An AI agent can understand an API operation, call an MCP tool, and respond to an HTTP 402 payment requirement—and still be unable to decide whether it should buy the service.
The missing information is often buried in prose. Is the quoted amount per call or per accepted result? Does a timeout qualify for a refund? May the output be cached, redistributed, or used for model training? Does a valid JSON response count as delivery even when the data is stale?
A human can open a Terms page, interpret the wording, and ask for clarification. A machine needs explicit fields, units, identifiers, conditions, versions, and decision outcomes. Copying legal prose into JSON does not solve that problem; it changes the container without making the meaning executable.
The practical design rule is: machine-readable service terms should be a versioned, issuer-bound policy bundle, and an agent should automate a purchase only when every material term maps to a rule it supports. Unknown or contradictory terms should stop the flow or request approval—not invite the model to guess.
Four Records Hide Behind One “Terms” Label
Many implementations treat “the terms” as one document. That creates temporal confusion because some terms are durable, some are request-specific, and some exist only after delivery.
Separate at least four records:
Record | Question it answers | Typical lifetime | What must be bound |
|---|---|---|---|
Service policy | What are the provider's default pricing, rights, delivery, and remedy rules? | Days or months | Issuer, scope, version, effective period, policy digest |
Live quote | What will this request cost under the current conditions? | Seconds or minutes | Resource, billable event, amount or ceiling, asset, payee, expiry, policy reference |
Accepted order snapshot | Which exact terms did buyer and seller accept? | Immutable for the order | Policy hash, quote hash, buyer authorization, seller commitment, input and acceptance references |
Delivery and remedy record | What happened, and which remedy is now available? | Order retention period | Result digest, timestamps, validation outcome, acceptance decision, refund/replacement state |
The separation matters even for a two-cent API call. A seller may update its general price tomorrow, but that must not silently change yesterday's accepted order. A quote may expire in five minutes without invalidating the underlying service policy. A refund rule may exist before purchase, while refund eligibility depends on a delivery event that occurs later.
Do not fetch a mutable Terms page during a dispute and assume it represents what the agent accepted. Store the version and digest at the point of authorization.
Interface Schemas Describe Calls, Not Commercial Permission
Several standards already make services easier for software to discover and call. None of the reviewed core specifications combines the entire commercial policy.
As of OpenAPI Specification 3.2.0, the termsOfService field in the Info Object is a URI. It points a client to terms; it does not structure their refund conditions, usage license, or delivery test. An OpenAPI document can describe operations, parameters, security, and response schemas while leaving the purchase decision to another layer.
The current MCP schema gives a tool a name, description, required inputSchema, optional outputSchema, and annotations. That makes invocation and structural validation more predictable. MCP also treats tool annotations as hints and warns clients not to rely on them when the server is untrusted. A tool declaring itself read-only or idempotent is not the same as an independently verified commercial promise.
JSON Schema can require fields, types, formats, enums, and ranges. It can reject a string where refundWindowSeconds requires an integer. It cannot, by itself, make two providers agree on when that clock starts, what evidence stops it, or whether “refund” means an onchain return, account credit, or manual review. Interoperability requires both a schema and a shared vocabulary.
x402 addresses another part of the stack. It can communicate a payment requirement at the HTTP layer and carry payment authorization and settlement feedback. Those messages answer how the client may pay. They do not automatically answer what the client may do with the output or what service failure entitles it to receive.
The result is composition, not replacement: interface metadata describes the call, service terms describe permission and obligation, a quote describes the current charge, payment infrastructure moves value, and a delivery contract evaluates the result.
Build a Terms Manifest With Typed Semantics
A terms manifest should be smaller than the legal document but more precise about the decisions software must make. The following object is illustrative reference architecture, not an adopted industry standard. Digests are shortened for readability.
The top level performs six jobs:
Vocabulary selection:
schemaVersiontells the client which fields and semantics it must support.Identity:
termsId,version, andissueridentify the policy and the party claiming to issue it.Scope:
appliesToprevents a policy for one endpoint or customer class from being reused for another.Time: effective and expiry timestamps make stale-policy handling deterministic.
Legal binding: the legal-text version and digest connect the operational manifest to the human-readable agreement without asking the agent to reinterpret that prose on every call.
Integrity and conflicts: a digest, signature format, runtime precedence, and legal-conflict action define how records are verified and what happens when they disagree.
References such as pricing:enrichment:v4 should resolve to immutable, typed policy objects. A production design may embed them in one signed bundle to avoid reference substitution. If external references are allowed, the client needs size limits, approved schemes and origins, depth limits, hash checks, and cycle detection.
