Actera.dev
Actera checks every AI agent action against your policies before it runs, then allows it, routes it for human approval or blocks it. Live business context, MCP support and a full audit trail.
Actera checks every AI agent action against your policies before it runs, then allows it, routes it for human approval or blocks it. Live business context, MCP support and a full audit trail.
This page is designed to help you find out whether Actera.dev is good and if it is the right choice for you.
Actera is a guardrail layer for AI agents. Agents send each action to Actera before it runs, such as a refund, a payment, a data export or a production deploy. Actera checks it against your policies and live context (approvals already given, workflow state, spend so far, what other agents have done) and returns one of three outcomes: allow, require approval, or block.
Actions that need approval go to the right approver with the full context and resume once approved. Every decision is recorded with the policy behind it, so you can show exactly why an agent acted.
Policies are enforced outside the model, so no prompt or model update can change them. Actera works with Claude Code and Codex through open-source plugins, with your own agents through the TypeScript SDK, and with Claude through a remote MCP connector for reviewing activity and writing policies in conversation. Free to start.
Listed in
Policy decisions
Allow, require approval or block each agent action before it runs
Human approval
Routes actions to the right approver with full context; the action resumes once approved
Context-aware rules
Policies use prior approvals, workflow state, spend so far and other agents' actions
Audit trail
Every decision is recorded with the policy behind it
Integrations
Claude Code and Codex plugins, TypeScript SDK, remote MCP connector for Claude
Actera checks each action an AI agent tries to take before it runs, not after. Every action gets one of three outcomes: allowed, sent for approval, or blocked. Decisions use live context, such as approvals already given, the state of the workflow, spend so far today and what other agents have done, so one policy can give different answers to the same request. Policies are enforced outside the model, so no prompt, model update or clever wording can change them.
Authorization tools like Cerbos and Permit.io answer whether a user may access a resource. Actera is built for agent actions: it adds approval routing with full context, actions that resume once approved, and a decision record with the policy behind every attempt. It also ships ready-made enforcement for coding agents (open-source plugins for Claude Code and Codex), a TypeScript SDK for your own agents, and a remote MCP connector so policies can be reviewed and written from Claude. Teams can test rules on real scenarios before publishing them, and there is a free plan.
Companies putting AI agents to work on real systems: engineering teams running coding agents such as Claude Code and Codex, and product teams building agents that issue refunds, change records, export data or deploy code. Finance, engineering and operations leads use Actera to set what each agent can do on its own, what needs approval and what is off limits, and compliance teams use the decision record to show auditors why an agent acted.
Go for the API and policy engine, with gRPC and protobuf contracts, PostgreSQL and Redis. The web app is React and TypeScript. It runs on Kubernetes on AWS. Integrations include a TypeScript SDK, plugins for Claude Code and Codex built on their hook systems, and a remote MCP server for Claude.
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