If an agent made a $1.4M decision at 2:47 a.m., would you know who owns it?
Autonomous agents now act, spend, and delegate to other agents in production — most with credentials, a budget, and no owner. Govern360 treats every agent as a first-class identity, caps its spend, attributes consumption across delegation chains, and instruments the humans meant to be supervising it.
Agent accountability is the practice of making every autonomous AI agent a governed, owned identity — so that when an agent acts, spends, or delegates to another agent, there is a clear owner, a budget, and a record of who is accountable for the decision.
The problem
Everyone governs the AI; almost nobody governs the humans supervising it. Organizations measure the agents but never track supervisor fatigue, whether overrides actually catch errors, or whether approvals are real review or rubber-stamping. Human-in-the-loop is the control most programs lean on for accountability — and the one nobody instruments.
How Govern360 handles it
Discover. Govern. Control — applied to this problem specifically.
Each autonomous agent is enrolled as a first-class identity with an owner and a policy scope — you can't govern an agent you haven't inventoried.
Per-agent token budgets enforce under the same phased modes as human users, and consumption is attributed across agent-to-agent delegation chains so a budget enforces at any node in the tree. MCP tool calls are governed alongside direct model calls, as one unit.
Govern360 is extending governance to the humans in the loop — supervisor attention, approval-fatigue, override-effectiveness, and human-in-the-loop health — so you can prove not just that a human was in the loop, but that the human was actually governing.
Questions, answered
How do you govern autonomous AI agents?
Govern360 treats every agent as a first-class identity in a registry with an owner and policy scope, enforces per-agent token budgets, attributes consumption across agent-to-agent delegation chains, and governs Model Context Protocol tool calls alongside direct model calls.
What is human-in-the-loop AI oversight, and how do you measure it?
Human-in-the-loop oversight is requiring a person to review or approve an agent's action. It's measured by instrumenting the supervision layer itself — approval fatigue, override effectiveness, supervisor attention, and human-in-the-loop health — not just by counting that a human was present.
Why do agentic AI programs fail after launch?
They commonly stall because oversight quality silently degrades — approvers rubber-stamp at volume, overrides stop catching errors — and nobody measures it, so the program looks governed right up until an audit or incident.