Price Needs a Billable Event, Formula, and Ceiling
price: 0.02 is not a complete machine-readable price. The agent still needs to know 0.02 of what, for which event, at which quantity, under which rounding rule, and whether it is a charge or merely an authorization maximum.
A pricing policy should define:
billable event: request received, tool execution, token consumed, task completed, result delivered, result accepted, or outcome achieved;
unit and quantity: calls, input tokens, output tokens, records, seconds, megabytes, or another versioned measure;
rate formula: fixed amount, unit rate, tiered schedule, or request-bound formula identifier;
denomination: fiat currency or settlement asset, network, token identifier, and decimal precision;
minimum and maximum: minimum charge, request ceiling, daily ceiling, or total order ceiling;
metering source: which system measures usage and which record the buyer can inspect;
rounding: direction, precision, and when aggregation occurs;
additional charges: tax treatment, network fees, conversion costs, and whether each is included or estimated;
validity: quote issue time, expiry, and the policy version used to calculate it;
final-charge evidence: meter reading, rate version, calculation trace, and settled amount.
Consider a token-priced inference service. “$0.000002 per token” remains ambiguous if input and output tokens have different rates, cached input receives a discount, failed calls consume tokens, or the meter rounds each request to the nearest thousand. The terms must expose those distinctions before an agent can compare the service with a fixed-price alternative.
Variable pricing also requires two different values: the authorization ceiling and the final charge. An agent may permit a service to charge up to $0.05, but that does not mean the service earned $0.05. The settlement record should contain the measured quantity and applied rate. The buyer should reject a final amount that exceeds the authorized formula even when it remains below the numerical ceiling.
Refund Rules Need Triggers, Evidence, and a Remedy Order
A boolean such as refundable: true is almost useless to an agent. It does not say which failures qualify, how long the claim window remains open, what evidence is required, or whether the first remedy is a retry rather than cash repayment.
An executable refund policy needs at least:
a named eligibility event, such as
NOT_DELIVERED_BY_DEADLINE,PROVIDER_5XX, orOUTPUT_SCHEMA_INVALID;the state prerequisites, including whether payment was authorized, settled, or only reserved;
the clock source and claim deadline;
acceptable evidence, such as request ID, payment ID, response status, result digest, or validation report;
exclusions, including caller cancellation, invalid input, unsupported jurisdiction, or buyer-caused timeout;
a remedy order: retry, replacement, proportional refund, full refund, credit, or manual dispute;
the amount formula and destination;
expected processing states and time limits;
an escalation path when evidence conflicts.
A deterministic rule may read:
That rule still does not promise that funds return instantly. Eligibility, approval, execution, and settlement are separate states. A crypto transfer may be irreversible at the original payment layer, requiring a new transfer from the merchant. Another provider may issue a platform credit. A regulated or high-value service may require manual review even when the machine can assemble the evidence automatically.
This distinction prevents two opposite errors: assuming every failed call deserves a refund, and assuming an irreversible payment means no remedy can exist.
Usage Rights Need Permissions, Prohibitions, Duties, and Scope
When an agent buys data, generated media, research, or model output, access does not automatically imply ownership or unrestricted reuse.
The W3C ODRL Information Model offers a useful foundation. It represents a Policy through Permissions, Prohibitions, and Duties involving Assets, Parties, Actions, and Constraints. That vocabulary is more precise than a list such as allowedUses: ["business"], which leaves “business” undefined.
For an agent-facing service, a usage rule should identify:
asset: the raw input, returned dataset, generated artifact, model output, or derived work;
party: buyer, end user, affiliate, subcontractor, or public audience;
action: consume, display, cache, copy, transform, redistribute, train, fine-tune, publish, or delete;
purpose: internal analysis, customer support, advertising, research, model improvement, or resale;
constraint: retention period, territory, audience, quantity, environment, model, or expiry;
duty: attribution, deletion, usage reporting, confidentiality, or downstream notice;
remedy or consequence: what follows from a violated duty;
conflict strategy: whether a conflict invalidates the policy or one class of rule takes priority.
Use established identifiers where they fit. A software component can reference an SPDX license expression instead of a loosely spelled license name. A data or generated-content product may require an ODRL profile or another documented service vocabulary because software-license identifiers do not capture retention, training, redistribution, or attribution conditions for every asset.
The buyer must publish its supported actions and profiles. If the seller introduces action: synthetic-replication and the buyer has no definition for it, the agent should return TERMS_UNSUPPORTED. Asking a language model to infer that the phrase probably means “training is allowed” turns legal ambiguity into an automated permission grant.
