Control agent actions.At runtime.
Full visibility into agent execution.
Control tool calls, data flow, and risky actions before they run.
Backed by top investors
The core problem
Access proves permission.Flow proves safety.
Agent identity, user, action, and resource prove permission.
They do not prove the context flow behind the action is safe.
The missing layer betweenagents and actions.
Full visibility into runtime tool actions, with deterministic controls before execution.
Aligned with recognized security and AI assurance frameworks
Every agent action becomes a control point
Evaluate intent, data provenance, destination, and risk before any tool call executes.
CRM support agent
Support chat
CRM support agent
I’ll read the ticket, extract the customer fields, and send the email.
const ticket = await tool.support.readTicket("SUP-1842");const { customerEmail, customerName, emailSummary} = await tool.extract({ text: ticket.body});await tool.email.send({ to: customerEmail, subject: `Update for ${customerName}`, body: emailSummary,});tool
support.readTicket
origin
support.readTicket
value
ticket.body
source trust
policy
"Never send PII / confidential data by email."
decision
Allowed: support.readTicket
Agents bypass approval paths.
Put controls between intent and execution.
CodeIntegrity separates instructions, data, and actions
01 / Code execution
Prompts become executable control flow.
The runtime turns agent instructions into sandboxed code, making each action explicit, inspectable, and repeatable. Code becomes the control layer where intent, data, and tool calls can be checked before execution.
const ticket = await tool.readTicket("SUP-1842");const updates = []; for (const item of ticket.history) { updates.push(normalize(item));} await tool.sendEmail({ to: ticket.customer.email, body: updates.join("\n"),});02 / Dual LLM
Untrusted data stays separate from instructions.
One model controls the workflow. Another reads untrusted content and returns structured values inside the sandbox.
03 / Action evidence
Every sensitive action gets a security record.
CodeIntegrity records the request, source context, destination, policy decision, and outcome behind each agent action, so security teams can review what happened without replaying the whole session.
AI breaks all defenses.
Agents need deterministic control.
Research and insights defining the next era of AI agent security
CodeIntegrity Labs tracks the latest agentic threats, from prompt injection and tool abuse to data leakage across connected systems. Our AI agent security research turns emerging attack paths into clear evidence, practical controls, and executive-ready guidance.
Steven
Abi