APIs and Webhooks in Automation: The Complete Technical Guide to REST, GraphQL, Authentication, and Production-Grade Error Handling

APIs and webhooks are the technical foundation of every non-trivial automation. An API lets your workflow read data from one system and write it to another. A webhook lets a system push events to your workflow in real time. Together, they enable event-driven architectures that react in seconds rather than minutes or hours.

According to Postman's 2023 State of the API Report, 89% of developers reported that APIs are essential to their organisation's strategy, and API-first companies were significantly more likely to report faster development cycles. For automation builders, understanding APIs and webhooks at a practical level—authentication, error handling, rate limiting, delivery guarantees—is the difference between workflows that work in demos and workflows that work in production.

This guide is written for developers and technical automation builders. It covers REST, GraphQL, and webhook fundamentals, dives deep into authentication patterns, and provides production-grade strategies for rate limiting, error handling, monitoring, and performance optimisation.

What is an API and why it matters for automation

An API (Application Programming Interface) is a contract between two systems. System A sends a request in a defined format; System B returns a response in a defined format. In the context of automation, APIs are how your workflows read from and write to external services—CRMs, payment processors, email platforms, databases, analytics tools, and thousands of other systems.

Every modern SaaS application exposes an API. Stripe's API handles payments, HubSpot's API manages CRM data, Twilio's API sends SMS messages, Slack's API posts messages to channels. When you connect these systems through Zapier, Make, or n8n, the platform is making API calls on your behalf.

Understanding how APIs work directly—not just through a platform's abstraction—gives you the ability to debug failed automations, build custom integrations, optimise performance, and handle edge cases that no-code platforms cannot anticipate.

REST APIs: the dominant pattern

REST (Representational State Transfer) is the most widely used API architecture style. Approximately 86% of developers use REST APIs, according to Postman's research. REST APIs organise functionality around resources (customers, orders, invoices) and use standard HTTP methods to operate on them.

HTTP methods and their meanings

• GET — Read data. `GET /api/customers` returns a list of customers. `GET /api/customers/123` returns a single customer. GET requests should never modify data. They are idempotent—calling the same GET request multiple times returns the same result (assuming no other changes).
• POST — Create a new resource. `POST /api/orders` with a JSON body creates a new order. POST is not idempotent by default—calling it twice creates two orders. This is why idempotency keys exist (more on that below).
• PUT — Replace a resource entirely. `PUT /api/customers/123` with a full customer object replaces the entire record. PUT is idempotent—calling it twice with the same data produces the same result.
• PATCH — Partial update. `PATCH /api/customers/123` with `{"email": "new@email.com"}` updates only the email field. PATCH is idempotent for the same payload.
• DELETE — Remove a resource. `DELETE /api/customers/123` deletes the customer. DELETE is idempotent—deleting an already-deleted resource returns a 404 or 204, not an error (in well-designed APIs).

HTTP status codes you need to know

Status codes tell you what happened with your request. For automation, understanding them determines how your workflow should respond to each outcome.

Success codes:

• 200 OK — Request succeeded. Data is in the response body.
• 201 Created — New resource created successfully. Common response to POST.
• 204 No Content — Success, but no response body. Common for DELETE and some PATCH operations.

Client error codes (do not retry):

• 400 Bad Request — Your request is malformed. Fix the payload before retrying.
• 401 Unauthorised — Authentication failed. Credential is invalid, expired, or missing. Refresh the token or check the API key.
• 403 Forbidden — Authenticated but not authorised. The credential does not have permission for this operation.
• 404 Not Found — The resource does not exist. Verify the ID or URL.
• 409 Conflict — The request conflicts with the current state (e.g. duplicate resource). Often indicates your idempotency logic is working—treat as success in retry scenarios.
• 422 Unprocessable Entity — The request is well-formed but contains invalid data (e.g. missing required field). Fix the data.

