service-cataloglogisticsintegrations

Service Catalog Design: Exposing Autonomous Trucking Capacity as an Internal Service

UUnknown

2026-02-16

10 min read

Design an internal service catalog to expose autonomous trucking capacity with discoverable endpoints, SLAs, and TMS-ready integrations.

Hook: You need discoverable autonomous trucking capacity without operational chaos

Logistics and platform teams in 2026 face a repeating set of problems: fragmented capacity sources, manually stitched TMS integrations, and opaque SLAs that make capacity planning a guessing game. Teams evaluate Aurora-style autonomous truck capacity but struggle to surface it as a first-class, discoverable internal product that dispatchers, planners and partner applications can consume reliably. This article shows how to design a service catalog entry for autonomous trucks — with domain endpoints, explicit service-levels, discovery metadata and practical integration patterns for modern TMS-driven operations.

The context in 2026: why this matters now

By late 2025 and into 2026, the industry accelerated TMS-to-autonomy integrations. Public pilots and early production links — like the Aurora-McLeod connection that gave carriers direct autonomous capacity within their TMS workflows — made clear that the technical challenge is not simply connecting APIs. The real challenge is exposing fleet capacity as a reliable internal product that operations teams can discover, measure and automate against.

Trends shaping design choices:

Edge compute and telemetry: Autonomous trucks stream high-cardinality telemetry and health metrics.
Regulatory fragmentation: Coverage is regional and evolves quickly; discovery metadata must include jurisdiction and compliance tags.
SLO-driven operations: Teams favor SLOs and SLIs over vague SLAs to manage predictable capacity.
Marketplace-style internal catalogs: Productized internal services reduce cognitive load and accelerate adoption.

Design goals: what the catalog entry must provide

Design the autonomous-truck catalog entry as an internal product. The minimum viable product should satisfy four goals:

Discoverability — planners must find capacity by region, vehicle class and SLA tier from the catalog UI or API.
Consistent contract — stable API endpoints for tendering, dispatching and telemetrics with OpenAPI specs and event schemas.
Observable SLAs — SLIs, SLOs and dashboards that mirror capacity performance and support chargeback.
Operational controls — rate limits, quotas, access scopes and failover policies exposed in the catalog metadata.

Core domain endpoints to expose

Keep endpoints small, intent-driven and versioned. Treat these endpoints as the canonical surface for every internal consumer (TMS, planning tools, analytics, partner portals).

Recommended endpoint set

/capacity/search — Query available autonomous truck capacity by time window, origin/destination corridors, vehicle class, and special capability (temperature control, hazmat).
/tenders — Create and manage tender requests. Accepts standardized load manifests and business rules.
/dispatch — Commands for assignment and schedule confirmations, including ETA windows and reroute requests.
/tracking/{assetId} — Real-time location and state stream for an asset, with snapshot and historical query. For edge agents and vehicle-side logic see notes on edge agent reliability.
/telemetry — High-frequency health and sensor data ingestion for fleet ops and predictive maintenance.
/exceptions — Register and manage incidents, manual interventions, and escalation artifacts.
/status/health — Service health indicators and regional availability metadata used by discovery layers and load balancers.

Example simple REST resource paths keep TMS integration straightforward. For high-volume telemetry and tracking, pair REST control-plane endpoints with an event-pipeline for streaming telemetry and webhooks for immediate state changes.

Discovery metadata: make capacity findable and trustworthy

Service discovery for autonomous capacity needs richer metadata than standard microservices. The catalog entry should include:

Geographic coverage — Regions, states, road types and any conditional coverage notes.
Capacity types — Long-haul, regional, refrigerated, heavy-haul, team-drive.
SLA tiers — Bronze/Silver/Gold mappings with explicit SLOs per metric.
Operational windows — Start/stop times and maintenance windows.
Compliance tags — Permits, local exemptions and regulatory statuses.
Onboarding requirements — Subscription needs, test credentials and insurance prerequisites.

SLA & SLO design: translate commercial guarantees into operational metrics

Modern practice favors SLOs that teams can measure and act on. For autonomous trucking capacity, translate business SLAs into measurable SLIs.

