AI agent cost control
AI agent cost control means attributing model token spend to the agent, team and model that drove it, and enforcing token limits at the model call, so that spend is capped before it is incurred rather than discovered on the invoice.
Why agent costs are hard to see
A single agent task can make many model calls: planning steps, tool results fed back into the model, retries and loops. Provider invoices break spend down by account, project or key, not by agent or team, and each coding tool reports usage in its own console. The result is a total on an invoice with no clear owner, and a runaway agent that shows up only after the spend.
Attribution
Attribution ties every model call to the agent that made it, then rolls token usage and spend up per agent, per model, per provider and per team. It depends on each call carrying the identity of the agent behind it. Without that, spend can be split by key or by provider, but not by the workload that caused it.
Enforcement
Reporting shows spend after the fact. Enforcement applies a token limit at the model call, so an agent that reaches its limit is stopped at the next request. Limits can be set per agent, and for coding agents per user and per team. This is the difference between LLM cost governance and a monthly cost report.
Coding agents
Claude Code, Cursor, Copilot and Codex each report their own usage, so nothing shows what a developer or a team costs across all of them. Token budgets per user and per team, applied at the model call, give one view and one cap across every tool.
Cost control in CompFly
CompFly enforces token limits per agent at the model call, sets token budgets per user and per team for coding agents, and rolls up spend per agent, per model and per provider.