Every agent action,
checked before it runs.
Actera checks every action your AI agents try to take, like a refund, a payment or a production deploy, against rules you set. It allows the action, sends it to a person for approval, or blocks it before anything happens in your systems.
Context-aware decisions
Same request.
Three different outcomes.
Actera evaluates every action against live context from your systems: approvals already given, the state of the workflow, spend so far today and what other agents have done. One set of policies covers every situation, and each decision reflects what’s true at that moment.
Policies
Write the rule once.
Actera applies it every time.
Your finance, engineering and operations leads set what each agent can do, what needs approval and what’s off limits, and test the rules on real scenarios first. Actera enforces them outside the model, so no prompt, model update or clever wording can change them.
Approvals and audit
Keep a person in the loop
for the actions that matter.
Actions that go beyond an agent’s authority are routed to the right approver with the full context, and resume the moment they’re approved. Every decision is recorded with the policy behind it, so you can show auditors exactly why an agent acted.
What is Actera?
Actera is a guardrail layer for AI agents. You set rules for what your agents can do on their own, what needs a person’s approval and what’s off limits. Actera checks every action against those rules before it runs, and logs the result.
How does Actera check an action before it runs?
Your agent’s tool calls go through Actera first. Actera gathers the context it needs, like which agent is asking, the state of the work and what the agent has already done, then applies your rules. The result is allow, require approval or block. Only allowed or approved actions reach the tool.
Why isn’t a system prompt enough?
A prompt asks the model to behave, and a model can misread it, ignore it or be talked out of it. Actera’s rules run outside the model, so they apply the same way every time, whatever the model decides to do.
Does Actera replace our identity and access controls?
No, keep them. They decide which systems an agent can reach. Actera decides whether a specific action should happen right now, and it can use your existing identity and permission data as part of that decision.
Can we enforce spending limits and multi-step rules?
Yes. Rules can use running totals, required steps and who did what. A payment can be within its own limit but still need approval because it would take the agent over its daily total. An agent can be blocked from approving an invoice it created itself.
What happens when an action needs approval?
Actera pauses the action and sends the approver the request, the details and the reason it needs sign-off. If they approve, the action runs. If they reject it, it doesn’t. Either way, the decision is recorded.
Does Actera work with our agent stack, MCP tools and APIs?
Yes. Actera works with the models, agent frameworks, MCP servers and APIs you already use. The TypeScript SDK includes an MCP client interceptor and a client for other integrations.
Who writes and manages the rules?
The people who own a process, like a finance or engineering lead, write its rules in Actera. They can test rules on realistic examples and publish them as versions. Developers connect the actions and data the rules need.
How is Actera different from Cerbos?
Cerbos is a general authorization service for applications. Actera is built for AI agents: it tracks what each agent has already done, applies running limits, routes actions to people for approval and keeps a record of every decision.
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Give your agents real work.
Stay in control of what they do.
We’re working with early teams running agents in real workflows. Tell us what your agents do and we’ll show you where Actera fits.