Key SLIs to track

Availability — Percentage of time the capacity search and tender APIs return valid capacity for a supported corridor.
Acceptance latency — Median time between tender submit and accepted/declined response.
Acceptance rate — Percent of tenders accepted (by SLA tier or capacity class).
On-time delivery — Percent of delivered shipments within agreed ETA window.
Incident MTTR — Mean time to resolve operational incidents affecting acceptance, dispatch or tracking.

Example SLO targets

Availability: 99.95% for Gold corridors.
Acceptance latency: median < 30 seconds for tenders in Gold.
Acceptance rate: > 92% for Gold, > 80% for Silver.
On-time delivery: > 95% within scheduled window for Gold.

Publish these SLOs in the catalog and provide ready-made dashboards. Expose error budgets as first-class items that trigger operational playbooks and quota changes.

Authentication, authorization and secure discovery

Secure the catalog and endpoints using industry best practices that integrate with enterprise identity:

mTLS for service-to-service communications between TMS adapters and fleet APIs.
OAuth2 with Token Exchange for front-end applications and partner integrations, enabling scoped access for tendering vs telemetry queries.
Fine-grained RBAC — scopes for tender:create, dispatch:update, telemetry:read, admin:manage.
Secrets management via Vault with short-lived credentials for edge agents in trucks.
Audit trails for tenders and dispatch commands — mandatory for compliance and dispute resolution.

Integration patterns: synchronous control + asynchronous events

Match patterns to functional requirements. Use synchronous APIs for control-plane work (search, tender, dispatch) and event-based pipelines for telemetry, tracking and bulk updates.

Control plane — REST or gRPC endpoints behind an API gateway with OpenAPI contract and deterministic responses.
Data plane — Kafka, Pulsar or cloud-native streaming for telemetry; use protobuf/Avro schemas and schema registry.
Webhooks — For immediate status changes to downstream TMS workflows; subscribe through the catalog UI and manage webhook delivery retries and signing.
Event sourcing — Persist tender lifecycle events for audit and rehydration.

Recommended stack and integrations marketplace architecture

Below is a practical, production-ready stack used by platform teams that expose internal services as marketplace products.

Infrastructure and platform

Kubernetes for microservices and fleet-edge connectors.
Knative or KEDA for scalable event-driven workloads.
Envoy with Istio or Linkerd for service mesh features (mTLS, traffic shifting, observability).

API and service catalog

API gateway: Kong, Ambassador, or cloud-native API Management for tenant-aware routing and rate limiting.
Service catalog/registry: HashiCorp Consul or Backstage for internal developer catalog and UI-driven discovery.
OpenAPI specs and AsyncAPI for event contracts; host them in a catalog artifact store.

Streaming and telemetry

Event bus: Kafka, Pulsar or cloud-managed equivalent for telemetry and tracking streams.
TimescaleDB or ClickHouse for time-series and route/history analytics.
OpenTelemetry, Prometheus, Grafana and Jaeger

Security and identity

Keycloak or Ory for OAuth2 token management and token exchange patterns.
HashiCorp Vault for secrets and signing keys for edge agents.

CI/CD and governance

ArgoCD or Flux for GitOps delivery.
Terraform for infra as code and policy enforcement via Sentinel or OPA.

Catalog UX: make it easy for operations and developers

Design the catalog UI and APIs so users can filter, compare and onboard quickly:

Search by corridor, SLA tier, capacity window and compliance tags.
Side-by-side SLA comparisons with SLOs, pricing and historical performance charts.
Self-service onboarding with test credentials and a sandbox corridor for validation.
One-click integration templates for common TMS platforms and workflows (McLeod, TMW, Oracle Transportation, etc.).

Operational playbook: SLO-driven runbook and escalation

Publish an embedded runbook with the catalog entry so every consumer knows what to do when things go wrong. Key elements:

Health check sequence — How to validate service health and local network path before escalation.
Incident triage — Steps to check acceptance latency, telemetry lag and on-truck health signals.
Fallback flows — How to fall back to human-driven capacity or alternate carriers when error budget is breached.
Escalation matrix — Contact lists, SLAs for response and resolution, and automated pager routing.

Billing, quota and chargeback

Expose pricing and quota metadata so internal finance and delivery teams can plan and control spend.