Server error codes (retry with backoff):

• 429 Too Many Requests — Rate limit exceeded. Wait and retry. Check the `Retry-After` header for guidance.
• 500 Internal Server Error — Server-side failure. Retry with exponential backoff.
• 502 Bad Gateway — Upstream server issue. Retry.
• 503 Service Unavailable — Server is temporarily overloaded or in maintenance. Retry with backoff.

Resource design and URL patterns

Well-designed REST APIs follow consistent URL patterns. Understanding these patterns helps you construct requests for APIs that may have sparse documentation:

• `/api/v1/customers` — Collection of customers (GET for list, POST to create)
• `/api/v1/customers/123` — Specific customer (GET, PUT, PATCH, DELETE)
• `/api/v1/customers/123/orders` — Orders belonging to customer 123
• `/api/v1/customers/123/orders/456` — Specific order for specific customer

Query parameters filter and paginate: `/api/v1/customers?status=active&page=2&limit=50`

GraphQL: when REST is not enough

GraphQL is an alternative API architecture developed by Facebook. Instead of fixed endpoints returning fixed data shapes, GraphQL provides a single endpoint where the client specifies exactly what data it needs. The server returns precisely that—nothing more, nothing less.

When to use GraphQL in automation

GraphQL is valuable for automation when: you need data from multiple related resources in a single request (e.g. a customer with their orders and each order's line items), the REST API returns too much data and you only need specific fields, or the system only offers a GraphQL API (Shopify's Storefront API, GitHub's GraphQL API).

GraphQL vs REST for automation use cases

| Aspect | REST | GraphQL | |--------|------|---------| | Data fetching | Fixed response per endpoint | Client specifies exact fields | | Multiple resources | Multiple requests | Single request | | Overfetching | Common (full objects returned) | Eliminated (request what you need) | | Caching | HTTP caching works natively | Requires client-side caching | | Error handling | HTTP status codes | 200 OK with `errors` array | | Automation platform support | Universal | Limited (HTTP module required) | | Learning curve | Lower | Higher |

For most automation workflows, REST is the pragmatic choice because of universal platform support. Use GraphQL when the API requires it or when data efficiency matters (large payloads, nested resources, high-volume operations).

Using GraphQL in workflow platforms

Zapier, Make, and n8n do not have native GraphQL modules, but you can use their HTTP/webhook modules to make GraphQL requests. Send a POST to the GraphQL endpoint with a JSON body containing `query` and `variables` fields. Parse the JSON response in subsequent steps.

Webhooks: real-time event-driven automation

A webhook inverts the request direction. Instead of your workflow calling an API to check for new data (polling), the external system pushes data to your workflow when an event occurs. This enables real-time automation with lower latency and fewer unnecessary API calls.

How webhooks work

1. Registration. You provide a URL to the webhook provider. This can be a platform webhook trigger URL (Zapier, Make, n8n all provide these), a custom endpoint you build, or a proxy service like Hookdeck or Svix. 2. Event occurs. A customer places an order, a payment succeeds, a contact updates in the CRM—whatever event you subscribed to. 3. Delivery. The provider sends an HTTP POST to your registered URL with a JSON payload containing the event data. 4. Acknowledgement. Your endpoint must respond with 200 OK within the provider's timeout window (typically 5-30 seconds). This acknowledges receipt. 5. Processing. After acknowledging, your workflow processes the event. Heavy processing happens asynchronously, not before the 200 response. 6. Retry on failure. If your endpoint does not respond, returns a 5xx error, or times out, the provider retries according to its retry policy (typically exponential backoff over hours or days).

Webhook vs polling: a quantitative comparison

Consider a workflow that reacts to new orders. A store processes approximately 100 orders per day, distributed unevenly.

Polling every 5 minutes: 288 API calls per day. 287 return no new data. Total API calls per month: 8,640. Latency: up to 5 minutes between event and processing.

Webhook: 100 deliveries per day (one per order). Zero unnecessary calls. Latency: typically under 5 seconds. Total webhook deliveries per month: approximately 3,000.

