--- title: "AI Agent Cost per Action: Token Bill Calculator" canonical: https://wavect.io/blog/ai-agent-cost-per-action-2026/ language: en description: "Why do agentic workflows blow up token bills? Calculate AI agent cost per action for support tickets, invoices, PR reviews, lead enrichment, retries, tools and evals." image: "https://wavect.io/img/blog/headers/header_ai-agent-cost-per-action-2026.png" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 09 Jul 2026 Last reviewed August 7, 2026 [**Next**](/blog/local-models-vs-apis-break-even-eu-2026/) # AI Agent Cost per Action: Why Agentic Workflows Blow Up Token Bills TL;DR AI agent cost per action is the all-in cost of one successful business outcome, not one model call. Include failed attempts, tool calls, retries, approvals, escalation, and human rework in the numerator. Measure resolved tickets, extracted invoices, reviewed pull requests, or enriched leads. Reduce cost by tracing every action, capping loops, narrowing tools, caching stable context, routing simple steps to cheaper models, and validating quality with evals. Provider prices are a July 2026 snapshot, so re-check them before budgeting. Related service: [AI Enablement](/services/ai-enablement/) Executives do not buy tokens. They buy actions: a support ticket resolved, an invoice extracted, a pull request reviewed, a lead enriched, a refund approved, a claim triaged. That is the unit your AI agent cost model should use. Cost per action is the all-in cost to complete one business action to your quality bar, including every model call, tool call, retry, verifier, escalation, cached token, uncached token, and human correction. This is narrower than our [LLM cost calculator](/blog/llm-cost-calculator-2026/). That article explains cost per successful task across LLM systems. This one focuses on agentic workflows, where the bill grows because the agent loops: it plans, calls tools, reads tool output, updates context, asks another model, verifies, retries, and sometimes hands work to another specialist. OpenAI's Agents SDK docs describe agents as applications that plan, call tools, collaborate across specialists, and keep state, and its runner can perform the tool loop, switch agents after handoffs, and trace model calls, tools, agents, guardrails and handoffs. Each one of those surfaces can become a cost line. ## What is AI agent cost per action? AI agent cost per action is the total cost of one completed business outcome, not one model call. It includes input tokens, cached input, output tokens, tool schemas, retrieved context, tool observations, retries, verifier calls, escalations, human review, and failed attempts divided by successful actions. The formula is: Cost per action = (sum of all model-call costs + tool and retrieval costs + retry costs + verifier costs + human rework) / successful actions. For agentic systems, the denominator matters as much as the numerator. If an agent attempts 10,000 lead enrichments but only 8,700 pass validation, the cost per action uses 8,700. Failed actions are not free. They consumed tokens, tools, time, and trust. ## Why agents multiply tokens A single LLM call is a line item. An agent run is a trace. The ReAct paper made the pattern famous: language models can interleave reasoning traces and actions so they can gather information from external environments before continuing. That is useful, but it changes the economics. Every loop carries more history and more observations. | Cost multiplier | What happens | Why the bill grows | | --- | --- | --- | | Planning | The agent asks what to do next. | Planner prompts and reasoning output become extra calls before work begins. | | Tool schemas | Available functions, MCP tools, permissions and examples are included in context. | Static tool descriptions can be large, and they repeat unless caching works. | | Tool observations | Search results, CRM records, invoices, diffs or logs are fed back into the model. | Every observation becomes new input on the next step. | | Retries | Schema failures, timeouts, low confidence or blocked tools trigger another pass. | The second attempt often carries the full first attempt as context. | | Verification | A separate model or rule layer checks grounding, policy, extraction quality or code risk. | Quality control is necessary, but it is another cost line. | | Handoffs | A general agent delegates to a specialist. | Context is copied, summarized or re-expanded for the next agent. | | Human review | Borderline actions pause for approval. | Approval can be the right choice, but the handoff and audit trail are not free. | ## Four examples executives recognize The trap is to budget for "one agent request." A real budget starts with the action. | Business action | Naive assumption | Typical agent trace | Cost metric | | --- | --- | --- | --- | | Support ticket resolved | One answer. | Classify, retrieve policy, inspect account, draft, verify, maybe ask approval, update