An AI agent is not one model call. It may inspect context, plan, call a tool, read the result, retry an error, and ask for approval before it finishes. That loop can be useful, but every step consumes tokens, latency, compute, or an external service budget.

The short answer

Budget an agent run as a workflow, not as a single request. Track model calls, input and output tokens, tool calls, retries, storage reads, and human approvals. Put limits on steps and time, then measure cost per successful task rather than cost per model response.

Draw the execution loop

Start with a trace that records run ID, user request, model, prompt version, tool name, duration, result size, and outcome. A basic loop might be: classify, plan, retrieve, act, verify. Each transition should have a reason and a maximum retry count.

This is important because a failed tool can cause an agent to repeat itself. If one run makes 8 calls instead of the normal 3, the average cost may look fine while a small group of failures consumes the budget.

Measure useful work

The cheapest call is not always the best call. A small model may save money but require more retries or produce an invalid tool argument. Compare cost per completed task, median duration, p95 duration, and correction rate.

I would run a representative 2-hour test with real prompt shapes but safe tools. Include timeouts, malformed arguments, provider errors, and a user cancellation. Save the trace sample so a later model change can be compared with the same workload.

Put limits around autonomy

Set a maximum number of model turns, tool calls, wall-clock time, and output tokens. Require approval before destructive actions, financial actions, or changes to important state. An agent should be able to stop and explain why it stopped.

Use idempotency for tools that write data. A retry after a network timeout must not charge a user twice or create duplicate records. Keep a durable operation ID and record the provider response separately from the agent’s interpretation.

Control the expensive paths

Cache stable retrieval results, summarize long histories, and send simple classification work to a smaller model when quality remains acceptable. Avoid putting the entire conversation and every tool result into every subsequent prompt. Context is a cost and latency multiplier.

Route interactive work to capacity with predictable latency and send batch work to a cheaper queue. Reserved and on-demand compute are different tradeoffs, so measure both against the product’s actual traffic rather than using a provider’s marketing estimate.

What to show on a dashboard

Track success rate, average and p95 run cost, model calls per run, tool calls per run, retry rate, approval wait time, and abandonment. Also track the number of runs that exceed a limit. A falling token price does not help if the agent now takes twice as many steps.

Sources and limits

OpenAI’s Cerebras partnership illustrates why low-latency inference can matter for interactive workloads. Its Amazon partnership shows how custom cloud silicon can become part of a larger serving strategy. The OpenAI scaling update provides broader context on dedicated inference capacity. These announcements do not predict the cost of your agent; only your traces can do that.

The durable principle is simple: autonomy must have a budget, an audit trail, and a controlled stop. That makes an agent easier to trust and easier to improve.

Before launch, test the limit behavior itself. A well-designed agent should return a useful status when it reaches a step or cost cap, preserve the trace, and let the user resume deliberately. Silent loops are both an engineering bug and a budget leak. Measure the normal path and the failure path separately, then keep the safer default when the numbers are close.

Run that test for 2 hours with a representative mix of short and long tasks, then review the highest-cost traces manually. The outliers often reveal an unclear tool description, an oversized context window, or a retry policy that should be fixed before adding more autonomy.