Webhooks are approximately 65% fewer requests and deliver sub-5-second latency versus 5-minute latency. The efficiency advantage grows with lower event frequency—a system with 10 events per day would make 288 polling calls versus 10 webhook deliveries.

When webhooks are not available

Not every API supports webhooks. When they are not available, use polling with these optimisations:

• Use `since` or `modified_after` parameters to only fetch records changed since your last poll. This reduces data volume.
• Use pagination to handle large result sets without overwhelming memory.
• Cache the last poll timestamp to avoid reprocessing.
• Poll at appropriate intervals — every 15 minutes for low-urgency data, every 1-2 minutes for time-sensitive data.

Authentication deep dive

Authentication determines how your workflow proves its identity to an API. The choice of authentication method affects security, maintenance burden, and failure modes.

API keys

An API key is a static credential—a string that identifies your application and grants access. The key is typically sent as a header (`Authorization: Bearer sk_live_xxx`) or query parameter (`?api_key=xxx`).

Advantages: Simple to implement, no token refresh logic, works everywhere.

Risks: Long-lived (compromise means extended exposure), difficult to scope granularly, cannot be revoked per-user. If an API key leaks, every workflow using it is compromised.

Best practices for API keys:

• Use separate keys per integration or workflow—not one key for everything
• Store in a secrets manager or encrypted environment variables (see our automation security guide)
• Rotate every 90 days (30 days for high-sensitivity systems like payments)
• Restrict by IP address where the API supports it (Stripe, [SendGrid](https://docs.sendgrid.com/ui/account-and-settings/api-keys))
• Monitor usage for anomalies (unexpected volume, unusual endpoints)

OAuth 2.0

OAuth 2.0 is the standard for delegated authorisation. Instead of sharing a static credential, the user grants your application specific permissions through an authorisation flow. The application receives a short-lived access token (typically 1 hour) and a refresh token (longer-lived, used to obtain new access tokens).

Authorization code flow (most common for automation): 1. Your application redirects the user to the provider's authorisation page 2. The user logs in and grants permissions (scopes) 3. The provider redirects back to your application with an authorisation code 4. Your application exchanges the code for an access token and refresh token 5. The access token is used for API requests; the refresh token renews it when it expires

Client credentials flow (machine-to-machine): 1. Your application sends its client ID and client secret to the provider's token endpoint 2. The provider returns an access token (no user interaction) 3. Used for server-to-server automation where no user context is needed

OAuth in workflow platforms: Zapier, Make, and n8n handle OAuth flows through their connection setup. When you "connect" a Salesforce or Google account, the platform manages the token lifecycle. For custom HTTP modules, you may need to handle token refresh manually.

Practical examples: Google OAuth 2.0 for Google Workspace, Salesforce OAuth for CRM access, HubSpot OAuth for marketing data, Slack OAuth for workspace integrations.

JSON Web Tokens (JWT)

A JWT is a self-contained token that encodes claims (user identity, permissions, expiration) in a signed JSON structure. JWTs are commonly used for API authentication in custom backends and service-to-service communication.

Structure: A JWT has three parts separated by dots: header (algorithm and type), payload (claims like `sub`, `iat`, `exp`), and signature (cryptographic verification). The signature ensures the token has not been tampered with.

In automation: JWTs are used when: building custom API integrations that issue their own tokens, authenticating with Google Cloud APIs using service account credentials (Google signs JWTs with the service account's private key), or implementing custom webhook verification where the provider includes a JWT in the request header.

Verification: Always verify the JWT signature using the issuer's public key or shared secret. Check the `exp` claim to ensure the token has not expired. Validate the `iss` (issuer) and `aud` (audience) claims to ensure the token is intended for your application.

HMAC (for webhook verification)

HMAC (Hash-based Message Authentication Code) is not an authentication method for making API calls—it is a verification method for incoming webhooks. The webhook provider computes an HMAC of the request body using a shared secret and includes it in a header. Your endpoint recomputes the HMAC and compares.

