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Runtime agent authorization for tool-using AI.

BLUF

Runtime agent authorization checks tool name, arguments, actor, tenant, environment, risk, and approval state after the model proposes an action and before the tool executes.

Agent → proposed tool call → Veto policy check → allow/block/approval → underlying tool

Runtime means post-plan, pre-execution

The check runs after the model has made a concrete decision. That matters because policy can inspect actual arguments, not guessed intent. It also runs before side effects, so blocked and approval-gated calls never silently execute.

Tool name and declared capability
Arguments, amounts, recipients, paths, and IDs
Actor, user delegation, tenant, and project
Environment, branch, resource, and data class
Risk level, rate, session history, and time window
Approval state, approver identity, and expiration

Why runtime checks beat static grants

Static grants are coarse. They say an agent can call a payment API, database, browser, or deployment tool. Runtime authorization says whether this exact call should run now. Same tool, different arguments, different outcome.

Static grantRuntime questionLikely outcome
Can refund customer.Is this refund under threshold and within policy?Allow small, approve high-value, block suspicious.
Can query patient data.Is this patient in scope and is the purpose valid?Allow scoped reads, block broad export.
Can deploy code.Is this production, destructive, or outside window?Allow staging, approve production, block deletes.

High-risk workflows that need the boundary

The pattern is the same across verticals: put runtime policy between proposed action and side effect, then decide with concrete context.

Money movement

Workflows like refunds, ACH, wires, invoice approvals, treasury changes, or trading orders need amount, destination, and approval checks before execution.

PHI access

Healthcare workflows touching systems such as Epic, Cerner, FHIR, or EHR data need patient scope, purpose, and minimum-necessary checks.

Claims and underwriting

Insurance workflows in systems such as Guidewire, Duck Creek, or Salesforce need authority limits, SIU escalation, and denial controls.

Deploys and infrastructure

Coding and DevOps agents need environment, branch, command, migration, and production approval checks before changes apply.

Browser automation

Browser agents need URL allowlists, form submission gates, download controls, and data exfiltration limits before actions run.

External messages

Email, Slack, CRM, support, and social workflows need recipient, domain, attachment, PII, and approval checks before messages leave the system.

The decision surface

Keep the runtime surface small enough to test. Veto returns one of three outcomes and records the policy match, arguments, actor, tenant, timestamp, and approval context when present.

Allow

Run the underlying tool and record the policy match.

Block

Deny execution and return a safe reason to the agent.

Approval

Pause until a human reviews context and approves or denies.

Related routes

Runtime agent authorization FAQ

What is runtime agent authorization?

Runtime agent authorization is the policy check that runs after an AI model proposes a tool call and before the underlying tool executes. It evaluates the concrete call, not just the agent identity or prompt.

What does runtime authorization inspect?

It should inspect tool name, arguments, actor, tenant, project, environment, risk class, session history, rate, resource, and approval state. The exact fields depend on the action surface and policy model.

How is runtime authorization different from OAuth scopes?

OAuth scopes tell you what an identity can reach in broad categories. Runtime authorization decides whether this specific call with these arguments should execute right now. A token can permit transfers while policy still blocks or approval-gates a particular transfer.

Where does Veto fit in the execution path?

Veto wraps the tool boundary. The agent proposes a tool call, Veto evaluates policy and context, and the underlying function, API, browser action, deployment call, or MCP tool executes only after an allow decision.

Put the check where side effects start.

Review one workflow and leave with a concrete policy boundary.

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