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a decision procedure for detecting model substitution (knockoff/swap) during agent LLM spend, with evidence collection and pay/hold/dispute guidance.","exampleAgentPrompt":"Before I release payment for this LLM inference session, run the model-identity verification procedure — I want to know if the model responding matches what I was billed for, what evidence to collect (logprobs, latency fingerprints, output hashes), and whether I should pay, hold, or dispute.","exampleUseCases":[{"title":"Pre-payment model authenticity check","prompt":"I'm about to pay an LLM provider for a batch of inference calls but I'm not sure the model they served matches the one they're charging me for — give me the step-by-step procedure to verify model identity and tell me whether to pay or hold the funds."},{"title":"Dispute evidence collection after suspected swap","prompt":"I think my LLM provider swapped in a cheaper knockoff model mid-session — walk me through what verifiable evidence I need to collect (pricing mismatch, logprobs, watermarks, prompt hashes) so I can build a dispute case."},{"title":"Autonomous agent spend guard before LLM invoice settlement","prompt":"My agent just completed a long LLM job and is about to settle the invoice — run the model-identity verification decision procedure so it knows whether the responding model matches the contract and whether to pay, hold, or flag for dispute."}],"resultDescription":"A structured brief containing a blunt decision procedure for model-identity verification: evidence to collect (pricing mismatch, logprobs, latency fingerprints, watermarks, prompt/output hashes), a verdict on whether the model matches what was billed, and an explicit pay/hold/dispute recommendation. Includes service slug, generation timestamp, and generation source (llm/reasoning/deterministic).","failureModes":["Missing or invalid input object returns schema validation error","Payment not received results in 402 response requiring x402 micropayment","Topic override not recognized may fall back to default procedure","LLM generation source may produce non-deterministic procedure text across calls","Endpoint unavailability at wrong.systems returns 5xx"],"whenToPreferThis":"Choose this endpoint when an AI agent needs a concrete, actionable decision procedure for verifying that the LLM model it paid for is the one that actually responded — especially when pricing anomalies, latency inconsistencies, or output quality drops suggest a knockoff swap. Prefer over general fraud guides when you need specific evidence types (logprobs, watermarks, hashes) and a binary pay/hold/dispute outcome, not marketing prose.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:59:48.473Z","isFirstParty":false}