How it works: 1. Provider computes: `HMAC-SHA256(shared_secret, request_body)` = signature 2. Provider includes the signature in a header (e.g. `X-Signature-256`) 3. Your endpoint computes the same HMAC with the same shared secret 4. Compare signatures using a timing-safe comparison function 5. If they match, the request is authentic

Provider-specific headers:

• Stripe: `Stripe-Signature` (includes timestamp + signature) — docs
• GitHub: `X-Hub-Signature-256` — docs
• Shopify: `X-Shopify-Hmac-SHA256` — docs
• Twilio: `X-Twilio-Signature` — docs

Authentication decision matrix

| Scenario | Method | Rationale | |----------|--------|-----------| | Access user data in SaaS (CRM, email) | OAuth 2.0 | Scoped, revocable, user-delegated | | Server-to-server, no user context | Client credentials (OAuth) or API key | Machine-to-machine | | Custom backend API | JWT | Self-contained, verifiable, stateless | | Incoming webhook verification | HMAC | Ensures payload authenticity | | Legacy or internal systems | Basic auth or API key | Simplest option where security is managed at the network level |

Rate limiting: strategies and handling

Every API imposes rate limits—the maximum number of requests allowed per time window. Exceeding the limit returns 429 Too Many Requests. For automation workflows that process batches or fan out to multiple systems, rate limits are a constant design constraint.

Common rate limit policies

• Stripe: 100 read requests/second, 100 write requests/second in live mode
• HubSpot: varies by tier—Free/Starter: 100 requests per 10 seconds, Professional/Enterprise: 150 requests per 10 seconds
• Shopify: REST API uses a leaky bucket algorithm—40 requests fill the bucket; it drains at 2 requests/second
• Twilio: varies by endpoint—typically 100 requests/second for messaging
• Slack: tier-based—Tier 1 methods allow 1 request/minute, Tier 4 methods allow 100+ requests/minute

Exponential backoff with jitter

When you receive a 429 response, wait before retrying. Exponential backoff increases the wait time with each retry. Jitter adds randomness to prevent multiple workflows from retrying simultaneously (the "thundering herd" problem).

Algorithm: 1. Base delay: 1 second 2. Multiply by 2 for each retry: 1s, 2s, 4s, 8s, 16s 3. Add random jitter: `delay * (0.5 + random(0, 0.5))` 4. Cap at maximum delay: 60 seconds 5. Maximum retries: 5 (then fail with alert)

Check headers: Many APIs include a `Retry-After` header with the number of seconds to wait. If present, use it instead of calculating your own backoff. Stripe includes this header on 429 responses.

Queue-based rate limiting

For workflows that process batches (e.g. sync 10,000 contacts from CRM to email platform), a simple delay between requests is insufficient. Use a queue-based approach:

1. Load all items into a queue (database table, Redis list, or workflow platform's built-in queue) 2. Process items from the queue at a controlled rate (e.g. 2 per second for Shopify) 3. On 429, pause the queue consumer for the `Retry-After` duration 4. Resume processing when the rate limit resets

In n8n, use the SplitInBatches node with a Wait node to control processing rate. In Make, iterators with a sleep module achieve the same result. In Zapier, looping is limited—consider a custom Code step or breaking the batch into multiple Zap runs.

Pre-emptive throttling

Rather than hitting rate limits and reacting, calculate your request rate in advance and throttle proactively. If the API allows 100 requests per 10 seconds, space your requests at 100ms intervals. This avoids 429 errors entirely and produces smoother, more predictable execution.

Real-world webhook patterns

Delivery guarantees: at-least-once vs at-most-once

Most webhook providers offer at-least-once delivery: they guarantee the event will be delivered at least once, but it may be delivered more than once (due to retries). This means your handler must be idempotent—processing the same event twice should not create duplicate records, double-charge a customer, or send duplicate emails.

