How to Build an AI Review API Integration

Integration overview: the request lifecycle

Every AI review integration follows the same async loop. Your application submits content via REST API; the platform routes it to qualified reviewers; when review completes, a signed webhook POSTs the verdict back to your server. Your app owns business logic and delivery; the review platform owns routing, reviewer assignment, and verdict integrity. Keep that boundary crisp so you can change routing rules or scale reviewer pools without redeploying application code.

The integration surface has three touchpoints engineers must get right:

• Outbound API calls — Authenticated task submission with idempotency keys and routing metadata
• Inbound webhooks — HMAC-verified callbacks processed asynchronously with deduplication
• Operational hooks — Retries, rate-limit handling, dead-letter queues, and structured logging

Design your integration as event-driven from the start. Polling GET /v1/tasks/{id} works for debugging, but webhooks are the production path — they reduce latency, cut API quota usage, and give you a natural hook for idempotent result processing.

Authentication setup

Start by generating an API key from your Verified Workflows dashboard. Store it in environment variables — VW_API_KEY in production, a separate sandbox key for development — never in source code or client-side bundles. Your key grants access to specific workspaces and task types; scope it to only what your integration needs. A CI pipeline key should not have billing admin permissions. A production submission key should not have sandbox-only scopes.

Authenticate every outbound request with a Bearer token in the Authorization header. Rotate keys on a schedule — quarterly is a reasonable default — and use separate keys per environment so a leaked dev key cannot submit production tasks. When rotating, support a brief overlap window where both old and new keys work, then revoke the old key once traffic confirms the new one.

curl -X POST https://api.verifiedworkflows.com/v1/tasks \
  -H "Authorization: Bearer ${VW_API_KEY}" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: req_8f3a2b1c" \
  -d '{"callback_url": "https://example.com/webhooks/review", ...}'

Log authentication failures separately from validation errors. A spike in 401 responses usually means a deployment pushed the wrong secret — not a model quality problem. Alert on 401 rate above baseline within five minutes of any deploy.

Task submission

Submit tasks via POST to /v1/tasks. Each task needs a type (text-review, code-review, image-review, and others), the content to review, and routing preferences. Include a unique client-generated idempotency_key in every request — this becomes critical when networks drop packets or your retry logic fires twice. The API returns a task_id (HTTP 201 on first submission, HTTP 200 on duplicate key within the 24-hour cache window) that you store for correlation and audit trails.

A minimal production payload includes four fields:

• callback_url — Where signed verdicts are POSTed when review completes
• payload — The AI output, task type, and domain context reviewers need
• routing — Minimum reviewers, required skills, priority tier, and SLA class
• idempotency_key — Deterministic key derived from your internal request ID

{
  "callback_url": "https://api.example.com/webhooks/vw-review",
  "idempotency_key": "order_4421_transcript_v3",
  "payload": {
    "type": "text-review",
    "content": "[AI-generated support reply]",
    "context": "Tier 1 customer — billing dispute"
  },
  "routing": {
    "min_reviewers": 2,
    "skills": ["customer-support"],
    "priority": "express",
    "sla_hours": 4
  }
}

Attach risk tier metadata at submission time — critical, standard, or exploratory — so routing rules align with your verification gates. Critical outputs should always set min_reviewers: 2 or higher. Standard outputs can use sampling rules you configure in the visual builder.

Routing configuration

Configure how tasks are routed to reviewers through the routing object on each submission, or set workspace defaults in the dashboard. You can specify reviewer expertise requirements, priority levels, and timeout thresholds. For sensitive content, route to verified reviewers with domain expertise. For high-volume, lower-stakes review, use automated pre-screening with human spot-checks configured in your routing policy.

Routing rules can be updated via the API and dashboard without redeploying your integration — but your submission payload should still declare intent explicitly. Do not rely on implicit defaults that might change when a product manager updates workspace settings. Version your routing config and log which version was active when each task was submitted.

Design routing as versioned configuration, not hardcoded logic. When you launch a new product line with unfamiliar terminology, temporarily tighten skill requirements. When error rates drop after a model update, relax consensus thresholds. Every routing change should be logged and reversible within one click.

Webhook setup

Do not poll for results in production — use webhooks. Configure a webhook endpoint in your dashboard and register the URL on each task via callback_url, or set a workspace default. When review completes, Verified Workflows sends a POST request to your endpoint with the full result payload. Include HMAC signature verification on every inbound request so you can confirm authenticity before processing.

POST /webhooks/vw-review HTTP/1.1
Content-Type: application/json
X-Verified-Workflows-Signature: sha256=abc123...

{
  "task_id": "tsk_live_a1b2c3",
  "status": "completed",
  "result": {
    "approved": false,
    "decision": "flag",
    "confidence": 0.72,
    "comments": "Dosage unit inconsistent with patient weight",
    "corrected_content": "..."
  },
  "reviewers": 2,
  "agreement": true
}

Your webhook endpoint must return HTTP 200 within five seconds, or the system retries with exponential backoff. Acknowledge fast, process async: write the raw payload to a queue or staging table, return 200, then run business logic in a background worker. Slow handlers are the number-one cause of duplicate deliveries and out-of-order processing.

Result handling

Process webhook payloads asynchronously in a dedicated worker, not in the HTTP handler thread. The result includes the review decision (approve, reject, flag), confidence score, reviewer comments, corrected content when applicable, and metadata about the review process. Store results in your database for audit trails — original AI output, verdict, corrections, reviewer count, and timestamps.