CRM. | Cost per resolved ticket that passes QA. | | Invoice extracted | One OCR plus JSON output. | Read file, classify layout, extract fields, call vendor lookup, validate totals, retry missing fields. | Cost per invoice posted without manual correction. | | PR reviewed | One code review prompt. | Summarize diff, inspect files, run tests, search dependencies, flag risks, verify findings, write review. | Cost per reviewed PR with actionable findings and low false positives. | | Lead enriched | One enrichment call. | Search web, parse company page, check LinkedIn or CRM, classify ICP fit, dedupe, score, write notes. | Cost per usable lead accepted by sales. | ## The action calculator Set up one row per action type. A support action and a lead action should not share a blended average. They have different loops, tools, failure modes and quality bars. | Column | Example | Why it matters | | --- | --- | --- | | Action type | Support ticket resolved | Business unit, not API unit. | | Monthly attempts | 40,000 | Raw volume before failures. | | Success rate | 86% | Turns attempts into successful actions. | | Model calls per action | 4.7 average | Captures planning, drafting, verification and retries. | | Tool calls per action | 3.2 average | Search, CRM, database, code, browser or ticketing calls. | | Uncached input tokens | 8,400 | Dynamic user data, tool results and observations. | | Cached input tokens | 18,000 | System prompt, tool schemas, policies and examples. | | Output tokens | 2,300 | Drafts, reasoning-visible output, JSON, review comments. | | Retry rate | 14% | Hidden loop tax. | | Escalation rate | 9% | How often a stronger model or human reviews the action. | | Human rework minutes | 0.8 | Converts low quality into money. | | Cost per successful action | Calculated | The board-level number. | ## Worked example: support ticket resolved Imagine a support agent that should resolve tier-1 tickets. The first demo looked cheap because it answered one ticket with one model call. Production is different: the agent classifies the issue, retrieves policy, checks the customer account, drafts an answer, asks a verifier whether the answer is grounded, and updates the ticket. Some tickets need a refund approval. Some fail schema validation and retry. | Step | Calls | Input pattern | Optimization lever | | --- | --- | --- | --- | | Classify ticket | 1 | Short ticket plus taxonomy | Small model or deterministic rules. | | Retrieve policy | 1 tool call | Search query and snippets | Better retrieval filters and shorter chunks. | | Check account | 1 tool call | Account status and order data | Return only fields needed for decision. | | Draft answer | 1 | Ticket, policy, account, tone guide | Cache static instructions and templates. | | Verify grounding | 1 | Draft plus evidence | Cheap verifier or rules before model verifier. | | Refund approval | 0.12 average | High-risk subset | Policy threshold and human approval gate. | | Retry | 0.18 average | Failed schema or low confidence | Fix tool output contracts before changing model. | Now the cost unit is not "one ticket answer." It is "one resolved ticket that passes QA without human correction." If 10,000 tickets enter the workflow, 8,900 resolve automatically, 700 escalate, and 400 fail, the model cost of all 10,000 attempts belongs in the numerator and 8,900 belongs in the denominator. ## Where prompt caching helps, and where it breaks Prompt caching is the first cost lever for agents because tool schemas, policies and instructions repeat. OpenAI says prompt caching can reduce latency by up to 80% and input token costs by up to 90%, starts automatically for prompts of 1,024 tokens or longer, and requires exact prefix matches. Anthropic prices cache reads at 0.1x base input price after a cache write, while Gemini paid tiers include context caching and batch pricing. The catch is that agent context is messy. A 2026 paper on prompt caching for long-horizon agentic tasks found 45% to 80% API cost reduction across providers when caching was structured well, but it also found that naive full-context caching can behave worse than cache-aware layouts. Another 2026 paper, TokenPilot, frames the same problem as a trade-off between pruning tokens and preserving cache continuity: mutate the prefix too often and you save tokens while destroying cache hits. The operational rule is simple: stable content first, volatile content last. Put system instructions, tool schemas, policy text and output examples at the top. Put ticket text, invoice pages, retrieved chunks, search results, code diffs and tool observations at the end. Do not let a timestamp, random request ID or reordered JSON object break the prefix. ## When batch saves money, and when it does not Batch discounts are powerful but only for actions that can wait. OpenAI's Batch API lists a 50% discount compared with synchronous APIs and completion within 24 hours. Anthropic lists a 50% discount on both input and output tokens for batch processing. Gemini paid tier lists Batch API access with 50% cost reduction. That makes batch a good fit for invoice backlogs, lead enrichment, nightly QA, eval runs, document migration and offline classification. Batch is a bad fit for live support chat, interactive PR review, real-time fraud decisions or any action where the user is waiting. In those paths, the cost lever is usually fewer loops, better caching, smaller verifier models, sharper tool contracts and routing through an [LLM gateway or router](/blog/llm-gateway-router-comparison-2026/). ## How to stop token bills from exploding 1. **Trace every action.