Summary
- Useful autonomy requires explicit goals, allowed systems, spending limits and stopping conditions.
- High-impact actions need reversible steps, evidence logs and a named human escalation path.
The appeal of AI agents is not conversation but delegated execution: arranging information, calling services and completing a chain of work. Reliability therefore depends on the authority around the model as much as on its reasoning. Teams should separate read access from mutation, cap money and scope, require confirmation at irreversible boundaries and preserve the evidence used for each decision. The next useful test is an adversarial exercise with an ambiguous request and a failing tool. A trustworthy agent should pause safely, expose uncertainty and return control before damage compounds.
