Developer Guide: Build a Google-AI-Optimised Integration for Your Mobility Marketplace

Hook: Your mobility marketplace is invisible to AI shoppers — and losing bookings

Travelers and commuters increasingly ask AI assistants for local mobility options. If your marketplace can’t authenticate, deliver precise availability, or reply to agentic requests from Google AI, you’ll miss direct-conversion paths and enterprise integrations that started scaling in late 2025 and early 2026. This guide gives engineering teams the playbook — auth patterns, payload examples, webhook and retry best-practices, testing strategies, and merchant UI rules — to build a Google-AI-optimised integration for your mobility marketplace.

The 2026 context: Why AI discoverability matters now

Large platforms are moving beyond links to agentic commerce and direct bookings inside AI interfaces. In 2026 we saw major retailers and marketplaces enabling purchases via Google’s AI Mode and the Gemini app; Shopify and Google co-developed open specs for agentic checkouts (the Universal Commerce Protocol); and Google Cloud launched agentic AI tooling that surfaces third-party commerce integrations to logged-in users in the U.S. These shifts change how discovery and conversion happen — AI agents expect structured, authenticated APIs and clear merchant metadata.

For mobility marketplaces this is particularly critical: users ask a conversational assistant for a “cheap scooter near King’s Cross for 2 hours” and expect a real-time answer, booking, and payment flow without hopping between apps. To be actionable in that flow you must support:

• Authenticated agent requests with clear permission boundaries (who can act, on what behalf).
• Fast availability queries with deterministic, consistent responses.
• Order creation and confirmation with idempotency and webhook delivery.
• Merchant UI patterns that enable discovery signals and trust verification for AI indexing.

High-level architecture for Google-AI-optimised integrations

At a glance, your system should expose three things to an AI integration layer:

1. Discovery & metadata API — product catalog, constraints, insurance, verification badges.
2. Availability & pricing API — deterministic quotes for given time windows and locations.
3. Order & payment API — create, confirm, cancel, and post-order webhooks.

Behind those APIs you’ll run authentication, authorization, telemetry and rate-limiting, and generate structured metadata (JSON-LD, Schema.org) for the merchant and offers. The sections below explain secure auth flows, payload definitions, and testing approaches that let Google’s agentic tooling and other AI platforms index and transact with confidence.

Auth patterns: What to use and when

AI agent integrations need both machine-to-machine trust and delegated user permission. Use a combination of OAuth 2.0 (Authorization Code with PKCE) for merchant and end-user delegations, and short-lived service tokens for machine-to-machine requests.

1) Merchant onboarding (OAuth 2.0 + scopes)

When a merchant links your marketplace to Google or a third-party agent, use OAuth with fine-grained scopes:

• scope: payments:read/write — allow agent to create pre-authorizations.
• scope: inventory:read — allow availability lookups.
• scope: orders:write — allow order creation on behalf of the merchant.

Store the refresh token encrypted and mint short-lived access tokens for agent calls. Require merchants to confirm T&Cs for AI-mode transactions and display what the agent can do in the merchant dashboard.

2) End-user delegations (OAuth + token exchange)

For logged-in users that want AI agents to act on their behalf (e.g., “book a car from my default payment method”), implement an explicit delegation flow where the user consents via your UI or via a deep link. Use OAuth 2.0 authorization code with PKCE and the Token Exchange (RFC 8693) pattern if you need to exchange Google-issued tokens for marketplace tokens.

3) Machine-to-machine: JWT and mutual TLS (mTLS)

For verified partner agents (Google’s agentic runtime, marketplace aggregators), use mTLS or signed JWTs (with short TTLs). JWTs should include:

{
  "iss": "partner.example.com",
  "sub": "agent-google",
  "aud": "https://api.marketplace.example",
  "exp": 1710000000,
  "scope": "inventory:read orders:write"
}

Validate the cert chain or JWT signature, check the kid against your JWKS endpoint, and rotate keys regularly.

4) Scopes, roles and least privilege

Design scopes for specific capabilities and enforce them at the resource level. Example scopes for mobility:

• vehicles:read
• vehicles:reserve
• transactions:authorize
• user:contacts (for pickup/drop contact data)

Always require explicit merchant opt-in for payment and user impersonation scopes.

Practical payload examples

Below are concise JSON examples for the APIs AI agents will call. These are designed to be deterministic and easy for LLM prompt engineers to consume.

