AI agent authorization
Give agents the authority they need, with a clear decision before every consequential action.
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What is AI agent authorization?
AI agent authorization determines which resources and actions an agent may access, on whose behalf, and under which conditions. It can include roles, scopes, resource permissions and contextual policies evaluated when a tool call executes.
Separate identity, access and the action decision
Authentication establishes who is making the request. Authorization determines whether that identity can perform it. In an agent workflow, record both the software actor and the user or service it represents. A single shared credential makes it difficult to explain whose authority was used.
Modern authorization can already be contextual and fine-grained. The practical question is whether your current checks evaluate the business conditions of each action. A tool permission may allow refunds while leaving the amount, invoice state and duplicate-refund check to your application. Gatewise is a policy checkpoint for those conditions.
A refund example: one tool, three different decisions
Consider an example policy that permits autonomous refunds up to $100, requires manager approval above $100 through $500, and blocks amounts above $500. The agent has permission to call the refund tool in all three cases, but the proposed amount changes the decision.
A $79 request with complete evidence returns ALLOW. A $349 request returns REQUIRE APPROVAL. A $2,000 request returns BLOCK. Missing invoice evidence should stop evaluation with INSUFFICIENT EVIDENCE, and a confirmed existing refund can return NO ACTION. These thresholds illustrate a policy; each organization defines its own.
Where to enforce an authorization decision
Enforce the result in the component that actually calls the business API: a server-side tool handler, application service or controlled gateway. Asking the language model to respect a returned decision is insufficient if it also holds credentials for an unguarded execution path.
Bind each decision to a specific action, parameters, actor and policy version. An approved refund for one invoice must not become approval for another recipient or amount. Re-evaluate when relevant evidence changes, and make the execution operation idempotent. If the policy service is unavailable, consequential writes should stop according to your failure policy.
Choosing the controls for your stack
- OAuth and delegated access: establish which user or service grants access to a protected resource.
- Roles and scopes: restrict which categories of operations an actor can request.
- Attribute or relationship policies: evaluate contextual permissions using resource and actor information.
- Action policies: apply operational rules such as amounts, duplicates, timing and required evidence.
- Human review: route understood exceptions to a person whose authority covers the request.
- Decision records: preserve the evaluated policy, evidence references, reason and execution outcome.
How Gatewise fits into AI agent authorization
Gatewise provides deterministic business policy evaluation before execution. Its decision API and TypeScript wrappers can be placed around supported tool handlers. Your application remains responsible for identity, access tokens and executing only actions that the decision permits.
Start with one action type, such as refunds or CRM updates. Map the evidence source, define the autonomous boundary and identify the approval owner. Use shadow evaluation to compare decisions against expected outcomes before enabling enforcement on that path. Gatewise is available through early access; integration requirements should be reviewed for your stack.
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