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Solutions · AI agent governance

Decide what AI agents may call, before they call it.

AIControls checks each tool call, MCP request and model call an agent makes against your policy: who it's acting for, what it may touch and how much budget is left. Then it allows the call, blocks it or asks a person.

aicontrols · gateway
timeidentitydecisionrequestlatency
40:16.713[email protected]AllowLLM claude-opus-5-56746ms
40:16.556[email protected]ObservedTOOL Bash0ms
40:15.928release-agentDenyTOOL kubectl delete↳ block-destructive-ops-production3ms
40:13.430[email protected]AllowLLM gpt-6-astra5262ms
40:12.712[email protected]AllowMCP github.create_pull_request412ms
40:09.510[email protected]DenyLLM claude-sonnet-5-5↳ content-safety-credentials2ms
Illustrative data · not a live tenant6 of 10,000+ events · 2 denied · live

Agents act on your behalf. Most governance finds out afterwards.

  • A coding agent picks up a new MCP server from a README. Nobody approved it.
  • A support agent retries a failing call in a loop. The invoice arrives at month end.
  • An ops agent holds a token that can write to production. It was meant for staging.
  • Control actions

    Write policy for which tools, MCP servers and clusters each agent can reach, and on whose behalf. Each call is allowed, denied or held for a person to approve.

  • Control spend

    Allow-list models and set token budgets per team or per agent. A call that would go over budget is denied before it reaches the model, and every call is attributed to a team.

  • Keep the record

    Each decision is logged with the policy that made it, next to your Kubernetes policy results. When someone asks why an agent was blocked, the answer is already written down.

Same policy engine. Additional control points.

AIControls extends Enterprise Kyverno's Admission control with three additional control points for AI workloads and agents — Pipeline, Gateway, and Runtime — so an agent is governed before it ships, on every model and tool call it makes, and while it runs.

AIControls

One engine · One audit log

  1. 1. Pipeline

    Registry · CI/CD · Admission

    Verify what ships

    • AI inventory
    • Trusted components
    • Signed & attested
  2. 2. Gateway

    LLM · MCP · A2A · TLS

    Control every call

    • Inspect prompts & responses
    • Allow · Deny · Require approval
    • Cost & attribution
  3. 3. Runtime

    eBPF · Clusters · Egress

    Contain what runs

    • Shadow AI discovery
    • Secure config
    • Runtime sandboxing

Powered by Kyverno — the CNCF policy engine your platform team already runs, now governing AI.

See what your agents are calling before you write a single policy.

A short working session on your own clusters. No agent changes, no code changes, and nothing enforced until you decide it should be.