How to Control AI Agent Costs with Budget Policies

AI agent costs can spiral when autonomous agents make unconstrained API calls to LLM providers. SafeClaw by Authensor enforces budget policies that cap spend at daily, weekly, and monthly intervals — per agent, per model, and per team. When a budget threshold is hit, SafeClaw denies further LLM calls until the next period resets or an administrator raises the limit, preventing surprise bills before they accumulate.

Quick Start

npx @authensor/safeclaw

Budget Policy Configuration

Define spend limits in your SafeClaw policy:

version: "1.0"
description: "AI agent budget controls"

budgets:
  # Global daily cap
  - scope: global
    limit: "$50.00"
    period: daily
    action: deny
    reason: "Daily global spend cap reached"

# Per-model limits
  - scope: model
    model: "claude-opus-4"
    limit: "$30.00"
    period: daily
    action: deny
    reason: "Daily Opus budget exhausted"

- scope: model
    model: "gpt-4o"
    limit: "$20.00"
    period: daily
    action: deny
    reason: "Daily GPT-4o budget exhausted"

# Weekly team budgets
  - scope: team
    team: "backend"
    limit: "$200.00"
    period: weekly
    action: deny
    reason: "Weekly backend team budget reached"

# Monthly organizational cap
  - scope: global
    limit: "$2000.00"
    period: monthly
    action: deny
    reason: "Monthly organization budget reached"

rules:
  - action: "*"
    effect: deny
    reason: "Default deny"

Tiered Budget Enforcement

Implement warning thresholds before hard stops:

budgets:
  - scope: global
    limit: "$40.00"
    period: daily
    action: warn
    reason: "80% of daily budget consumed — warning"

- scope: global
    limit: "$50.00"
    period: daily
    action: deny
    reason: "Daily budget exhausted — hard stop"

At the warn threshold, SafeClaw logs a warning but allows the action. At deny, the action is blocked. This gives teams time to prioritize remaining budget or escalate for an increase.

Per-Agent Cost Allocation

Track costs per agent instance:

budgets:
  - scope: agent
    agentId: "code-review-bot"
    limit: "$10.00"
    period: daily
    action: deny
    reason: "Code review agent daily limit"

- scope: agent
    agentId: "deployment-agent"
    limit: "$5.00"
    period: daily
    action: deny
    reason: "Deployment agent daily limit"

- scope: agent
    agentId: "research-agent"
    limit: "$25.00"
    period: daily
    action: deny
    reason: "Research agent daily limit"

Cost Tracking and Reporting

Monitor spend in real time:

# Current period spend summary
npx @authensor/safeclaw budget status

Historical spend report
npx @authensor/safeclaw budget report --period monthly --since "3 months"

Example output:

Budget Status (Daily — 2026-02-13)
──────────────────────────────────
Global:        $32.47 / $50.00 (64.9%)
claude-opus-4: $21.30 / $30.00 (71.0%)
gpt-4o:        $11.17 / $20.00 (55.9%)

Team: backend  $142.00 / $200.00 (71.0%) [weekly]

Preventing Runaway Costs

The most dangerous cost scenario is an agent in a loop making repeated LLM calls. SafeClaw's budget enforcement catches this:

budgets:
  # Tight per-minute rate limit catches loops early
  - scope: agent
    limit: "$2.00"
    period: "1 minute"
    action: deny
    reason: "Per-minute spend rate exceeded — possible loop"

Combined with action-level rate limiting:

rules:
  - action: llm.call
    rateLimit:
      maxRequests: 10
      window: "1 minute"
    effect: allow
    reason: "Rate-limited LLM calls"

This dual protection (cost cap + rate limit) ensures that even if individual calls are cheap, high-frequency loops are caught within seconds.

Budget Alerts

Configure notifications when thresholds are approached:

alerts:
  - trigger: budget.warn
    channel: slack
    webhook: "${SLACK_WEBHOOK_URL}"
    message: "AI agent budget warning: {scope} at {percent}% of {limit}"

- trigger: budget.deny
    channel: pagerduty
    serviceKey: "${PD_SERVICE_KEY}"
    message: "AI agent budget HARD STOP: {scope} exceeded {limit}"

Audit Trail for Cost Decisions

Every budget-related deny is logged with cost context:

{
  "timestamp": "2026-02-13T16:45:02.119Z",
  "action": "llm.call",
  "effect": "deny",
  "reason": "Daily global spend cap reached",
  "budgetScope": "global",
  "currentSpend": "$50.12",
  "budgetLimit": "$50.00",
  "period": "daily"
}

This gives finance teams clear evidence of when and why agents were throttled.

Why SafeClaw

• 446 tests covering budget computation, threshold evaluation, and period resets
• Deny-by-default extends to budget enforcement — agents cannot spend without authorization
• Sub-millisecond evaluation ensures budget checks do not slow agent workflows
• Hash-chained audit trail provides tamper-proof cost accounting records
• Works with Claude AND OpenAI — unified budget tracking across providers
• MIT licensed — no per-seat or usage-based licensing costs on top of your LLM spend

See Also

• Token Budgets for AI Agents: Controlling LLM Spend
• API Rate Limiting for AI Agents: Preventing Runaway Costs
• AI Agent Incident Response: A Playbook for Engineering Teams
• Building an AI Governance Framework with SafeClaw