1) Discovery / product manifest

{
  "merchant_id": "m_10234",
  "offers": [
    {
      "offer_id": "scooter_uk_001",
      "title": "Electric scooter — 2-hour slot",
      "vehicle_type": "scooter",
      "location": {"lat": 51.5308, "lng": -0.1230, "radius_m": 200},
      "constraints": {"min_age": 18, "license_required": false},
      "trust": {"verified": true, "insurance": "third_party_basic", "ratings": 4.7},
      "supported_actions": ["availability_check", "reserve", "cancel"],
      "schema_version": "2026-01"
    }
  ]
}

2) Availability & quote request

{
  "request_id": "req_20260118_0001",
  "offer_id": "scooter_uk_001",
  "start": "2026-02-20T10:00:00+00:00",
  "end": "2026-02-20T12:00:00+00:00",
  "pickup_location": {"lat":51.5308, "lng":-0.1230},
  "passengers": 1,
  "currency": "GBP"
}

// Response
{
  "request_id":"req_20260118_0001",
  "availability": true,
  "price": {"amount": 9.50, "currency":"GBP"},
  "quote_id": "q_501",
  "estimated_total": 9.50,
  "ttl_seconds": 120
}

3) Order creation (idempotent)

{
  "idempotency_key": "user_12345:quote_q_501",
  "quote_id": "q_501",
  "user": {"user_id":"u_12345", "contact":{"phone":"+447900000000"}},
  "payment": {"method":"preauth_card", "payment_method_id":"pm_9876"},
  "metadata": {"source":"google_agent_v1", "agent_request_id":"ga_001"}
}

// Response
{
  "order_id":"o_3001",
  "status":"CONFIRMED",
  "pickup_code":"AB12CD",
  "webhook_urls":["https://merchant.example/webhooks/orders"]
}

4) Webhook example (order.updated)

{
  "event":"order.updated",
  "order_id":"o_3001",
  "status":"IN_USE",
  "timestamp":"2026-02-20T10:05:00Z",
  "signature":"sha256=..."
}

Sign webhooks (HMAC SHA-256) and publish the public keys for webhook verification in your developer portal.

Webhooks, retries and idempotency

Reliable webhook delivery is non-negotiable for agentic commerce. Follow these practices:

• Exponential backoff with jitter for retries (retry 5 times over 24 hours for critical events).
• Idempotency keys for order creation — store and return the same resource if the key repeats.
• Replay endpoints in your merchant dashboard to let merchants reprocess events for debugging.
• Webhook signing so agents and merchants can verify authenticity.
• Event ordering guarantees using sequence numbers or timestamps to resolve out-of-order deliveries.

Testing strategies: simulate agentic flows

Testing for AI discoverability is multi-dimensional: unit, contract, integration, synthetic agent tests, and production canary runs. Use the following test layers:

1) Contract testing (Pact or OpenAPI-driven)

Publish an OpenAPI spec for your public integration and run consumer-driven contract tests. AI agents expect stable request and response shapes — lock them down with contract tests so schema drift doesn’t break agent flows.

2) Mock agent harness (simulate Google AI)

Create a mock agent that mimics Google AI requests: short-lived JWTs, rapid availability requests, and simultaneous order placements. Test behaviours like optimistic concurrency, double submits, and network partitioning. If you want a fast starter for a sandbox harness, consider using a micro-app scaffold to validate end-to-end flows.

3) End-to-end (+ payment network sandbox)

Use sandbox environments for your payment provider (Stripe/Adyen) and Google’s agentic endpoints where available. Verify full lifecycle: discovery → availability → quote → payment preauth → webhook → completion.

4) Load & chaos testing

Agentic queries can spike unpredictably. Perform load tests that emulate bursty conversational traffic (many small availability queries per second). Run chaos experiments that kill backend components and see whether your system fails gracefully.

5) Observability & distributed tracing

Instrument traces across request boundaries. Ensure each agent request has a tracing header (e.g., traceparent) that persists into webhooks and payments. Correlate logs to the agent_request_id and make logs searchable by merchants for debug sessions. For reference on embedding observability into serverless and distributed systems, see observability & distributed tracing.

Debugging tactics and production safety

Common production issues include mismatched time windows, price rounding errors, and token expiry. For fast diagnosis:

• Expose live replay for incoming agent requests in your dev console.
• Return human-readable error codes and machine-readable error codes; include resolution steps for merchants.
• Rate-limit agents with clear 429 payloads that include a Retry-After header and a reason code.
• Automate anomaly detections for suspicious agent behaviour (e.g., token reuse, high cancellation rates) — automate anomaly detections where possible and surface dashboards for merchant review.

Merchant UI recommendations to be discoverable via AI search

Visibility to AI agents depends on both API quality and how merchants present offers. Guide merchants to adopt these UX patterns:

1) Structured merchant profile

Require merchants to complete a structured profile: operation area, operating hours, verified insurance types, average dispatch time, and trust signals (ID, vehicle inspections). Expose this via the discovery API and as JSON-LD on their public listing pages.

2) Clear microcopy for AI mode opt-in

When merchants enable AI-booking, show a concise consent dialog outlining what agents can do: view availability, create reservations, process pre-authorizations. Keep a changelog and consent timestamps for auditing.

3) Availability windows & cancellation rules

AI agents prefer deterministic rules. Encourage merchants to define explicit cancellation penalties, buffer times for prep, and minimum/maximum booking durations. Use enumerated values rather than free text to aid machine parsing.