No major webhook provider offers exactly-once delivery. The pragmatic approach: design for at-least-once and deduplicate on the receiving end.

Event ordering

Webhook events may arrive out of order. A `customer.updated` event might arrive before the `customer.created` event that logically precedes it. This happens when: different events are processed by different servers at the provider, network latency varies, or retries deliver older events after newer ones.

Handling strategies:

• Timestamp-based resolution: Include a timestamp in your processing logic. If an incoming event has an older timestamp than the last processed event for the same resource, skip it.
• Version numbers: Some APIs include a version or sequence number. Only process events with a higher version than currently stored.
• Upsert logic: Use `INSERT ... ON CONFLICT UPDATE` semantics so that out-of-order events converge to the correct state regardless of arrival order.

Idempotency implementation

Every webhook provider includes a unique event identifier. Store processed event IDs and check before processing:

• **Stripe:** `id` field in the event object (e.g. `evt_1234567890`) — docs
• **GitHub:** `X-GitHub-Delivery` header (UUID) — docs
• **Shopify:** `X-Shopify-Webhook-Id` header — docs
• **HubSpot:** Event object includes unique identifiers — docs

Storage options for deduplication:

• Redis with TTL: Store event IDs with a 7-day expiration. Fast lookups, automatic cleanup. Use `SET event_id 1 EX 604800 NX` — returns null if the key already exists (duplicate).
• Database table: A simple table with event_id (primary key) and processed_at timestamp. Query before processing; insert after.
• **Workflow platform variables:** Make Data Stores and [n8n](https://n8n.io) can check external storage. Less ideal for high volume but works for low-to-medium throughput.

Error handling at scale

Production automation workflows must handle failures gracefully. A workflow that works 99% of the time but catastrophically fails on the 1% is not production-ready.

Dead letter queues

A dead letter queue (DLQ) captures messages (webhook events, API responses, workflow inputs) that could not be processed after all retry attempts are exhausted. Without a DLQ, failed events are lost.

Implementation: 1. After maximum retries, move the event to a DLQ (database table, Redis list, AWS SQS DLQ, or a dedicated "failed events" workflow) 2. Alert the operations team 3. Investigate and fix the root cause 4. Replay events from the DLQ once the fix is deployed 5. Track DLQ depth as an operational metric—a growing queue means unresolved failures

In workflow platforms: n8n has error workflows that trigger on failure. Make has error handling routes that can log failed executions. Zapier has a task history showing failed runs, but no native DLQ—use a custom webhook to a logging service.

Circuit breaker pattern

A circuit breaker prevents your workflow from repeatedly calling a failing service. It has three states:

• Closed (normal): Requests pass through. Failures are counted.
• Open (tripped): After a failure threshold is reached (e.g. 5 failures in 1 minute), the circuit opens. All requests fail immediately without calling the downstream service. This prevents cascading failures and reduces load on the failing service.
• Half-open (testing): After a timeout (e.g. 30 seconds), one test request is allowed through. If it succeeds, the circuit closes. If it fails, the circuit reopens.

In automation: Implement circuit breakers when your workflow calls APIs that occasionally experience extended outages. In n8n, use a Code node that checks a failure counter (stored in Redis or a database) before making the API call. In Make, use a Data Store to track recent failures and a Router to skip the API call when the circuit is open.

Retry policies by error type

Not all errors should be retried. A clear retry policy prevents wasted resources and infinite loops:

| Error | Retry? | Strategy | |-------|--------|----------| | 400 Bad Request | No | Fix the request payload | | 401 Unauthorised | Once (refresh token) | Refresh OAuth token, retry once | | 403 Forbidden | No | Check permissions | | 404 Not Found | No | Resource does not exist | | 409 Conflict | No (treat as success) | Idempotent retry already processed | | 422 Unprocessable | No | Fix the data | | 429 Too Many Requests | Yes | Wait for Retry-After, then retry | | 500 Internal Server Error | Yes (with backoff) | Exponential backoff, max 5 retries | | 502 Bad Gateway | Yes (with backoff) | Exponential backoff | | 503 Service Unavailable | Yes (with backoff) | Exponential backoff, check status page | | Network timeout | Yes (with backoff) | Exponential backoff |