Branch on decision type with explicit handlers:

• approve — Release output to downstream delivery; log approval event
• reject — Block delivery; trigger regeneration or human escalation in your app
• flag — Route to internal review queue; do not auto-ship until your team resolves

Design your result handler to be idempotent. Duplicate webhook deliveries — from retries or network duplicates — must not create duplicate processing or double-apply corrections. Use task_id as a natural deduplication key with a unique constraint in your results table.

Error management

Implement retry logic with exponential backoff for transient failures: connection timeouts, HTTP 502/503/504, and rate-limit responses. For permanent errors — invalid task type, missing required fields, malformed payload — log the error, alert your team, and do not retry. Blind retries on 422 responses waste quota and bury real bugs.

Handle rate limiting by reading the Retry-After header and backing off accordingly. Keep a dead-letter queue for tasks that fail repeatedly so you can investigate without losing data. Structure DLQ entries with the original payload, error classification, attempt count, and last response body — enough context to replay manually after a fix.

Separate error budgets for submission failures and webhook processing failures. Submission errors block review from starting; webhook errors block delivery after review succeeded. The latter is worse for customer trust because you paid for review but never shipped the result.

Idempotency

Always include an idempotency key with task submissions. If your network drops a request or your server retries, the idempotency key ensures the task is not submitted twice. Use a deterministic key derived from your internal identifiers — for example, order_{id}_{content_hash} or a UUID you generate once at request creation and persist before the HTTP call.

The API caches idempotency keys for 24 hours. Within that window, resubmitting the same key returns the original task_id without creating a duplicate task. Beyond 24 hours, the same key may create a new task — design keys with enough entropy that accidental collision across days is impossible.

Apply the same idempotency discipline to webhook handlers. Store processed task_id values with a processed-at timestamp. On duplicate delivery, return 200 immediately without re-running side effects. This pattern is non-negotiable for integrations that modify user-visible content.

Rate limiting

Respect rate limits to avoid throttling. The API enforces limits per workspace and per API key. Check X-RateLimit-Remaining and X-RateLimit-Reset on every response and implement client-side throttling when you approach the limit. A token-bucket or leaky-bucket limiter in your submission service prevents thundering herds after deploys or batch jobs.

For high-volume integrations, contact support to request a rate limit increase before you go live — not after you start hitting 429 errors in production. Model your peak submissions per minute from expected traffic plus a 3× safety margin. If batch imports spike above sustained limits, queue submissions client-side and drain at a controlled rate.

When you receive HTTP 429, honor Retry-After exactly. Jittered backoff prevents synchronized retries across multiple instances. Log 429 frequency by endpoint — sustained throttling means your architecture needs a queue, not longer backoff intervals.

Testing in sandbox

Use the sandbox environment for development and integration testing. It mirrors production API behavior but uses synthetic reviewers and does not count against your live task quota. Test the full lifecycle: submit a task, receive the webhook, process the result, and handle error cases including timeout and malformed payload scenarios.

Build an integration test suite that covers:

1. Successful submission with valid routing — expect 201 and task_id
2. Duplicate idempotency key — expect 200 with same task_id
3. Invalid payload — expect 422 with structured error body
4. Webhook signature verification — reject tampered payloads
5. Webhook retry simulation — duplicate delivery does not double-process

Pay special attention to timeout scenarios. What happens when a reviewer does not complete a task within your SLA? Configure timeout webhooks or poll fallback for stuck tasks, and ensure your app does not block user-facing flows indefinitely. Return a graceful degraded state rather than hanging.

Production monitoring

Monitor key metrics from day one: task completion rate, P95 review latency, webhook delivery success rate, idempotency collision rate, and error frequency by HTTP status class. Set up alerts for anomalies — a sudden drop in completion rate might indicate a routing problem, while spiking latency could signal reviewer capacity issues.

Use the platform analytics endpoint to track quality trends over time and adjust routing rules as needed. Export metrics to your existing observability stack — Datadog, Grafana, or CloudWatch — with consistent labels: workspace, task_type, priority, and routing_version. Dashboards should answer three questions in under ten seconds: Are submissions succeeding? Are reviews completing on time? Are webhooks reaching our app?

Define rollback triggers before launch: webhook failure rate above 2% for fifteen minutes, submission error rate above 1%, or P95 review latency exceeding SLA by 2×. When a trigger fires, pause automated submissions, route new outputs to a hold queue, and alert on-call. If you cannot pause and diagnose in under five minutes, you are not ready for Tier 1 traffic.

Your two-week implementation checklist

Start with one task type and one routing profile. Measure completion rate, webhook success, and correction rate before expanding. Use real data from shadow mode — submit production-shaped payloads without blocking delivery — to tune routing before you enforce gates.

1. Days 1–2: Generate API keys; wire authenticated submission with idempotency keys; read the API reference
2. Days 3–4: Deploy webhook endpoint with HMAC verification and async worker queue
3. Day 5: Run full sandbox lifecycle tests including duplicate delivery and 429 backoff
4. Week 2: Shadow submissions on live traffic; tune routing and error alerts from real data
5. Week 2, end: Enable blocking gates for Tier 1 outputs; keep Tier 2 on sampled async review

Share a one-page readiness summary with leadership: shadow error rate, webhook success rate, P95 review latency, and rollback drill result. Engineering owns the integration; executives approve go-live. That separation keeps velocity high without hiding integration risk.

An API integration is not a feature — it is infrastructure. Authentication, idempotency, signed webhooks, and observability are not polish you add later; they are the foundation that determines whether human review accelerates your pipeline or becomes the thing that breaks at scale. Teams that treat integration as seriously as their data layer ship faster over the long run.