CodeIntegrity was founded in May 2024 by CEO Steven Jung and CTO Abi Raghuram, who met as undergraduates at the Rose-Hulman Institute of Technology and reconnected to build the company in the Seattle area before relocating its founders to San Francisco. The company addresses a fast-emerging risk: as enterprises deploy AI agents that can call tools, move data, and take actions autonomously, those agents become unpredictable actors with real access to production systems, and traditional security controls were never designed for them.
CodeIntegrity's core product is a runtime control layer that sits between AI agents and the enterprise systems they interact with. It acts as both a translator and a filter, forcing an inherently probabilistic model to operate within strict, deterministic rules. The layer governs which systems and data an agent can touch, controls tool calls and data movement, and inserts approval gates so that high-risk actions require explicit authorization before they execute.
Beyond enforcement, the platform is built for accountability. It captures execution evidence for every agent action, creating an audit trail that security and compliance teams can review to understand exactly what an agent did, what data it accessed, and which guardrails were applied. This combination of prevention and evidence is aimed squarely at enterprises that want to adopt agentic AI without exposing themselves to uncontrolled or unauditable behavior.
The company announced a $5 million seed round, led by cybersecurity-focused Syn Ventures with participation from existing pre-seed investors Antler and Boost VC. The backing from a dedicated security investor reflects how the agentic AI security category has become a distinct and urgent area of enterprise spending as autonomous agents move into production workflows.
CodeIntegrity competes within the emerging agentic AI security and guardrails space, but its emphasis on a deterministic runtime control layer with fine-grained tool and data governance plus execution evidence positions it for security and platform teams that need hard, auditable constraints on what their AI agents are allowed to do.