4) Rich trust artifacts

Provide verified badges (insurance verified, ID verified), a public inspection certificate link, and recent user ratings in the discovery API. These signals increase agent-level trust and conversion rates.

5) Support for agentic receipt and dispute flow

Agents need to confirm bookings and handle disputes. Provide endpoints that let agents update a user-facing receipt, escalate an insurance claim, or add a late-fee note.

Indexing & structured data for AI discovery

In addition to APIs, provide machine-readable public metadata. Implement:

• JSON-LD using Schema.org Offer/Service/Place
• OpenAPI / AsyncAPI published to a developer portal
• UCP-compatible commerce responses for agentic checkouts (where applicable)

Example JSON-LD snippet to embed on merchant pages:

{
  "@context":"https://schema.org",
  "@type":"Service",
  "name":"City Scooter — 2hr",
  "provider": {"@type":"Organization","name":"CityScoots"},
  "offers": {
    "@type":"Offer",
    "price":"9.50",
    "priceCurrency":"GBP",
    "availability":"http://schema.org/InStock"
  }
}

Security, privacy and regulatory notes

Agentic integrations add privacy obligations. Follow these guardrails:

• Minimise personal data returned in discovery endpoints; require authenticated detail calls for PII.
• Log consent and revocations and surface them in audits for merchants.
• Support regional compliance controls (GDPR Right to Erasure, UK data residency options) for merchant data and user data processed by third-party agents.
• Explicitly document your liability model for agent-initiated bookings (insurance, deposit, chargebacks).

Case study: Quick wins from a regional mobility operator (experience)

In late 2025 a UK regional operator integrated a simple discovery API and OAuth onboarding for Google AI Mode. Within 8 weeks they reported:

• 20% of new bookings attributed to AI assistant conversions
• 50% faster booking confirmation time (quote TTL and idempotency prevented duplicates)
• Lower dispute rate after adding signed webhooks and explicit cancellation rules

Their technical changes were straightforward: publish a stable OpenAPI spec, add HMAC-signed webhooks, and implement preauth payment flows. The biggest lift was merchant education around consent and trust signals.

Checklist: Roadmap to a production-grade Google-AI integration

1. Publish OpenAPI and JSON-LD manifests for merchants and agents.
2. Implement OAuth 2.0 onboarding with scoped permissions and token rotation.
3. Build availability & pricing endpoints with deterministic TTLs and idempotency keys.
4. Sign webhooks and expose replay/debug tooling in the merchant dashboard.
5. Run contract tests, mock agent harnesses, and payment sandbox E2E tests.
6. Instrument distributed tracing and anomaly alerts for agent traffic.
7. Add merchant UI prompts for AI opt-in, insurance info, and trust badges.
8. Define incident playbooks for mass-cancellation or token compromise scenarios.

Future-proofing: Predictions for 2026 and beyond

Agentic commerce will keep pushing toward deeper integrations. Expect:

• Greater adoption of universal commerce protocols (like UCP) and standardised agentable checkout flows.
• Increased demand for real-time policy and liability assertions embedded in offers (insurance on a quote).
• New verification standards for physical assets (vehicle inspection attestations) that agents will require before booking.
• More automated reconciliation APIs between marketplaces, payment providers and agent platforms.

Aligning your APIs and merchant UX now will give you a discovery and conversion advantage as these standards consolidate.

“AI discoverability is no longer just about SEO; it’s about API reliability, explicit consent, and machine-readable trust.”

Actionable takeaways

• Implement OAuth + short-lived tokens and enforce least privilege scopes for agent access.
• Deliver deterministic availability and quote APIs with tight TTLs and idempotency keys.
• Sign and replay webhooks and provide merchant replay tools to reduce dispute friction.
• Expose structured merchant metadata (JSON-LD, OpenAPI) to be indexable by Google AI and other agents.
• Test with a mock agent harness and do burst/chaos testing to verify production resilience.

Next steps — rollout plan for product and engineering teams

1. Week 1–2: Publish API specs, add JSON-LD snippets on merchant pages, and define scopes.
2. Week 3–4: Build OAuth onboarding and service token exchange; deploy sandbox endpoints.
3. Week 5–8: Integrate payment preauth flows, webhook signing, and implement idempotency.
4. Week 9–12: Run contract tests, mock agent E2E tests, merchant pilot, and collect telemetry.
5. Ongoing: Iterate on merchant UX for consent, trust signals, and dispute automation.

Call to action

Ready to make your mobility marketplace discoverable and bookable via Google AI? Start with a simple step: publish an OpenAPI spec and JSON-LD offers for your top 10 most-booked items. If you’d like, download our integration scaffold and mock-agent harness (includes OAuth samples, webhook signing, and test vectors) to run an AI-mode pilot in your sandbox.

Get the integration scaffold and pilot checklist — sign up for the SmartShare developer sandbox or contact our integration team for a 30-minute technical review.

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