This template organizes an agent’s cost at the level where the decision happens: the task. It separates observed usage, rates, tools, retries, human review, cost per success and period limits. It is not an automatic calculator and does not replace the provider invoice.

How to use it

  1. Copy a version into an authorized environment and keep the original file as the template.
  2. Enter one row per agent and workflow for the selected period; use a task identifier or aggregation another person can audit.
  3. Keep observed numbers and hypotheses separate; do not turn an estimate into a measured result.
  4. Compare cost per run with cost per completed task and record the success rate.
  5. Set the decision: keep, calibrate, limit, scale or block.

Essential fields

  • Usage: expected and completed tasks, calls, tokens and retries.
  • Variable cost: model, tools, review and observability.
  • Decision: period budget, cost per success, evidence, owner and next review.

The EXAMPLE-001 row is fictional and exists only to show the format. Do not put full prompts, emails, tokens, secrets or real personal data in the file. Read the research on official cost controls for the method and the cost per task guide for interpretation.

[ PRACTICAL RESOURCES ]

Use these resources

CSVDownload the budget template
[ CONTINUE EXPLORING ]

AI Agents

Understand, build, evaluate and operate AI agents with clear goals and boundaries.

PILLAR GUIDEAI Agents: what they are, how they work and when to use themLearn how AI agents use tools, state, guardrails and evaluations, and decide when agentic systems make sense for products and operations.RELATED GUIDEHow to build AI agents: from outcome to operationsBuild AI agents with a practical method for outcomes, tools, context, safety, evaluations, staged launch and monitoring.RELATED GUIDEAI agents with n8n: architecture and production checklistBuild AI agents with n8n using triggers, tools, optional memory, human approval, evaluations and explicit error handling.RELATED GUIDEAI agent examples: use cases, metrics and risksExplore AI agent examples for product, support, operations and engineering, including tools, outcomes, metrics and risks.RELATED GUIDEAI agent evaluation templateEvaluate AI agents for task completion, tools, safety, cost, latency, oversight and readiness to launch.RELATED GUIDEAI agent evaluation: test cases, metrics and release gatesLearn how to evaluate AI agents for outcomes, trajectories, tools, safety, cost and readiness before releasing a change.RELATED GUIDEHow to operate AI agents in production: observability, SLOs and incidentsLearn how to operate AI agents with observability, proportionate SLOs, cost controls, incident response and reversible changes.RELATED GUIDEResearch: which controls do official documents recommend for operating AI agents?A reproducible audit of seven official documents covering observability, evaluation, metrics, human control and incidents in AI agents.RELATED GUIDEAI agent incident response: containment, recovery and rollbackLearn how to detect, contain, recover from and learn from AI agent incidents without confusing configuration rollback with reversal of external effects.RELATED GUIDEResearch: which official controls appear in AI agent recovery?A reproducible audit of eight official documents on detection, containment, resumption, fallback and approval in AI agent recovery.RELATED GUIDEAI agent cost management: cost per task, budgets and scaleLearn how to measure cost per task, set budget limits and decide when an AI agent is ready to scale.RELATED GUIDEResearch: which official controls appear in AI agent cost management?A reproducible audit of eight official pages covering measurement, attribution, limits and financial reconciliation for agent systems.

Primary sources