{"uid":"cap_oAAzUhkZ99023JJ32UCV_","slug":"verity-suite-compass-3a7bad32","name":"Verity Suite Compass","description":"The trust fabric for AI agents — calibrated, fail-closed services agents pay per call.","url":"https://suite.veritylayer.dev/compass","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"options":{"type":"array","items":{"type":"string"},"title":"Options","description":"the candidate choices to pick among; one option per item, stated verbatim as you want it returned"},"evidence":{"anyOf":[{"type":"string","maxLength":2000},{"type":"null"}],"title":"Evidence","default":null,"description":"facts known about the options (specs, prices, availability, data); for any current or external fact, only what is stated here may be used"},"constraints":{"type":"string","title":"Constraints","maxLength":2000,"minLength":1,"description":"the goal plus hard requirements (must-meet gates), soft preferences (weigh, don't gate), and any priorities or tradeoffs between them"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.06","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.06/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.06","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.06","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_iSUMnpupO5oMOwTaUnUD3","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.06","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Evaluates a set of candidate options against stated constraints and evidence, returning a structured verdict (recommend, tentative, gather_more_info, or no_viable_option) with confidence score and reasons.","exampleAgentPrompt":"I need to pick between three cloud storage plans — Plan A at $9/mo with 1TB and no versioning, Plan B at $15/mo with 2TB and 30-day versioning, Plan C at $20/mo with 5TB and unlimited versioning — my constraints are: must have versioning, prefer under $18/mo, storage at least 1TB. Which one should I go with and how confident are you?","exampleUseCases":[{"title":"Vendor selection for procurement agent","prompt":"I'm deciding between four software vendors for our CRM: Salesforce, HubSpot, Zoho, and Pipedrive. My constraints are: must support SSO, must have an API, budget under $50/user/month, and I prefer strong mobile apps. Here's what I know about each: Salesforce supports SSO, API, costs $75/user, good mobile; HubSpot SSO available on Enterprise only at $60/user, API yes, mobile good; Zoho SSO yes, API yes, $20/user, mobile average; Pipedrive SSO yes, API yes, $35/user, strong mobile. Which vendor should we go with?"},{"title":"Routing decision for autonomous workflow","prompt":"My agent needs to decide which fulfillment center to use for an order — options are Center A (2-day shipping, $4.50 fulfillment cost, 97% on-time rate), Center B (1-day shipping, $7.00, 99% on-time), Center C (3-day shipping, $2.80, 93% on-time). Constraints: customer paid for standard delivery so max 3 days is fine, minimize cost, but on-time rate must be above 94%. Which center should it pick?"},{"title":"Model selection in an AI pipeline","prompt":"I'm choosing between three LLMs for a summarization task in my pipeline — GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. My constraints are: output quality must be high for legal text, latency under 3 seconds on average, cost under $0.01 per 1k tokens preferred. Evidence: GPT-4o scores 92% on legal benchmarks, avg 1.8s, $0.005/1k; Claude 3.5 Sonnet scores 94%, avg 2.1s, $0.003/1k; Gemini 1.5 Pro scores 87%, avg 1.4s, $0.002/1k. Which model should I use?"}],"resultDescription":"Returns a structured verdict object with: a verdict field (recommend, tentative, gather_more_info, or no_viable_option), the chosen option copied verbatim from the input list (when a pick is made), a confidence score, an array of concrete reasons grounded in the constraints and evidence (including the main tradeoff against the runner-up), and a payment receipt object.","failureModes":["Empty or missing options array — request rejected at validation","Constraints field blank or too short — fails minLength:1 validation","Evidence references external/current facts not provided in the evidence field — service may return gather_more_info verdict instead of a pick","Options list contains ambiguous or duplicate entries — may produce lower confidence or tentative verdict","Body not sent as JSON with correct bodyType — request rejected","Payment not included or insufficient — x402 payment required error","Constraints or evidence exceed 2000 character limits — request rejected"],"whenToPreferThis":"Choose Compass when an agent needs a structured, auditable, calibrated pick from a finite set of known options — especially when confidence scoring and explicit reasoning are required. Prefer it over raw LLM reasoning when you need fail-closed behavior (it won't hallucinate external facts beyond supplied evidence), a machine-readable verdict enum, or a cryptographic payment receipt for audit trails. Not suited for open-ended generation, ranking large unstructured datasets, or real-time web lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:41:08.677Z","isFirstParty":false}