Unit-based pricing: per-mile, per-tender, or reserved capacity subscriptions.
Quota controls: concurrent tenders, tenders per minute, telemetry throughput caps.
Usage telemetry exported to billing systems for showback and chargeback — see guidance on budgeting and invoice forecasts.
Dynamic pricing tiers in the catalog for surge windows and premium corridors.

Governance, data residency and compliance

Because autonomous operations cross jurisdictional boundaries, include compliance and data residency metadata in each catalog entry. Also implement governance controls:

Policy enforcement using OPA to gate tenders or dispatches in restricted regions — pair this with automated checks and CI gates described in legal and compliance automation.
Data retention policies for telemetry and event auditable records.
Encryption at rest and in transit for PII and sensitive operational data.

Advanced strategies: resilience, ML and predictive allocation

For mature platforms, incorporate intelligent capabilities that make the catalog proactive:

Predictive capacity allocation — Use historical tenders and traffic models to surface likely available capacity for upcoming windows.
SLO-aware scheduler — A scheduler that reserves capacity only if it maintains error budgets; otherwise it flags fallback options.
Canary and chaos experiments — Run controlled experiments on corridors to validate changes in dispatch logic without impacting all consumers.
Contract-aware routing — Respect regulatory, contractual and equipment constraints when routing tenders to fleet assets.

Concrete example: tender lifecycle API sketch

Below is a minimal pseudo-API to make the concept tangible. Use OpenAPI for production-ready specs.

POST /tenders
Request body: {
  origin: {lat, lon},
  destination: {lat, lon},
  earliestPickup: ISO8601,
  latestDelivery: ISO8601,
  weightKg: 12000,
  equipment: ["reefer", "heavy"],
  compliance: {hazmat: false},
  customerId: "acct-123",
  slaTier: "gold"
}

Response: {
  tenderId: "tndr-456",
  status: "submitted",
  expectedResponseInSeconds: 20
}

Consume status changes via the events stream or a webhook defined at onboarding time.

Case study reference and real-world signals

Early integrations like the Aurora-McLeod link showed demand patterns: carriers wanted the ability to tender and manage autonomous loads from within existing TMS workflows without disrupting operations. Russell Transport’s early adoption highlighted that the right UX and predictable acceptance behavior produce rapid operational gains.

"The ability to tender autonomous loads through our existing TMS dashboard has been a meaningful operational improvement. We are seeing efficiency gains without disrupting our operations." — Russell Transport

Checklist: launch your autonomous capacity catalog entry

Define domain endpoints and publish OpenAPI/AsyncAPI specs.
Model discovery metadata: regions, capacity types, compliance tags and SLA tiers.
Implement API gateway + service registry and publish the catalog UI entry.
Instrument SLIs and create SLO dashboards; publish SLOs and error budgets in the catalog.
Enable OAuth2/mTLS, RBAC and secrets rotation for secure onboarding.
Provide onboarding templates for major TMS platforms and a sandbox corridor for validation.
Expose billing metrics and quota controls to finance and consumption teams.
Create an operational runbook and automated escalation playbooks tied to SLO breaches.

Final recommendations and pitfalls to avoid

Recommendations:

Start with a small, well-scoped catalog entry focused on a limited set of corridors and SLA tiers.
Prioritize SLOs that directly reflect business outcomes: acceptance rate and on-time delivery beat raw uptime metrics for operational teams.
Provide turnkey TMS connectors so adopters can test the product without building bespoke integrations.
Invest in schema management for telemetry to avoid downstream incompatibilities.

Pitfalls to avoid:

Publishing undocumented or unstable endpoints — always version and publish breaking changes via the catalog.
Hiding quotas and pricing — surprise chargebacks kill internal adoption.
Ignoring regional compliance — a service that works in one state may be illegal in another.

Call to action

If your platform or logistics team is evaluating how to expose autonomous trucking capacity as an internal managed product, start by modeling a single corridor and SLA tier in your catalog. Use the checklist above to get to a production-ready entry and instrument SLOs from day one. For a ready-made template, integration adapters and OpenAPI examples tailored to common TMS platforms, contact your platform team or explore the internal integrations marketplace to bootstrap adoption. Make capacity discoverable, measurable and actionable — and turn autonomous trucks from a novelty into a reliable product in your enterprise fleet.

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