** Log action ID, model, tool, token counts, cache hits, retry reason, latency, result status and eval verdict. 2. **Cap loops by design.** Give the agent a maximum number of tool calls and a clear fallback when confidence is low. 3. **Separate planner, worker and verifier.** The same expensive model should not do every step by default. 4. **Make tools narrow.** Return the five fields needed for the next decision, not the whole CRM object. 5. **Cache static context.** Keep prompts prefix-stable across actions. 6. **Batch offline actions.** Lead enrichment, invoice extraction and evals should not pay live prices when they can wait. 7. **Measure success, not attempts.** Failed actions must stay in the numerator. 8. **Review expensive traces weekly.** One broken tool or broad retrieval query can dominate the bill. ## Do not hide quality outside the cost model The cheapest agent is often the one that quietly creates work for people. A support ticket that needs a human to fix tone, a lead that sales rejects, an invoice that accounting corrects, or a PR review full of false positives did not save its advertised cost. It moved the cost to another team. That is why cost per action needs evals. The action is successful only when it passes the business quality bar. For a production rollout plan, see our [30/60/90-day AI agent pilot](/blog/ai-agent-pilot-30-60-90-days/). For the failure pattern behind abandoned pilots, see [why AI agent projects get cancelled](/blog/why-ai-agent-projects-get-cancelled/). Keep this operating-cost model separate from language research. The [Semaprax benchmark](/semaprax/benchmarks/) asks whether typed, bounded compiler context could reduce agent work, but it does not yet measure model tokens, accepted actions or cost. It is not evidence for a lower AI agent cost per action. ## Related calculators Use this article when the workflow is agentic and the bill grows through loops. Use [cost per token vs cost per task](/blog/cost-per-token-vs-cost-per-task/) when someone is comparing model sticker prices. Use the [LLM cost calculator](/blog/llm-cost-calculator-2026/) when you need the full spreadsheet across caching, batch, routing, self-hosting, retries and human rework. Use [local models vs APIs](/blog/local-models-vs-apis-break-even-eu-2026/) when sustained volume makes self-hosting a candidate. If your traces are dominated by large tool output, compare the [coding agent cost control with output compression](/blog/codag-cost-control/) pattern before changing only model strategy. If the buying decision is the coding harness rather than the model, our [Claude Code vs OpenCode team-cost comparison](/blog/claude-code-vs-opencode-team-cost-2026/) separates fixed prompt overhead, cache economics, subscription pricing and cost per accepted repository task. ## Sources and live-price caveat Provider pricing and agent platforms move quickly. Treat the mechanics here as stable and the numbers as a July 2026 snapshot. Re-check [OpenAI Agents SDK docs](https://developers.openai.com/api/docs/guides/agents), [OpenAI prompt caching](https://developers.openai.com/api/docs/guides/prompt-caching), [OpenAI Batch API](https://developers.openai.com/api/docs/guides/batch), [Anthropic pricing](https://platform.claude.com/docs/en/about-claude/pricing) and [Gemini pricing](https://ai.google.dev/gemini-api/docs/pricing) before budgeting. For the research background, read [ReAct](https://arxiv.org/abs/2210.03629), [Don't Break the Cache](https://arxiv.org/abs/2601.06007) and [TokenPilot](https://arxiv.org/abs/2606.17016). Getting a trace like that down to a defensible cost per action is design work: fewer steps, cheaper models on the mechanical ones, a retry budget and an escalation path that stops the loop. That is what our [AI enablement service](/services/ai-enablement/) does when the agent runs inside your team, and [AI software development in Austria](/ai-software-development-austria/) when it ships inside a product. [Twinsoft AI](/case-studies/twinsoft-ai/) shows the evaluation layer that keeps the numbers honest. ## Final thoughts Agentic workflows blow up token bills because they turn one request into a trace: plan, tool, observation, draft, verify, retry, escalate and record. The right cost unit is not one model call or one chat message. It