AI Agent Cost Overrun: How to Set Budget Limits

AI agent cost overruns happen when agents consume more API tokens, make more external calls, or run longer than expected — often resulting in surprise bills of hundreds or thousands of dollars from a single runaway session. SafeClaw by Authensor enforces budget limits at the policy level, capping token usage, total actions, and execution time so your agent stops before it drains your account. This works with both Claude and OpenAI-powered agents, and the limits are enforced locally with no cloud dependency.

Why AI Agent Costs Spiral Out of Control

• Unbounded loops: The agent retries failing actions indefinitely, consuming tokens each attempt.
• Verbose reasoning: The agent produces long chain-of-thought outputs that burn through token budgets.
• Large context windows: The agent reads entire codebases into context, using thousands of tokens per action.
• Tool call chains: Each tool call incurs API costs — an agent that makes 200 tool calls in a session can cost 10-50x what you expected.
• No session limits: Without explicit caps, agents run until the task is "done" — which may never arrive.

Immediate Fix: Check Your Current Spending

Review your provider dashboard (OpenAI Usage, Anthropic Console) to see the actual spend. Then review SafeClaw's audit trail to understand what the agent was doing:

npx @authensor/safeclaw audit --last 50

Count the total actions and look for repeated patterns that indicate wasteful behavior.

Install SafeClaw and Set Budget Limits

npx @authensor/safeclaw

Configure Cost Controls in Your Policy

Add budget limits to your safeclaw.policy.yaml:

limits:
  max_total_actions: 50        # cap total actions per session
  max_execution_time: 300      # 5-minute session cap
  max_repeated_actions: 3      # prevent retry loops

budget:
  max_tokens_per_action: 4000  # cap per individual action
  max_tokens_per_session: 50000 # total token budget
  warn_at_percentage: 80       # alert at 80% usage
  hard_stop_at: 100            # deny all actions at 100%

Set Per-Action Limits

Some actions are more expensive than others. Apply granular limits:

rules:
  - action: llm.generate
    resource: "*"
    effect: allow
    max_tokens: 4000
    reason: "Cap token output per generation"

- action: file.read
    resource: "/src/**"
    effect: allow
    max_size: 1048576  # 1 MB - prevents reading huge files into context
    reason: "Limit file read size to control context costs"

- action: network.request
    resource: "https://api.openai.com/**"
    effect: allow
    max_repeats: 10
    reason: "Cap direct API calls"

Enable Budget Alerts

Configure alerts so you know when spending approaches your limit:

alerts:
  budget_warning:
    threshold: 80
    action: log
  budget_critical:
    threshold: 95
    action: pause  # pause agent and require human approval to continue

Troubleshooting Cost Overrun Scenarios

Agent consumed 100K+ tokens in one session: This usually indicates the agent was reading large files or producing verbose output. Add max_tokens_per_session to your policy and restrict file read sizes.

Agent made hundreds of API calls: Check the audit log for loops. Set max_repeated_actions and max_total_actions:

limits:
  max_total_actions: 30
  max_repeated_actions: 2

Multiple agents running simultaneously multiplied costs: If you run parallel agents, set per-agent budgets:

agents:
  code_writer:
    max_tokens_per_session: 30000
    max_total_actions: 40
  code_reviewer:
    max_tokens_per_session: 20000
    max_total_actions: 20

Agent kept running after task completion: The agent lacked a clear termination condition. Set a hard time limit:

limits:
  max_execution_time: 180  # 3 minutes hard stop

Prevention: Budget-First Policy Design

Design your policies with cost in mind from the start:

1. Start with conservative limits — you can always increase them.
2. Use simulation mode to estimate costs before real execution: npx @authensor/safeclaw --simulate
3. Set session budgets that align with your monthly API spend targets.
4. Review audit logs weekly to identify cost trends.
5. Use SafeClaw's deny-by-default model — actions not explicitly allowed cannot consume resources.

Related Resources

• AI Agent Timeout: Causes and Solutions
• AI Agent Stuck in a Loop: How to Stop and Prevent
• Token Budget Controls for AI Agents
• Enterprise AI Agent Cost Management