{"slug": "the-receipt-i-priced-every-token-of-one-ai-agent-task", "title": "The Receipt: I Priced Every Token of One AI Agent Task", "summary": "Karmendra Pandey, a Practice Architect in AI & ML at TEK Systems, published a line-by-line cost breakdown of a single production-style AI agent task running on AWS Bedrock, showing roughly $0.051 in raw inference cost versus $0.23 fully loaded with eval harness, retrieval infrastructure and human review. The receipt for a mid-complexity support-triage agent shows a failed tone-eval retry ($0.0121) costing more than the original reasoning steps combined, and knowledge-base retrieval accounting for 43% of input tokens. Pandey argues teams should itemize agent spend rather than rely on aggregate monthly dashboards.", "body_md": "Everyone talks about AI costs in aggregate. Dashboards show monthly spend, per-model totals, team budgets. Nobody shows you a receipt.\n\nSo here's one. A single AI agent task, priced line by line — every token, every tool call, every retry. This is a worked example from production-style workloads I describe in my reference architecture work (full paper: [https://doi.org/10.5281/zenodo.23178788](https://doi.org/10.5281/zenodo.23178788)). The numbers are representative of a mid-complexity support-triage agent running on AWS Bedrock. Your numbers will differ. The shape of the receipt won't.\n\nA customer support triage agent: read an incoming ticket, pull the customer's history, check the knowledge base, decide whether to auto-resolve or escalate, and draft the response. One task, one receipt.\n\n| # | Line item | Tokens in | Tokens out | Unit cost basis | Cost | \n|---|---|---|---|---|---|\n| 1 | System prompt (loaded once, cached) | 1,850 | — | cached input | $0.0011 | \n| 2 | Ticket text + customer history | 2,300 | — | standard input | $0.0069 | \n| 3 | Knowledge base retrieval (3 chunks) | 4,100 | — | standard input | $0.0123 | \n| 4 | Reasoning: triage decision | — | 420 | standard output | $0.0050 | \n| 5 | Tool call: CRM lookup | 180 | 90 | in/out | $0.0016 | \n| 6 | Reasoning: draft response | — | 680 | standard output | $0.0082 | \n| 7 | Retry: first draft failed tone eval, regenerated | 6,200 | 710 | in (cached) + out | $0.0121 | \n| 8 | Embedding: ticket for memory store | 340 | — | embedding | $0.0000 | \n| 9 | Final response + audit log write | — | 350 | standard output | $0.0042 | \n|  | **Total** | **~15,000** | **~2,250** |  | **~$0.051** | \n\nWait — $0.051, not $0.23? The $0.23 figure from my paper is the fully-loaded cost: it includes the amortized cost of the eval harness, the retrieval infrastructure, and the human review queue time for escalations. The raw inference receipt is five cents. The *governed* cost is twenty-three cents. Both numbers matter, and most teams track neither.\n\nThree things jump out:\n\n**1. The retry cost more than the original attempt.** Line 7 — a failed tone evaluation forced a regeneration — cost $0.0121, more than lines 4+6 combined. Retries are the silent budget killer in agent systems. Every eval gate you add has a cost; every gate you skip has a bigger one. The receipt lets you price the tradeoff instead of guessing.\n\n**2. Context is the bulk of input spend.** Lines 2+3 are 6,400 input tokens — 43% of the total input. That knowledge-base retrieval is doing real work, but it's also the first place to optimize: better chunking, semantic caching of frequent queries, and prompt compression all attack this line directly.\n\n**3. The system prompt is nearly free — because it's cached.** Line 1 costs a tenth of a cent thanks to prompt caching. Without caching, that 1,850-token system prompt gets re-priced on every call in the chain. If your provider supports caching and you're not using it, you're donating money.\n\nYou can't govern what you can't itemize. Monthly dashboards tell you *that* you spent $40K. The receipt tells you *why* — which line items to attack, which eval gates earn their keep, which retries are worth preventing.\n\nThree practices that fall out of this:\n\nThe next time someone asks what your AI agents cost, don't open the dashboard. Show them a receipt.\n\n*Karmendra Pandey is a Practice Architect in AI & ML at TEK Systems, building production agentic AI on AWS. His reference architecture for cost-governed AI agents is at [https://doi.org/10.5281/zenodo.23178788](https://doi.org/10.5281/zenodo.23178788), and the companion implementation at [https://github.com/karmendra8386/agentic-ai-aws-reference](https://github.com/karmendra8386/agentic-ai-aws-reference).*", "url": "https://wpnews.pro/news/the-receipt-i-priced-every-token-of-one-ai-agent-task", "canonical_source": "https://dev.to/karmendra_pandey_43ac6983/the-receipt-i-priced-every-token-of-one-ai-agent-task-4ibp", "published_at": "2026-10-06 14:16:57+00:00", "updated_at": "2026-10-06 14:18:27.825353+00:00", "lang": "en", "topics": ["ai-agents", "ai-infrastructure", "mlops", "large-language-models"], "entities": ["Karmendra Pandey", "TEK Systems", "AWS Bedrock", "AWS"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/the-receipt-i-priced-every-token-of-one-ai-agent-task", "markdown": "https://wpnews.pro/news/the-receipt-i-priced-every-token-of-one-ai-agent-task.md", "text": "https://wpnews.pro/news/the-receipt-i-priced-every-token-of-one-ai-agent-task.txt", "jsonld": "https://wpnews.pro/news/the-receipt-i-priced-every-token-of-one-ai-agent-task.jsonld"}}