is one successful business action. Once you measure cost per action, the optimization order becomes practical: trace every action, cap loops, narrow tools, cache stable context, batch offline work, route cheap steps to cheaper models, verify quality, and keep failed attempts in the numerator. That is the number executives can govern. ## You may also like.. [**LLM Cost Calculator 2026** The broader calculator for cost per successful task: cached input, batch, routing, retries, self-hosting and eval quality.](/blog/llm-cost-calculator-2026/) [**AI Enablement vs a generic AI consultancy** One hands you a strategy deck. The other ships a working setup on your infrastructure that your team owns.](/compare/ai-enablement-vs-generic-ai-consultancy/) ## Primary sources used in this guide Use the tracing and risk guidance as the stable method, then refresh provider prices for the actual model mix. - [OpenAI agents guide](https://developers.openai.com/api/docs/guides/agents) - [OpenAI API pricing](https://openai.com/api/pricing/) - [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) Agent engineering ## Continue through this cluster Coding agents, MCP, context systems, evaluation and the controls required for dependable automation. [Start with the cornerstone**Graph Engineering for AI Agents: When Does a Knowledge Graph Pay Off?**](/blog/graph-engineering-ai-agents/) - [Graft Review 2026: Do Agent Repo Maps Belong in Git?](/blog/graft-review-agent-repo-map/) - [How Coding Agents Keep Token Bills in Check with Output Compression](/blog/codag-cost-control/) - [Smarter Token Usage with Your AI Coding Agent](/blog/smarter-token-usage-with-your-ai-coding-agent/) - [DeepSeek Harness Review: Is the Plugin Stack Production-Ready?](/blog/deepseek-harness-enterprise-review/) - [OpenSandbox Review: Is Self-Hosting Worth It?](/blog/opensandbox-ai-agent-sandbox-review/) Inbox, without the noise ## Follow the work that matters to you Get a short email when we publish something new. Follow the whole blog or only the problems you care about. [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 09 Jul 2026 Last reviewed August 7, 2026 [**Next**](/blog/local-models-vs-apis-break-even-eu-2026/) New posts by email × × Get new posts by email A short email when we publish. Free, no tracking. ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "founder": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "legalRepresentative": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "name": "Wavect GmbH", "subjectOf": { "@id": "https://wavect.io/verified-claims.json#dataset", "@type": "Dataset", "creator": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.", "inLanguage": "en", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "name": "Wavect verified publication claims", "url": "https://wavect.io/verified-claims.json" }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "sameAs": [ "https://www.wikidata.org/wiki/Q139796367", "https://www.linkedin.com/in/jocr77/", "https://github.com/jo-chris" ], "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/#website", "@type": "WebSite", "inLanguage": [ "en", "de", "es", "zh" ], "name": "Wavect", "potentialAction": { "@type": "SearchAction", "query-input": "required name=search_term_string", "target": { "@type": "EntryPoint", "urlTemplate": "https://wavect.io/search/?q={search_term_string}" } }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/ai-agent-cost-per-action-2026/#webpage", "@type": "WebPage", "dateModified": "2026-07-09", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-07-09", "url": "https://wavect.io/blog/ai-agent-cost-per-action-2026/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "AI agent cost per action is the all-in cost of one successful business outcome, not one model call. Include failed attempts, tool calls, retries, approvals, escalation, and human rework in the numerator. Measure resolved tickets, extracted invoices, reviewed pull requests, or enriched leads. Reduce cost by tracing every action, capping loops, narrowing tools, caching stable context, routing simple steps to cheaper models, and validating quality with evals. Provider prices are a July 2026 snapshot, so re-check them before budgeting.", "articleBody": " Blog overview/AI and agents/Agent engineering AI Agent Cost per Action: Why Agentic Workflows Blow Up Token Bills TL;DR AI agent cost per action is the all-in cost of one successful business outcome, not one model call. Include failed attempts, tool calls, retries, approvals, escalation, and human rework in the numerator. Measure resolved tickets, extracted invoices, reviewed pull requests, or enriched leads. Reduce cost by tracing every action, capping loops, narrowing tools, caching stable context, routing simple steps to cheaper models, and validating quality with evals. Provider prices are a July 2026 snapshot, so re-check them before budgeting. Related service: AI Enablement Executives do not buy tokens. They