How do you define a cost per task budget for an agent and enforce it at runtime?

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Define cost per task budgeting for an agent. Describe the enforcement mechanism and explain what happens when the budget is exceeded mid-task.

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Quick reference

TL;DR

A cost per task budget caps the dollars one agent run may spend on tokens plus tool fees, enforced by a running counter that injects a graceful stop signal before the cap.

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Easy to grasp

Imagine you give a worker a prepaid card and one job to finish. Every phone call they make and every paid lookup they buy comes off that card. You do not want them to keep spending forever, so you watch the balance. When the card is nearly empty, you do not snatch the phone away mid-sentence and lose everything they were doing. Instead you tap them on the shoulder and say the money is almost gone, please wrap up and tell me what you found so far. They write a short summary and stop. An agent budget works the same way. The card balance is the running cost, the tap on the shoulder is a polite message added to its notes, and the summary is a partial answer instead of a crash that throws away all the work.

Key concepts

Cost Compounding Stopping Conditions Budget Enforcement

Quick scan · 2–4 min

Everything important, quickly.

Define cost per task as token plus tool cost over one run, explain the running counter and conservative pre-flight check, contrast graceful synthetic error injection with a hard exception, stress that a crash loses paid work, then close with tiered degradation and cost per successful task as the metric that matters.

Code example

python

def step_guard(state, budget):
    spent = state.token_cost + state.tool_cost
    next_estimate = budget.per_call + budget.per_tool
    if spent + next_estimate > budget.cap:
        # soft stop: model sees this and summarises
        state.observations.append(
            {"role": "tool", "name": "budget_exceeded",
             "content": f"Budget cap ${budget.cap} reached. "
                        "Return a partial answer now."}
        )
        return "stop_signal_injected"
    return "ok"

Side-by-side

Stop strategy	How it triggers	Effect on completed work
Hard termination	Runtime raises an exception when the cap is hit	All in-progress work is discarded; user gets a crash
Graceful stop	Runtime injects a synthetic budget exceeded observation	Model summarises progress and returns a partial answer
Tiered degrade	Spend crosses a soft threshold below the cap	Loop switches to a cheaper model to extend the run

Real-world examples

Real products, models, and research that use this idea.

Anthropic's Claude Agent SDK and the Claude Opus 4.7 tool use loop expose per-step token usage, letting a runtime accumulate spend and enforce a dollar ceiling across a task.
LangGraph lets you store a running cost field in typed graph state and add a conditional edge that routes to a summarise-and-stop node once spend crosses a threshold.
LangSmith and Helicone attribute token and dollar cost per trace and per run, so teams track cost per successful task rather than cost per individual model call.
Coding agents like Cursor and Devin degrade to cheaper model tiers and cap iterations on a task so a stuck edit-test loop cannot silently burn an unbounded budget.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QHow do you estimate the cost of the next step before you have made the call?

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QHow would you degrade to a cheaper model tier as spend rises instead of stopping outright?

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QWhy measure cost per successful task rather than cost per task, and how do you compute it?

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.