Connecting an AI assistant to internal data is one thing. Giving an agent permission to query databases, update records, trigger workflows, or take actions based on its findings introduces a different level of risk. A read-only analytics agent, for example, might be useful for answering operational questions. An agent that can modify customer records or(Read More)
Connecting an AI assistant to internal data is one thing. Giving an agent permission to query databases, update records, trigger workflows, or take actions based on its findings introduces a different level of risk.
A read-only analytics agent, for example, might be useful for answering operational questions. An agent that can modify customer records or initiate financial transactions needs much tighter controls.
How are teams approaching this boundary in practice? Do you start with read-only access, require human approval for every action, or grant permissions based on the risk of each task?
What controls have proven useful without making the workflow too restrictive?




