How to stop several AI agents from burning through your AI plan
Give the agents a daily share of the plan, measure every session from outside the AI, warn early, stop hard at the cap, and spread the share across the day. Here is how each step works, and what changes when the agents run on a subscription instead of a pay per token key.
Why a plan runs out fast with several agents
One person with one AI chat spends at the speed they type. Several agents spend at the speed they work, all at once, from the same plan. In one Ask HN thread (read 2026-11-11), people name two causes of the bill: agents that retry more than they should, and several agents researching in parallel.
Much of the advice on this is written for API keys, where the fix is a dollar limit in the provider console. When the agents run on a subscription plan, the limit is the plan itself: when it runs out, every agent and your own chat stop together. The goal changes from "spend less money" to "spend the plan on purpose".
Step 1: give the agents a daily share
Decide what part of the plan the agents may use each day, and treat the rest as yours. A share per day is easier to keep than a share per week, because a bad morning shows up the same day. One team that runs 16 agents on a single subscription caps each route per day on a rolling 24 hour window, so the count resets 24 hours after the first request (The $200/Month CEO, 2026-03-27).
Step 2: measure every session, outside the AI
An agent that is told "stay under budget" in its instructions still has to remember the rule, count correctly, and choose to stop. An agent in a retry loop does none of that. The measuring belongs to a small program that runs beside the agents, reads what each session spent, and stops a session when the share is reached.
Measure per session, as well as for the whole team. The same team writes that its next change would be a budget per agent, because one agent could use up the shared budget on a single research rabbit hole (The $200/Month CEO).
Step 3: warn early, stop hard
Two thresholds work better than one. At the first one, the agents hear that the day is running hot and move work that can wait to tomorrow; the team above warns its agents at 80% of a cap. At the second one, new sessions stop. Hosting platforms that sell agent runs work the same way: when a team reaches its credit cap, active agent runs stop and new runs are blocked (Netlify changelog, 2026-03-06).
Step 4: spread the share across the day
A daily share spent by 10 in the morning leaves the agents idle for the rest of the day, and the work that arrives in the afternoon waits. Pace the share: let the agents use what the clock allows so far, so a busy morning slows the agents a little and the afternoon still has room.
Step 5: keep a small reserve for urgent work
When the brake is on, something urgent still comes in: a broken page, a customer waiting. Keep a small slice of the daily share for that, and let only work you marked urgent use it. Everything else waits for the next day's share.
What we use: the spending brake in the AI Company System
We run our own AI companies on the AI Company System, and the spending brake ships with it. On a server, it measures every employee session from outside the AI and stops sessions when the company reaches its share of your plan, so no employee can skip it, and it paces the company across the day. On your computer you set the budgets in a file.
Every employee runs on your own AI plan and stops at the limits you set, so a small plan means a slower company, still inside its share. On both setups, a post, an email, a live ad, a payment or a deploy goes out only with your yes on the final version, and a key guard blocks any command or file that contains a written key.
If your agents also lose track of what they did yesterday, read how to give your AI memory across sessions.
Common questions
Why do several AI agents use up a plan so fast?
Every agent spends from the same plan at the same time, and each one can loop, retry or carry a long context. One agent stuck in a retry loop can spend more than all the others.
Is a spending cap inside the prompt enough?
A rule in the prompt is advice the agent reads. A cap that holds is measured from outside the AI and stops the session itself when the share is reached, so an agent in a loop meets it too.
Does a spending brake work when the agents run on my own computer?
On one computer you set the budgets in a file. The computer sees only its own sessions, so if other machines share the same plan, keep their use in mind or run the agents from one server that measures them all.
FAQ
Why do several AI agents use up a plan so fast?
Every agent spends from the same plan at the same time, and each one can loop, retry or carry a long context. One agent stuck in a retry loop can spend more than all the others.
Is a spending cap inside the prompt enough?
A rule in the prompt is advice the agent reads. A cap that holds is measured from outside the AI and stops the session itself when the share is reached, so an agent in a loop meets it too.
Does a spending brake work when the agents run on my own computer?
On one computer you set the budgets in a file. The computer sees only its own sessions, so if other machines share the same plan, keep their use in mind or run the agents from one server that measures them all.