Machine-readable rights are an operational representation of agreed policy. They do not remove the need for legal text, jurisdiction-specific review, or a process for terms that cannot safely be reduced to deterministic rules.
Delivery Rules Need an Acceptance Contract
Delivery is not one boolean. A provider can produce bytes, return them to the client, satisfy a response schema, and still fail the purchase.
A delivery policy should specify:
response format and schema version;
delivery channel and correlation ID;
deadline and the event that starts the clock;
data freshness or observation window;
required fields and completeness threshold;
semantic acceptance tests appropriate to the service;
artifact digest and timestamp evidence;
retry and replacement rules;
whether partial results are billable;
who may mark an order accepted or rejected;
the acceptance window and default outcome if no decision arrives.
MCP's optional outputSchema and structured result can help a client validate shape. They cannot prove that a research answer is accurate, an image meets the requested brand constraints, or a compliance result used the required data vintage. The same limitation applies to an ordinary HTTP 200 response.
The x402 Signed Offers & Receipts extension adds useful proof-of-interaction. Its documented signed offer binds the resource, payment scheme, network, amount, payee, and validity. Its receipt binds the resource, payer, network, issue time, and optionally a transaction hash. Those artifacts can support an evidence chain, but the receipt does not, by its documented fields alone, commit to a response-body digest or prove that an acceptance test passed.
Use explicit service states:
The accepted order should name the acceptance-test version. Otherwise the buyer can tighten its test after delivery, or the seller can weaken the definition of success after payment.
Versioning and Precedence Stop Terms From Moving After Payment
Machine-readable service terms create a new failure mode: software may read a perfectly structured policy at the wrong time.
Every durable policy needs an immutable version or content digest. Every quote should name the policy version from which its defaults came. Every accepted order should preserve the exact quote and terms hashes. A response or receipt should carry the order identifier so the outcome can be joined to that snapshot.
One safe operational precedence pattern is:
accepted order snapshot for fields fixed by this transaction;
request-bound quote for dynamic commercial fields;
durable service policy for defaults not overridden by the quote.
Human-readable legal terms remain linked by version and digest. If the operational manifest and legal text conflict, the client should not silently choose whichever is cheaper or easier to parse. The manifest should specify a legal-conflict action, usually rejection or human review.
Change or conflict | Safe machine action |
|---|---|
Price changes before quote acceptance | Expire the quote and request a new one |
Service policy changes after order acceptance | Preserve the accepted snapshot unless an explicit update process applies |
Legal-text digest no longer matches | Stop authorization and re-fetch or escalate |
Quote omits a material field required by policy | Reject as incomplete |
Quote and service policy disagree without a precedence rule | Return |
Client does not support the declared vocabulary version | Return |
Signature verifies but issuer is not authorized for the service | Return |
Signatures are necessary for integrity but not sufficient for trust. A valid signature proves that a key issued a record. The client must still establish that the key belongs to the service, is valid for this purpose and time, and is appropriately bound to the endpoint and payment recipient.
Unsupported Terms Must Produce a Decision State
An agent should not turn raw terms directly into a payment call. It needs a deterministic evaluation pipeline:
The result should be a compact decision object, not hidden chain-of-thought:
Useful terminal states include:
ACCEPT: every material term is supported and passes local policy;REJECT: a known term violates a hard rule;REQUIRE_APPROVAL: the terms are understood but exceed delegated authority;TERMS_UNSUPPORTED: a material vocabulary, action, unit, or remedy is unknown;TERMS_CONFLICT: two applicable records disagree without a valid resolution rule;TERMS_EXPIRED: the policy or quote is outside its validity window;ISSUER_UNVERIFIED: integrity may be valid, but the issuer-to-service binding is not.
Seller terms and buyer policy perform different jobs. The seller may permit model training, but the buyer's principal may prohibit it. Permission from the seller does not create authority for the agent. The effective action is the intersection of seller permission, buyer policy, applicable law, and the agent's delegated mandate.
An LLM can summarize a validated decision for a person. It should not invent the meaning of an unsupported material term so that the transaction can continue.
Walkthrough: A Data Service With Executable Terms
Consider a hypothetical company-enrichment endpoint. The agent needs one result for internal risk analysis and has a maximum spend of $0.05.