Partial failure handling

In multi-step workflows, a single step failure should not discard the work done by previous steps. Strategies:

• Checkpoint pattern: Save intermediate results after each successful step. On retry, resume from the last checkpoint.
• Compensation pattern: If Step 3 fails and Steps 1-2 have side effects, run compensating actions (e.g. delete the record created in Step 1). This is the "saga" pattern from distributed systems.
• Accept at-least-once: Design each step to be idempotent. On retry, steps that already succeeded are harmless no-ops. This is the most practical approach for most automation workflows.

Monitoring and debugging

Key metrics to track

• Request success rate: Percentage of API calls that return 2xx. Target: above 99.5%.
• Latency (p50, p95, p99): How long API calls take. p95 above 5 seconds indicates a problem.
• Error rate by type: Distinguish 4xx (your problem) from 5xx (their problem) and rate limits (capacity problem).
• Webhook delivery success rate: Percentage of webhooks acknowledged with 200. Below 99% indicates endpoint issues.
• Queue depth: How many events are waiting to be processed. Growing depth means processing cannot keep up.
• Retry rate: How often retries occur. High retry rates indicate instability.
• DLQ depth: How many events have exhausted retries. Non-zero means investigation is needed.

Monitoring tools

• **Datadog** — Full observability platform with API monitoring, custom metrics, alerting, and dashboards. Integrates with most cloud providers and automation platforms.
• **Better Stack** — Uptime monitoring and incident management. Useful for monitoring webhook endpoint availability.
• **Hookdeck** — Purpose-built webhook infrastructure. Provides delivery monitoring, retry management, and debugging tools for webhooks specifically.
• **Svix** — Webhook sending infrastructure (if you are building a system that sends webhooks). Includes delivery tracking and retry management.
• **Platform-native logs:** n8n execution history, [Make](https://make.com) scenario logs, [Zapier](https://zapier.com) task history. First-line debugging for workflow-level issues.

Debugging failed API calls

When an API call fails in your workflow:

1. Check the status code — Determine if it is a client error (4xx) or server error (5xx). This determines whether to fix your request or wait and retry. 2. Read the response body — Most APIs return error details in the JSON body. Stripe returns `error.type`, `error.code`, and `error.message`. HubSpot returns `category` and `message`. 3. Check rate limit headers — If 429, look for `Retry-After`, `X-RateLimit-Remaining`, or similar headers. 4. Verify authentication — Expired tokens, revoked keys, and changed permissions are common causes. Test the credential independently. 5. Check the API status page — Stripe status, Twilio status, HubSpot status. If the service is degraded, wait. 6. Compare with documentation — Verify that your request matches the current API version. API fields change, endpoints deprecate, and payload formats evolve.

Performance optimisation

Batching

Instead of making 100 individual API calls to create 100 contacts, use the API's batch endpoint to create them in a single request. HubSpot batch create supports up to 100 contacts per batch request. Stripe does not have batch endpoints for most operations—but you can batch your requests client-side using concurrency control.

Batching reduces total request count, avoids rate limits, and improves throughput. Check each API's documentation for batch endpoints and their limits.

Caching

Cache API responses that do not change frequently. If your workflow looks up product details for every order, cache the product catalogue and refresh it hourly instead of querying per order. Storage options: Redis (fast, TTL-based), database table, or workflow platform variables.

Cache invalidation strategies:

• Time-based (TTL): Cache expires after a fixed period. Simple but may serve stale data.
• Event-based: Webhook notification invalidates the cache. More complex but always fresh.
• Hybrid: TTL for baseline freshness, webhook for immediate invalidation of changed records.

