Trace, a startup focused on streamlining AI agent deployment, has successfully raised $3 million to tackle the critical challenge of enterprise AI adoption. The company aims to bridge the gap between AI potential and practical business application by optimizing how organizations provide data to their AI systems.
From Prompt Engineering to Context Engineering
The shift in the artificial intelligence landscape is moving rapidly away from manual prompt crafting. According to CTO Artur Romanov, the industry is transitioning into a new phase defined by the quality of information provided to large language models.
“2024 and 2025 was still about prompt engineering. Now we’ve moved from prompt engineering to context engineering,” says Artur Romanov. “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.”
Solving the Enterprise Adoption Gap
Despite the hype surrounding AI agents, many enterprises struggle with integration due to fragmented data and a lack of relevant context for automated tasks. Trace is positioning its platform to become the foundational layer that ensures AI agents have the necessary information to function reliably within complex corporate environments.
By focusing on “context engineering,” Trace intends to simplify the deployment process, allowing businesses to scale their AI operations without being hindered by the technical complexities that have historically slowed down enterprise-wide adoption.
