A practical starting point.
An AI agent can plan a sequence of API calls, but the model should not be the final source of permission. Treat a tool call as a proposal that must pass an execution boundary. Make the initiating user, delegated identity, intended resource, and allowed action visible to the component that actually performs the work.
Scope the tools
Expose narrowly defined capabilities rather than one unrestricted execution interface. Separate read-only work from consequential changes.
Check delegated access
Carry trustworthy user and tenant context into the executor. Recheck permission when the resource state or membership changes.
Bind approvals precisely
For consequential operations, associate approval with the specific action, parameters, resource, and relevant validity conditions.
Turn the concept into a review.
Map consequences
Classify what each tool can read or change. Identify actions that need confirmation, tighter budgets, or a separate execution identity. Define what happens when context is incomplete.
Keep policy outside the prompt
Enforce permissions in server-side components using authoritative context. A textual instruction to behave safely does not revoke a credential or limit a tool implementation.
Control the whole workflow
Bound iterations, retries, time, and delegated calls. Record proposals, decisions, execution attempts, and results separately so an operator can reconstruct what happened.
Questions worth asking.
Can an approval cover an entire conversation?
For consequential actions, broad conversational consent may be ambiguous. Bind approval to the action and parameters the user actually reviewed, and revalidate when those conditions change.
Are internal tools automatically trusted?
No. Internal placement does not establish that every caller may perform every action. Keep explicit identities, scopes, and resource checks at the execution boundary.
Go deeper in the journal.
AI agent least privilege: let the model propose, let policy decide
A permission model for tool-using agents, with scoped identities, specific approvals, and auditable execution.
Prompt injection and API security: keep untrusted content out of control
Why valid requests can still carry unsafe instructions, and how to protect the boundaries around retrieval, tools, and output.
AI token budgets: control consumption before the bill arrives
Design per-request, workflow, and tenant budgets with explicit estimates, reconciliation, and safe stopping behavior.