Connection pooling and keep-alive

For high-volume workflows making many requests to the same API, reuse HTTP connections instead of establishing a new connection for each request. HTTP keep-alive (Connection: keep-alive header) maintains the TCP connection between requests, eliminating the overhead of TLS handshake (which can add 100-300ms per request).

In workflow platforms, connection reuse is typically handled by the platform. In custom integrations, use HTTP client libraries that support connection pooling (axios with an agent, got with keep-alive, Python's requests.Session).

Parallel execution

When your workflow needs to call multiple independent APIs (e.g. fetch customer from CRM, fetch order from e-commerce, fetch payment from Stripe), make the calls in parallel instead of sequentially. This reduces total execution time from the sum of all call durations to the duration of the slowest call.

In n8n, use the SplitInBatches node with parallel execution. In Make, use parallel routes from a Router module. In custom code, use `Promise.all()` (JavaScript) or `asyncio.gather()` (Python).

Pagination handling

APIs that return lists (customers, orders, events) use pagination. Common patterns:

• Offset-based: `?page=2&limit=50` — Simple but slow for large datasets (the database must skip rows).
• Cursor-based: `?after=cursor_abc123&limit=50` — More efficient for large datasets. The cursor is an opaque token pointing to the next page.
• **Link header:** The response includes a `Link` header with the URL for the next page. GitHub uses this pattern.

For automation: always paginate through the entire result set when syncing data. Do not assume the first page contains everything. Implement a loop that continues until no more pages are returned.

Testing APIs and webhooks

Local development tools

• **Postman** — GUI tool for building, testing, and documenting API requests. Excellent for exploring APIs before building workflows.
• **ngrok** — Exposes your local server to the internet, allowing webhook providers to deliver events to your development machine.
• **webhook.site** — Free tool that gives you a URL to receive and inspect webhook deliveries. Useful for understanding payload structure.
• **httpbin.org** — Returns request data back to you. Useful for testing headers, authentication, and payload formatting.

Provider test modes

Most API providers offer test or sandbox environments: Stripe test mode (separate API keys, fake payment methods), PayPal sandbox, Twilio test credentials (free, does not send real messages). Always develop and test against these environments before connecting to production.

Webhook testing strategy

1. Use webhook.site to capture and inspect the payload structure 2. Use ngrok to route webhook deliveries to your local workflow 3. Trigger test events using the provider's test mode 4. Verify your handler responds with 200 within the timeout window 5. Verify HMAC signature validation works (test with correct and incorrect secrets) 6. Verify deduplication works (send the same event twice) 7. Verify error handling works (simulate downstream failures) 8. Test retry behaviour (respond with 500 and observe the provider's retry schedule)

Troubleshooting reference

Missing webhook events

• Verify the webhook URL is correct and publicly accessible
• Verify you are subscribed to the correct event types
• Check for firewall rules or WAF (web application firewall) blocking webhook requests
• Check the provider's webhook delivery logs for error details
• Verify your endpoint is responding within the timeout window
• Check for SSL certificate issues (expired, self-signed, or intermediate certificate missing)

Duplicate processing

• Implement event ID deduplication (Redis or database)
• Check for duplicate webhook registrations in the provider's dashboard
• Review retry behaviour—slow responses cause retries that create duplicates
• Use idempotency keys for all create operations

Rate limit issues

• Implement exponential backoff with jitter
• Use batch endpoints where available
• Queue requests and process at a controlled rate
• Check if a higher API tier or plan provides higher limits
• Distribute requests across time (avoid burst patterns)
• Consider caching to reduce redundant requests

Authentication failures

• Verify the credential is valid and has not expired
• Check that OAuth tokens are being refreshed before expiration
• Verify the credential has the correct scopes/permissions
• Check if the API key has been rotated without updating the workflow
• Verify the API endpoint URL matches the credential environment (test vs production)

Experts on LogicLot can build custom API and webhook integrations for any use case. Post a Custom Project for tailored work.