The service policy states:
billing unit: one request;
base price: $0.02, settled in a supported stablecoin at the live quote;
input use: processing only, no model training, deletion within 24 hours;
output use: internal analysis, no public redistribution, retention up to 30 days;
delivery: schema
company-enrichment:v3within 30 seconds, source observation no older than 60 minutes;remedy: one no-charge replacement for schema failure or stale data;
refund: full payment if no result arrives within 45 seconds, subject to a one-hour claim window;
correctness disputes: manual review because semantic accuracy cannot be decided by schema alone.
The purchase flow can now be deterministic:
The agent fetches the service policy, verifies its version, digest, issuer binding, scope, and linked legal-text digest.
Its parser confirms that every pricing, rights, delivery, and remedy term uses a supported vocabulary.
The buyer policy accepts the price, prohibits training as required, permits 30-day internal retention, and recognizes the refund process.
The endpoint returns a live quote for $0.02 with a five-minute expiry and references the service-policy hash.
The agent compares the quote with the policy, confirms the payment recipient, creates an accepted order snapshot, and records its authorization decision.
The x402 payment flow handles the payment requirement and settlement record. The application joins those records to the order ID and terms hash.
The service returns a structured result. The client validates schema, response time, source timestamp, and artifact digest.
Suppose the result is structurally valid but its source timestamp is two hours old. The delivery policy classifies that as stale and grants one replacement. The client does not call it a refund immediately. If the replacement satisfies the test, the order becomes accepted. If the replacement also fails and the policy makes that condition refund-eligible, the agent can submit the linked evidence and track the remedy state.
The manifest did not make the service trustworthy. It made the provider's promise testable and gave the buyer a deterministic way to decide before and after payment.
Keep the Terms Layer Independent From Wallet and Payment Infrastructure
The terms engine should compose with identity, execution, payment, and merchant systems rather than hide inside one wallet adapter.
GOAT Network provides one example of that composable boundary. Current AgentKit documentation maps ERC-8004 to registration, service metadata, payout-wallet information, feedback, and reputation. Those records can help an agent find a service and verify which registered identity is presenting its terms. They do not make the published terms true or current by themselves.
AgentKit's documented Policy Engine checks network allowlists, action support, write permissions, risk level, and confirmation. Its execution runtime adds output validation, idempotency, retries, timeouts, metrics, and hooks. A service-terms evaluator can feed a decision into that control path, but the current documented policy checks should not be described as the complete pricing, licensing, refund, or monetary-budget model proposed here.
GOAT Flow's AgentKit integration separates five payer-side payment actions from thirty merchant-side operations covering areas such as authentication, orders, balances, webhooks, API keys, and audit logs. That split is useful after the accepted terms snapshot exists: payer records can track authorization and payment state, while merchant records can track the order and operational callbacks.
The responsibility map remains explicit:
Capability | System responsibility | Possible GOAT component | Application-owned gap |
|---|---|---|---|
Service identity and metadata | Identify issuer and discover service records | AgentKit ERC-8004 integration | Verify current terms and endpoint binding |
Terms interpretation | Parse vocabularies and evaluate commercial policy | No universal component claimed | Terms schema, parser, precedence, local policy, decision states |
Execution control | Gate and run supported actions safely | AgentKit Policy Engine and Execution Runtime | Translate terms decision into project-specific authorization |
Payment and order operations | Authorize payment and track merchant state | GOAT Flow payer and merchant plugins | Bind order, quote, and terms hashes to business records |
Delivery and remedy | Validate result and resolve failure | Runtime validation and merchant events can contribute records | Acceptance tests, refund eligibility, replacement, dispute logic |
This is the useful GOAT connection: not a claim that one SDK has solved legal semantics, but a way to place a project-owned terms layer beside documented identity, policy-execution, payment, and merchant capabilities.
A Valid Manifest Can Still Be Dangerous
Machine readability removes ambiguity only when the vocabulary and trust model are designed carefully. It can also make hostile terms faster to execute.
Review these failure paths:
Signed but abusive terms: issuer verification proves origin, not fairness, legality, or acceptable risk.
Stale cache: an agent evaluates an old policy while paying a new quote.
Signer/payee mismatch: the terms issuer is valid, but the payment destination is not bound to that service.
Semantic downgrade: a provider advertises schema version 2 but serves version 1 after the client has already cached broad permissions.
Unit confusion: one side treats an amount as whole tokens while the other uses the smallest denomination.
Omitted rights: the parser treats a missing redistribution rule as permission instead of unknown or prohibited.
Mutable references: an embedded policy ID resolves to different content after acceptance.
Legal drift: the manifest digest points to legal text that has changed or disappeared.
Untrusted annotations: a self-described “non-destructive” or “refundable” label bypasses verification.
