01
Agent activity trail
Preserve the relationship between agent runs, pull requests, specs, and review outcomes.
Agent trust governance
Govern the agents writing your production code.
Agent trust requires more than monitoring prompts. Cognium evaluates the code, tools, skills, repository context, and policy outcomes behind each agent-generated change.
Agent trust is a system property
Agent trust is a system property
A coding agent can be safe in one repository and risky in another. Cognium evaluates the agent workflow in context: what it changed, what tools it used, and what policy should apply.
02
Repository-aware risk
Score changes against the service, framework, data sensitivity, and deployment path.
03
Policy enforcement
Route low-risk work forward and hold risky changes for review.
Implementation
Controls for enterprise adoption
Teams can adopt agents without abandoning security and compliance expectations.
Bring your own agent
Works with Codex, Claude Code, Cursor, Copilot, Gemini CLI, and custom agents.
Role-aware policies
Apply different thresholds for internal tools, regulated services, and experimental repos.
Audit-ready history
Keep decision records that explain why an agent-created change shipped or was blocked.
Comparison
How Cognium changes the workflow.
These pages are built for teams evaluating AI coding security, agent trust, and enterprise governance beyond basic scanner checklists.
| Current approach | Typical gap | Cognium approach |
|---|---|---|
| Agent logs | Raw events without release context. | Agent activity tied to PR risk and policy. |
| Human approval | Approval depends on reviewer confidence. | Approval depends on evidence and thresholds. |
| Tool sprawl | Agents discover arbitrary public tools. | Tools are scored, preferred, restricted, or revoked. |
Agents do the work. Cognium proves what they did.
Start with one repository in staging. Cognium records what agents changed, verifies the result, and preserves the evidence behind each release decision.