What is AI agent authorization?

It is the decision, made before an agent acts, about whether that specific action is allowed. With Rends, the agent asks for a policy check before a tool call runs and gets back allow, warn, block or escalate.

How do I add authorization to AI agent tool calls?

Call Rends before the tool runs, using our Python SDK (adapters for LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents, Google ADK, Haystack, LlamaIndex and PydanticAI), our TypeScript SDK (LangChain, OpenAI Agents and Google ADK adapters) or the REST API, and act on the decision: run on allow or warn, stop on block, wait on escalate. In enforce mode the SDK does this for you.

What about privilege escalation?

Policy checks are not privilege escalation prevention on their own. They limit what an agent can do, for the actions it submits for a check, and work alongside your identity, credential and infrastructure controls.

AI agent authorization: a policy check before a tool call runs

AI agent authorization decides whether an agent may take a specific action before it takes it. With Rends, the agent asks for a policy check before a tool call runs, through our SDK or API, and gets a decision back. This page explains how that works, and what it does not do.

Runtime authorization for AI agents: the policy check

Before a tool call runs, the agent sends Rends a description of the action: what it wants to do, on which resource, and a summary of the input. Rends evaluates it against your configured policies and answers with one of four decisions: allow (proceed), warn (proceed, with the warning recorded), block (do not run), or escalate (hold the action for approval).

This is pre-execution policy enforcement: the decision comes before the tool call, not after it.

Allow/deny policy for AI agent tool calls

In enforce mode, a block decision is a deny: the SDK does not run the tool call. (The SDKs also have a monitor mode that records decisions without stopping calls.) Policies decide which tools and actions an agent may use, and on what terms, in one place instead of inside each agent's code.

Policies are written in RPL, the Rends policy language, published as an open specification. That makes them policy as code for AI agents: reviewable text rather than settings scattered across tools.

Tool call authorization for high-risk actions: approval holds

An escalate decision holds the action until someone approves it, and each approval is recorded. Automatic resume after an approval is still in testing; our list of known limits says so.

Emergency stop

Rends has an emergency stop for agents, running in production. While an agent is stopped, its policy checks come back as block, so its tool calls do not run.

AI agent access control

Policy checks apply least privilege to AI agents: each agent gets only the tools, actions and limits its policies allow. API calls work the same way when the call is the action the agent submits.

Policy checks limit what an agent can do. They are not privilege escalation prevention on their own: they apply to the actions an agent submits for a check, and they sit alongside your identity, credential and infrastructure controls.

Authorization for autonomous agents: SDKs and adapters

Agentic AI authorization works the same way whichever framework the agent uses, as long as it asks Rends before it acts. Today that is our Python SDK (rends-agent-sdk, with adapters for LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents, Google ADK, Haystack, LlamaIndex and PydanticAI), our TypeScript SDK (@rends/agent-sdk, with LangChain, OpenAI Agents and Google ADK adapters), or our REST API from any language.

Try it on your own agent: request a 14-day free trial, or book a demo.

Frequently asked questions

What is AI agent authorization?: It is the decision, made before an agent acts, about whether that specific action is allowed. With Rends, the agent asks for a policy check before a tool call runs and gets back allow, warn, block or escalate.
How do I add authorization to AI agent tool calls?: Call Rends before the tool runs, using our Python SDK (adapters for LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents, Google ADK, Haystack, LlamaIndex and PydanticAI), our TypeScript SDK (LangChain, OpenAI Agents and Google ADK adapters) or the REST API, and act on the decision: run on allow or warn, stop on block, wait on escalate. In enforce mode the SDK does this for you.
What about privilege escalation?: Policy checks are not privilege escalation prevention on their own. They limit what an agent can do, for the actions it submits for a check, and work alongside your identity, credential and infrastructure controls.
What happens when the decision is escalate?: The action is held until someone approves it, and each approval is recorded. Automatic resume after an approval is in testing.