How do you enforce a $0.50 per-task LLM budget at runtime beyond setting max_tokens?

A $0.50 task budget is a constraint across the **whole task**, not on any one call. An agent might make 20 LLM calls in a single task. If each one is under max_tokens but the total adds up to $2, you blew the budget. The right design tracks **cumulative cost** in the runtime. Every LLM call adds to a running sum: input tokens times the input price, plus output tokens times the output price. The runtime knows at any moment how much has been spent and how much is left. Before each call, the runtime projects the cost: input size now, expected output size at current pricing. If projected cost is comfortably under remaining budget, the call goes ahead normally. If projected cost is close to the limit, the runtime takes action. It can switch the next call to a cheaper model (Claude Opus 4.7 to Haiku, GPT-5.5 to mini) which preserves the call at lower spend. Or it can inject an early-finalize prompt asking the agent to wrap up with what it has now. On budget exhaustion, the task hard-kills with a structured failure so the caller knows what happened. Silent truncation produces an answer that looks fine but might be missing the actual conclusion. max_tokens alone caps one call. A step cap is a rough proxy but breaks when later steps grow expensive. Worst-case pre-compute is impossible for an open-ended agent.

Per-task budget enforcement is **per-task, not per-call**. The runtime maintains a running sum of token cost across every LLM call the task makes, computed at the current provider rate. Before each call, the runtime projects the next call's cost: input tokens at the input rate plus expected output tokens at the output rate. Compare against remaining budget. Three states emerge. Far from limit: call proceeds normally. Close to limit: trigger one of two strategies. **Tier downgrade** swaps the next call to a cheaper model, preserving the call structure but at a lower spend. **Early finalize** injects a system prompt asking the agent to produce its best answer with what it has now. Both extend reachable runtime under the same budget. At exhaustion, the task hard-kills with a structured failure: a typed error containing budget consumed, budget allowed, last step reached, and a partial answer if available. Silent truncation is the antipattern. The caller has no way to tell that the response is incomplete, and downstream pipelines treat truncated output as the final answer. The other options each fail. max_tokens bounds one call; the task makes many. Worst-case pre-compute is unknowable because agent step counts are open-ended. A step cap is a proxy that breaks when context grows quadratically and later steps become expensive. Production systems instrument every LLM call with the running cost and emit it to a tracing tool so you can see budget burn per task in real time.