AI Agent Governance Starts With the Tools You Expose

Description and use

<p>When an AI agent can call APIs, update records, or trigger workflows, governance becomes an engineering concern.</p> <p>The implementation needs to answer practical questions: Which actions are permitted? Where is approval required? Who can stop the workflow? What evidence will help explain a mistake?</p> <h3> Give Each Agent a Defined Job </h3> <p>A narrow responsibility makes permissions easier to design.</p> <p>For example, an invoice agent might extract document fields, validate required values, and create a draft record. Approving a payment is a separate responsibility that needs its o

Problem

<p>When an AI agent can call APIs, update records, or trigger workflows, governance becomes an engineering concern.</p> <p>The implementation needs to answer practical questions: Which actions are permitted? Where is approval required? Who can stop the workflow? What evidence will help explain a mistake?</p> <h3> Give Each Agent a Defined Job </h3> <p>A narrow responsibility makes permissions easier to design.</p> <p>For example, an invoice agent might extract document fields, validate required values, and create a draft record. Approving a payment is a separate responsibility that needs its o

Outcome

<p>When an AI agent can call APIs, update records, or trigger workflows, governance becomes an engineering concern.</p> <p>The implementation needs to answer practical questions: Which actions are permitted? Where is approval required? Who can stop the workflow? What evidence will help explain a mistake?</p> <h3> Give Each Agent a Defined Job </h3> <p>A narrow responsibility makes permissions easier to design.</p> <p>For example, an invoice agent might extract document fields, validate required values, and create a draft record. Approving a payment is a separate responsibility that needs its o

Evidence

AI Agent Governance Starts With the Tools You Expose

Source