Parser abuse: cyclic references, excessive nesting, huge documents, or unexpected remote fetches exhaust the evaluator or reach internal resources.
Treat every manifest as untrusted input. Canonicalize before verifying signatures, reject duplicate or ambiguous keys, constrain document size and recursion, restrict reference resolution, validate units and decimal ranges, and require explicit defaults. For material rights, absence should not mean permission.
There is also a real tradeoff. A richer vocabulary allows more precise automation but increases implementation and conformance cost. Start with a narrow profile that covers the services the agent actually buys. Expand only with versioned semantics and test vectors.
Production Checklist: Make Every Material Term Testable
Define the decision scope. List the commercial questions the agent must answer before it can authorize payment.
Publish a schema and vocabulary. Give each field, enum, unit, clock, and action one normative meaning.
Separate the four records. Do not merge durable policy, live quote, accepted snapshot, and outcome into one mutable object.
Use shared identifiers where possible. Reference established assets, networks, currencies, license expressions, and policy vocabularies rather than free text.
Bind identity and integrity. Version, hash, and sign policies; verify that the issuer is authorized for the service and payment destination.
Define explicit precedence. State which record overrides a default and what happens when legal and operational representations disagree.
Fail closed on material unknowns. Unknown rights, units, billable events, and remedies should not be guessed.
Intersect seller terms with buyer authority. A provider's permission cannot override the principal's budget, risk, data, or approval policy.
Snapshot before signing. Store policy hash, quote hash, input reference, acceptance-test version, and authorization with the order.
Keep payment and acceptance separate. Settlement admits the service request; it does not prove acceptable delivery.
Model remedies as states. Track eligibility, approval, execution, and settlement independently.
Ship conformance tests. Test stale versions, missing fields, unknown enums, contradictory records, unit boundaries, signature rotation, duplicate requests, delivery failure, and policy updates during checkout.
The highest-value test is not whether the happy-path JSON parses. It is whether two independent implementations reach the same stop, approval, or acceptance decision from the same signed records.
FAQ
What are machine-readable service terms?
Machine-readable service terms are structured, versioned rules that software can validate and evaluate. They can describe pricing, billable events, refund eligibility, usage permissions, prohibitions, duties, delivery standards, acceptance tests, effective dates, issuer identity, and conflict behavior. A JSON document is not sufficient unless its fields have shared semantics.
Is an OpenAPI termsOfService field enough for an AI agent?
No. In OpenAPI 3.2.0, termsOfService is a URI. It can lead a person or client to the legal terms, but the core field does not encode the decision rules an agent needs. A service can pair OpenAPI with a separate terms manifest or a documented extension, provided clients understand and verify that vocabulary.
Are x402 payment requirements complete service terms?
No. x402 communicates payment requirements, authorization, and settlement information for HTTP resources. That is essential for machine-native payment, but pricing semantics beyond the current charge, usage rights, refund conditions, and delivery acceptance may need separate policy objects. Signed x402 offers and receipts should be interpreted according to the fields they actually bind.
Can machine-readable terms replace a legal Terms of Service document?
Not generally. The structured policy is an operational representation that helps software make consistent decisions. It should reference a versioned legal document and its digest. Legal enforceability, mandatory disclosures, governing law, and terms that cannot be safely reduced to code still require appropriate human and legal review.
What should an agent do with an unknown term?
It should return a deterministic state such as TERMS_UNSUPPORTED or REQUIRE_APPROVAL. It should not infer a permission, refund right, or delivery promise from unfamiliar wording. The service or client can add support through a new versioned vocabulary and conformance tests.
How should a provider version and update service terms?
Issue immutable versions with effective and expiry times, content digests, issuer signatures, and an explicit supersession relationship. Bind each live quote to the applicable policy version and preserve the exact terms and quote hashes in the accepted order. If a material field changes during checkout, expire or reissue the quote and evaluate it again.
Terms Become Automatable Only When Unknown Means Stop
The goal is not to translate every paragraph of a legal agreement into code. It is to identify the decisions an agent must make and express those decisions with less ambiguity than prose allows.
That requires more than a Terms URL and more than valid JSON. Price needs a billable event and formula. Refunds need triggers and evidence. Usage rights need scoped permissions, prohibitions, and duties. Delivery needs an acceptance contract. Every record needs identity, time, version, integrity, and precedence.
Once those pieces exist, an agent can produce a bounded answer: accept, reject, request approval, or stop because the terms are unsupported. That final state—not a fluent summary of legal prose—is what makes service terms safe enough for automation.
