By providing this contextual foundation, Trace empowers AI agents to operate with a nuanced understanding of the enterprise environment, reducing the need for extensive customization and accelerating adoption.
The $3M Seed Funding and Strategic Vision
In February 2026, Trace announced a $3 million seed round led by a consortium of venture capital firms, including participation from Y Combinator alumni investors. The funding will be allocated to three primary initiatives:
- Product Enhancement: Expanding the knowledge graph's capabilities to support more complex enterprise scenarios, such as multi-step workflow automation and real-time data synchronization.
- Talent Acquisition: Scaling the engineering and sales teams to broaden market reach and accelerate integration partnerships.
- Go-to-Market Expansion: Strengthening outreach to enterprise customers, including pilot programs with Fortune 500 companies to demonstrate tangible ROI.
Tim Cherkasov emphasized that the capital will enable Trace to "move from prototype to production at scale," highlighting the strategic importance of context engineering in the next wave of AI adoption. "Our vision is to make context the default layer for any AI agent operating within an enterprise," he said.
The $3M infusion not only validates the market need for context-aware AI but also positions Trace to compete with emerging players who focus solely on agent capabilities without addressing the contextual gap.
Market Realities and the Path Forward
Enterprise AI agent adoption has been hampered by the very context challenges Trace seeks to solve. According to recent market analyses, while over 70% of large organizations have experimented with AI agents, fewer than 20% have achieved scalable success. The primary reasons cited include inadequate integration with existing systems, lack of actionable context, and insufficient governance frameworks.
Trace's approach directly addresses these pain points. By embedding context directly into the agent's operational environment, the startup reduces friction and increases the likelihood that agents will deliver consistent value. Moreover, the knowledge graph framework is extensible, meaning it can adapt to new tools and processes as enterprises modernize their tech stacks.
The broader market trend indicates a shift toward "contextual AI," where the focus is not just on raw model capability but on how AI interacts with the specific business environment. Trace's early entry into this space, combined with its Y Combinator pedigree and strong seed funding, positions it as a key player in shaping the future of enterprise AI agents.
Conclusion
The slow uptake of AI agents in enterprises is not due to a lack of capability, but rather a deficiency in contextual awareness. Trace's innovative use of knowledge graphs to provide real-time, actionable context bridges this gap, enabling agents to operate seamlessly within the complex ecosystems of modern businesses. With $3 million in seed funding and a clear strategic roadmap, Trace is poised to accelerate the adoption of context-aware AI, driving efficiency and unlocking new value across sectors.
As enterprises continue to grapple with the balance between AI potential and practical implementation, solutions like Trace's will become essential. The future of enterprise AI is not just about smarter agents—it's about smarter context.
