The honest worry about handing work to an agent is not whether it will mostly get things right. It is what happens on the day it does not. A system you can trust is one where a mistake is small, visible, and reversible, by design, not by luck.
Mistakes are a question of blast radius
Any system that does real work, human or agent, will occasionally get something wrong. The thing that matters is not pretending otherwise; it is bounding the damage. Good design keeps an error contained, surfaces it quickly, and makes it easy to undo. That is the lens to judge any agentic system by.
Permissions cap what can go wrong
An agent acts only within the permissions of the person it represents, checked when its tools are offered and again when they run. It cannot touch what that person could not, so the worst case is bounded by a boundary you already understand. A mistake stays inside the lane you set.
Approvals catch the consequential ones
The decisions that would hurt most, a large refund, a contract, a payment over a threshold, are exactly the ones routed to a person before they happen. The agent prepares them; you approve them. So the high-stakes actions never run unattended in the first place.
The audit trail makes it reversible
Every action is recorded: what the agent did, on whose behalf, and when. When something is wrong, you can see exactly what happened and step it back, rather than guessing. Catchable and reversible beats confident and opaque every time.
Why this beats a confident wrong answer
A model on its own can produce a fluent, wrong answer with no trace. An agent operating real tools inside a permissioned, audited system is the opposite: its actions are bounded, logged, and checkable. The structure around the model is what turns raw capability into something you can actually rely on.
Trust is not the absence of mistakes; it is what the system does about them. See how SOIS bounds and records agent actions, or launch a workspace and set the limits before you delegate a thing.