buy actions: a support ticket resolved, an invoice extracted, a pull request reviewed, a lead enriched, a refund approved, a claim triaged. That is the unit your AI agent cost model should use. Cost per action is the all-in cost to complete one business action to your quality bar, including every model call, tool call, retry, verifier, escalation, cached token, uncached token, and human correction. This is narrower than our LLM cost calculator. That article explains cost per successful task across LLM systems. This one focuses on agentic workflows, where the bill grows because the agent loops: it plans, calls tools, reads tool output, updates context, asks another model, verifies, retries, and sometimes hands work to another specialist. OpenAI's Agents SDK docs describe agents as applications that plan, call tools, collaborate across specialists, and keep state, and its runner can perform the tool loop, switch agents after handoffs, and trace model calls, tools, agents, guardrails and handoffs. Each one of those surfaces can become a cost line. What is AI agent cost per action? AI agent cost per action is the total cost of one completed business outcome, not one model call. It includes input tokens, cached input, output tokens, tool schemas, retrieved context, tool observations, retries, verifier calls, escalations, human review, and failed attempts divided by successful actions. The formula is: Cost per action = (sum of all model-call costs + tool and retrieval costs + retry costs + verifier costs + human rework) / successful actions. For agentic systems, the denominator matters as much as the numerator. If an agent attempts 10,000 lead enrichments but only 8,700 pass validation, the cost per action uses 8,700. Failed actions are not free. They consumed tokens, tools, time, and trust. Why agents multiply tokens A single LLM call is a line item. An agent run is a trace. The ReAct paper made the pattern famous: language models can interleave reasoning traces and actions so they can gather information from external environments before continuing. That is useful, but it changes the economics. Every loop carries more history and more observations. Cost multiplierWhat happensWhy the bill grows PlanningThe agent asks what to do next.Planner prompts and reasoning output become extra calls before work begins. Tool schemasAvailable functions, MCP tools, permissions and examples are included in context.Static tool descriptions can be large, and they repeat unless caching works. Tool observationsSearch results, CRM records, invoices, diffs or logs are fed back into the model.Every observation becomes new input on the next step. RetriesSchema failures, timeouts, low confidence or blocked tools trigger another pass.The second attempt often carries the full first attempt as context. VerificationA separate model or rule layer checks grounding, policy, extraction quality or code risk.Quality control is necessary, but it is another cost line. HandoffsA general agent delegates to a specialist.Context is copied, summarized or re-expanded for the next agent. Human reviewBorderline actions pause for approval.Approval can be the right choice, but the handoff and audit trail are not free. Four examples executives recognize The trap is to budget for \"one agent request.\" A real budget starts with the action. Business actionNaive assumptionTypical agent traceCost metric Support ticket resolvedOne answer.Classify, retrieve policy, inspect account, draft, verify, maybe ask approval, update CRM.Cost per resolved ticket that passes QA. Invoice extractedOne OCR plus JSON output.Read file, classify layout, extract fields, call vendor lookup, validate totals, retry missing fields.Cost per invoice posted without manual correction. PR reviewedOne code review prompt.Summarize diff, inspect files, run tests, search dependencies, flag risks, verify findings, write review.Cost per reviewed PR with actionable findings and low false positives. Lead enrichedOne enrichment call.Search web, parse company page, check LinkedIn or CRM, classify ICP fit, dedupe, score, write notes.Cost per usable lead accepted by sales. The action calculator Set up one row per action type. A support action and a lead action should not share a blended average.", "articleSection": "Engineering", "author": { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/" }, "citation": [ { "@type": "WebPage", "name": "OpenAI agents guide", "url": "https://developers.openai.com/api/docs/guides/agents" }, { "@type": "WebPage", "name": "OpenAI API pricing", "url": "https://openai.com/api/pricing/" }, { "@type": "WebPage", "name": "NIST AI Risk Management Framework", "url": "https://www.nist.gov/itl/ai-risk-management-framework" } ], "dateModified": "2026-08-07", "datePublished": "2026-07-09", "description": "AI agent cost per action is the all-in cost of one successful business outcome, not one model call. 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