For CIOs, CTOs, and Chief AI Officers
Get Agent Programs Past Review
and Into Production
Give platform, risk, and security leaders a shared basis for approval, with clear accountability and controls that keep pace as adoption grows.
What Keeps Agent Programs from Scaling
Successful pilots are only the beginning. Scaling requires evidence leaders can trust, consistent controls, and clear ownership when something goes wrong.
Approval Cycles That Repeat
Each use case brings questionnaires and evidence requests. Without a shared review standard, teams repeat questions and delay production.
Fragmented Controls
Teams implement their own limits, logging, and evaluations, increasing delivery costs and making it difficult to compare risk across the portfolio.
Unclear Accountability
Teams implement their own limits, logging, and evaluations, increasing delivery costs and making it difficult to compare risk across the portfolio.
Shared Controls, Separate Priorities
Bring Every Leadership Priority into One Platform
Give every leader a clear view of agent risk, the controls to manage it, and the evidence to support decisions.
CIO
Deploy inside your perimeter with identity from your directory, so access follows the groups and offboarding you already manage.
CTO
Adopt with a few lines of code, keep existing frameworks and tools, and ship agents without rebuilding controls for each project.
Chief AI Officer
See agents and use cases across teams in one registry, with one policy language for the limits on all of them.
Security Leads
Test vendor and internal agents as a black box, turn each finding into a policy, and close it only after the attack is replayed and blocked.
Risk and Audit
Rely on evaluation scores checked against your own reviewers, and trace each decision to the rule and input behind it.
How It Starts
- Your team
Pick one agent
One agent your teams want in production.
- Together
Agree the criteria
Success criteria and who signs off, written down before work starts.
- Together
Run the proof of value
A paid proof of value on your own infrastructure.
- Your team
Keep the results
Policies, decision records, findings, and detections export to your own systems.
Frequently Asked Questions
Most programs slow down at approval, not at build. When reviewers can see test results, readable controls, and recorded decisions, approvals move on evidence instead of extra meetings. Because every team uses the same controls, each new use case doesn’t start from scratch.
It works with them. Traces can keep flowing to Datadog or Splunk, identity comes from your existing directory, alerts route into your ITSM tools, and records export to your GRC system. What it replaces are the controls, logging, and evaluation each team currently rebuilds on its own.
It’s shared by design. Platform teams run it and write limits as policy, risk and compliance teams review the evidence, security tests agents and sets guardrails, and the Chief AI Officer gets one view of use cases across teams. Each group works from the same record instead of handing documents back and forth.
Controls run before an agent acts, not after, so limits on tools, parameters, and handoffs are enforced before a call goes out. If something does go wrong, you can show what the agent did, which rule allowed it, and who wrote that rule, which is the answer a board or regulator will ask for.
Set budget ceilings per team, use case, and user, enforced as each request arrives, so overruns stop at the limit instead of showing up on next month’s invoice. Every call is attributed to the agent and use case that made it.
Yes. Vendor and no-code agents can be tested as a black box with no code access, and traffic routed through the AI Gateway is limited to what each use case was approved to reach.