Run-level budgets are the missing piece in AI cost control. While traditional per-request limits only track individual API calls, run-level budgets monitor the total cost of an entire agent task—catching expensive loops and runaway spending that span multiple requests.
The Problem with Request-Level Budgets
Most AI cost control solutions focus on individual API requests. They limit how much a single call can cost, but this approach has a fatal flaw: AI agents don't work in single requests. They make multiple calls to complete tasks, and problems often span many requests.
Consider an agent stuck in a loop. Each individual request might cost only $0.01—well within any reasonable per-request limit. But if the agent makes 10,000 requests in a loop, that's $100 in wasted spending. Request-level budgets can't catch this because each request looks normal in isolation.
What Are Run-Level Budgets?
Run-level budgets track the total cost of an agent task from start to finish. A "run" is the complete lifecycle of an agent working on a goal: receiving the initial request, making multiple API calls, processing data, and delivering the final result.
By tracking costs at the run level, you can set meaningful limits: "This customer service query shouldn't cost more than $0.50" or "This data analysis task has a $5 budget." When a run exceeds its budget, the system can automatically terminate it before costs spiral further.
How Run-Level Tracking Works
Run Identification
Every agent task gets a unique run_id that persists across all API calls related to that task. This identifier links requests together, enabling cost tracking across the entire task lifecycle.
AgentWall automatically generates and tracks run IDs, associating each API call with its parent run. You can also provide your own run IDs for integration with existing tracking systems.
Cost Accumulation
As the agent makes API calls, costs accumulate against the run budget. Each request's cost is added to the running total, providing real-time visibility into how much the task has consumed.
Cost tracking includes all expenses: model inference, token usage, external API calls, and any other billable operations. This comprehensive tracking ensures no costs slip through unmonitored.
Budget Enforcement
When a run approaches its budget limit, warnings are triggered. When it exceeds the limit, automatic enforcement stops the run before additional costs accrue. This protection prevents a single runaway task from consuming your entire budget.
Setting Appropriate Budgets
Analyze Historical Data
Start by understanding typical costs for different task types. Analyze historical runs to see what legitimate tasks cost. Use this data to set budgets with appropriate margins—tight enough to catch problems but loose enough to allow normal operations.
Task-Based Budgets
Different tasks have different cost profiles. A simple query might need a $0.10 budget, while complex analysis could justify $10. Set budgets based on task type rather than using a one-size-fits-all approach.
AgentWall lets you configure budgets per agent type, per team, or per specific task categories. This flexibility ensures appropriate limits for each use case.
Dynamic Budget Adjustment
Budgets shouldn't be static. As you optimize agents and costs change, adjust budgets accordingly. Regular reviews identify opportunities to tighten budgets without impacting legitimate operations.
Benefits of Run-Level Budgets
Catch Expensive Loops
The primary benefit is detecting loops that span multiple requests. An agent making 100 cheap requests in a loop will exceed its run budget even though each individual request is inexpensive.
Predictable Costs
Run-level budgets make costs predictable and controllable. You know the maximum any single task can cost, making it easy to forecast monthly spending and prevent surprises.
Better Resource Allocation
Understanding run-level costs helps optimize resource allocation. You can identify which tasks are expensive, which agents need optimization, and where to focus cost reduction efforts.
Automatic Protection
No manual intervention required. Once budgets are set, the system automatically enforces them, stopping expensive runs before they cause damage. This automation scales to thousands of concurrent agents.
Implementing Run-Level Budgets
Integration
AgentWall makes implementation simple. Route your agent traffic through our platform, and run-level tracking happens automatically. No changes to your agent code required.
Configuration
Set budgets through the dashboard or API. Configure default budgets for different agent types, override them for specific tasks, and adjust limits in real-time without redeployment.
Monitoring
Real-time dashboards show current run costs, budget utilization, and historical trends. Alerts notify you when runs approach limits or exhibit unusual spending patterns.
Advanced Features
Soft and Hard Limits
Implement soft limits that trigger warnings and hard limits that stop execution. Soft limits alert operators to expensive runs while allowing them to continue if justified. Hard limits provide absolute protection.
Budget Pooling
Share budgets across multiple runs or agents. Team-level budgets let individual runs vary while maintaining overall spending control. This flexibility accommodates legitimate variation while preventing abuse.
Cost Forecasting
Based on current spending rates, AgentWall can forecast final run costs. If a run is on track to exceed its budget, you get early warning before the limit is reached.
Real-World Impact
Organizations using run-level budgets report dramatic cost reductions. One customer reduced their AI spending by 60% simply by implementing run-level limits that caught previously undetected loops and inefficiencies.
Beyond cost savings, run-level budgets provide peace of mind. You can deploy agents confidently knowing that no single task can cause financial damage, even if something goes wrong.
Conclusion
Run-level budgets are essential for effective AI cost control. By tracking costs across entire agent tasks rather than individual requests, you can catch expensive problems early, maintain predictable spending, and deploy agents with confidence.
AgentWall provides comprehensive run-level budget management with automatic enforcement, real-time monitoring, and flexible configuration. Take control of your AI costs today.
Frequently Asked Questions
Rate limits control request frequency. Run-level budgets control total cost across all requests in a task. Both are useful, but budgets provide direct cost control while rate limits manage load.
The system automatically terminates the run and returns an error. All actions taken before termination are logged for analysis. You can configure whether to allow partial results or fail completely.
Yes. AgentWall supports per-agent, per-team, per-task-type, and custom budget configurations. You can also override budgets for specific runs when needed.
Start by analyzing historical costs for similar tasks. Set budgets 20-50% above typical costs to allow for variation. Monitor and adjust based on actual usage patterns.