AI agent cost governance manages spending at the level of an agent run rather than only one model call. It links usage to runs, users and tasks, then applies policies to loops, context, tools, subagents and model selection.
Good governance uses progressive interventions before termination. A system can compact context, reduce tool output or steer behavior while preserving task completion, then enforce a hard cap when the approved budget is exhausted.
ELI5
Agent cost governance tracks and limits spending across a complete AI agent run. It connects costs to tasks, users, loops, tools, subagents, context, and model choices instead of viewing each model call alone.
For example, a system can warn an agent when a research loop is becoming expensive, shorten repeated context, and stop only when the approved budget is exhausted. The controls should preserve useful completion whenever possible.
What costs can AI agent governance manage?
It can manage model tokens, tool calls, compute, storage and the extra usage caused by retries, loops and subagents.
Why govern cost at the agent-run level?
A complete run reveals cumulative behavior and task context that cannot be understood from isolated model calls alone.
