Authensor

How We Built Budget Controls for AI Agent Spending

Authensor Team · 2026-02-13

How We Built Budget Controls for AI Agent Spending

AI agents can be expensive. Every tool call, every API request, every token generated costs money. And unlike a human developer who naturally paces their work, an AI agent will happily burn through your entire OpenAI budget in an afternoon if you let it.

We built SafeClaw's budget control system because we believe cost management is a safety concern. An agent that racks up a $500 API bill on a task you expected to cost $5 has failed in a meaningful way — even if it produced the right output.

The Problem

Modern AI agents interact with multiple cost-bearing services: LLM APIs for reasoning, cloud APIs for deployment, SaaS APIs for integrations, and more. Each has its own pricing model, its own metering, and its own billing cycle. There's no unified way to say "this agent can spend at most $20 today."

Developers have told us stories of agents caught in retry loops that generated hundreds of unnecessary API calls. One user reported a coding agent that, while trying to debug a test failure, made 200+ calls to an external API — each billed at $0.10. The test failure was a typo.

Our Approach

SafeClaw's budget control system introduces a unified spending abstraction across all agent actions. Here's how it works.

Cost Tagging — Every action that passes through SafeClaw can be tagged with an estimated cost. For LLM-backed actions, SafeClaw estimates cost based on token counts and model pricing. For external API calls, users can define custom cost maps in their policy configuration. Budget Pools — Budgets are organized into pools. You can create a pool per session, per day, per project, or per action category. Each pool has a hard cap and an optional warning threshold. When the warning threshold is reached, SafeClaw sends a notification. When the hard cap is reached, actions are denied. Hierarchical Limits — Pools can be nested. A daily budget of $50 might contain session budgets of $10 each. This prevents a single runaway session from consuming the entire daily allocation.

Implementation Details

Cost tracking is implemented as a lightweight middleware in SafeClaw's action pipeline. When an action is classified, the budget middleware checks the applicable pools before allowing execution.

For pre-execution cost estimation, we use a lookup table of known action costs that users can customize. For LLM token costs, we integrate with the agent framework's token counting to get accurate estimates before the action executes.

Post-execution, the actual cost (if available from response headers or billing APIs) is recorded and reconciled against the estimate. Over time, SafeClaw's estimates self-correct based on historical actual costs for your specific usage patterns.

What Happens at the Limit

When a budget pool is exhausted, SafeClaw has three configurable behaviors:

  • Hard Stop — All actions in the pool's scope are denied until the budget resets or is manually increased.
  • Escalate — Actions are routed to the human approval queue. The developer sees the current spend, the estimated cost of the pending action, and can make an informed decision.
  • Throttle — The rate limiter is tightened to reduce spending velocity, giving the developer time to intervene without completely stopping work.

    • The default is escalate, because it preserves developer autonomy while ensuring awareness.

    Dashboard Integration

    Budget status is displayed prominently in the SafeClaw dashboard. You can see real-time spend across all pools, historical spending trends, and per-action cost breakdowns. The data is also available via our API and CLI.

    We built CSV and JSON export for budget data so teams can integrate SafeClaw's cost tracking with their existing financial reporting tools.

    Try It

    Budget controls are available in SafeClaw's current release. Configuration details and examples are in our documentation, and the full implementation is on GitHub.

    Cost overruns are a real and underappreciated risk of autonomous AI agents. We think budget controls should be standard equipment, not an afterthought.