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problem-solving January 20, 2026

How to Troubleshoot Failed Posts and Common API Errors with Mallary.ai

Introduction

Failed posts and API errors disrupt user experience, stall workflows, and create support overhead. If you’re integrating Mallary.ai into your app — whether for AI-driven content, automation, or analytics — understanding common causes of failed API calls and having a repeatable troubleshooting process is critical. This post walks through the most frequent failure modes, actionable steps to diagnose and fix them, and patterns to make your integration resilient. Along the way, you’ll see how Mallary.ai’s tools and features make troubleshooting faster and more reliable.

Common causes of failed posts with Mallary.ai

1. Authentication and permission issues

Symptoms: 401/403 responses, “invalid_api_key”, or “insufficient_scope” errors.

  • Check that you’re sending the correct API key in the Authorization header (Bearer <key>).
  • Verify the key hasn’t been rotated, revoked, or expired in the Mallary.ai dashboard.
  • Confirm the account has the required permissions or subscription tier for the endpoint you’re calling.

Mallary.ai tip: use the API key management panel to generate scoped keys and immediately test them with the in-dashboard request inspector.

2. Rate limiting

Symptoms: 429 Too Many Requests, intermittent failures under load.

  • Inspect response headers for rate limit info (e.g., X-RateLimit-Remaining, Retry-After).
  • Implement exponential backoff and jitter when retrying. Avoid hammering the API with synchronized retries.
  • Consider request batching or increasing concurrency limits if your use case needs higher throughput.

Mallary.ai tip: the dashboard shows real-time usage and limit thresholds so you can proactively scale or adjust traffic patterns.

3. Payload validation and schema errors

Symptoms: 400 Bad Request, “invalid_payload”, or descriptive error that pinpoints a missing/incorrect field.

  • Always set Content-Type: application/json for JSON payloads and ensure you’re sending valid UTF-8.
  • Validate your JSON against the expected schema before sending; common pitfalls include wrong field names, missing required keys, or incorrect data types.
  • Log the request body so you can replay the failing request during diagnosis.

4. Network timeouts and connection issues

Symptoms: request timeouts, connection reset, or intermittent failures in certain regions.

  • Differentiate client-side timeouts from server-side timeouts by checking your HTTP client logs and Mallary.ai response timings.
  • Increase timeout windows for long-running requests and implement graceful cancellation for user flows.
  • Use regional endpoints if Mallary.ai exposes them to reduce network latency.

5. Idempotency and duplicate posts

Symptoms: duplicate side effects when retries succeed after a partial failure.

  • Use idempotency keys for non-idempotent operations so retries won’t create duplicates.
  • Store idempotency keys and their responses in your database to avoid repeated processing.

Mallary.ai tip: the API accepts idempotency headers — include them on your create/update calls to make retries safe.

6. Webhook delivery failures

Symptoms: missed webhook events, 5xx responses from your endpoint, signature verification failures.

  • Ensure your webhook endpoint responds with a 2xx within the required time window.
  • Validate webhook signatures using Mallary.ai’s signing secret to prevent spoofing.
  • Implement a dead-letter queue (DLQ) to capture events that repeatedly fail processing.

Step-by-step troubleshooting workflow

When a post fails, follow a reproducible procedure to identify root cause quickly:

  1. Capture the failure — collect request and response details: URL, headers, body, response status, body, and response headers.
  2. Check Mallary.ai request logs — the dashboard surfaces raw request/response records and request IDs to correlate with your logs.
  3. Reproduce locally — run the same request from a local environment or cURL to see if it fails reproducibly.
  4. Validate credentials and scopes — confirm the API key used matches the environment (test vs production) and has correct scope.
  5. Inspect payload and headers — validate JSON schema, required headers, and content types.
  6. Follow retry semantics — if transient errors happen, retry with backoff and idempotency keys.
  7. Monitor — set alerts on error rate, 5xx spikes, and latency regressions so you can react before customers complain.

Practical examples and helpful checks

When you see an error, the exact message often tells you what to do. For instance:

{ "error": "invalid_api_key", "message": "The API key provided is not valid." }

Useful quick checks:

  • Is the Authorization header present and correctly formatted?
  • Are you using the right environment (sandbox vs production)?
  • Is the request body too large? (Check if you’re hitting size limits)
  • Does the response include a request-id header you can send to support?

Automated resilience patterns to prevent failures

Designing for failure reduces firefighting later. Implement these patterns:

  • Retries with exponential backoff and jitter: prevents retry storms and respects rate limits.
  • Queueing and background processing: offload non-blocking API calls to a worker queue and process retries asynchronously.
  • Circuit breaker: temporarily stop calling Mallary.ai when a threshold of failures is reached to allow recovery.
  • Idempotency: ensure create/update calls include idempotency keys to make retries safe.
  • Graceful degradation: show cached content or a lightweight fallback when API features are temporarily unavailable.

Troubleshooting webhooks and delivery problems

Verify and replay

When a webhook fails:

  • Check Mallary.ai’s webhook delivery logs in the dashboard to see status codes, response times, and payloads.
  • Replay failed webhook events from the dashboard or your DLQ after fixing your endpoint.

Secure and validate signatures

  • Compute the HMAC or signature using the shared secret and compare against the signature header Mallary.ai adds.
  • Protect against replay attacks by checking timestamps included in the signature payload.

What to provide when contacting Mallary.ai support

If you need help from Mallary.ai support, collect this information to speed resolution:

  • The exact API endpoint, timestamp, and the request-id from the Mallary.ai response headers.
  • Full request and response (headers and bodies), with sensitive data redacted as needed.
  • A short reproduction script (curl or minimal code sample) demonstrating the issue.
  • Relevant environment details: SDK version, HTTP client, region, and whether the issue is intermittent or reproducible.

Mallary.ai engineers use request IDs and dashboard logs to trace and resolve issues quickly — the more context you provide, the faster they can act.

Conclusion

Failed posts and API errors are inevitable, but they don’t have to derail your product. By methodically checking authentication, payloads, rate limits, and network conditions, and by implementing resilience patterns like retries with idempotency and background processing, you can dramatically reduce failure impact. Mallary.ai helps by exposing detailed request logs, usage dashboards, webhook replay tools, and SDKs that implement best practices out of the box — saving you time when diagnosing and preventing issues.

Ready to build more reliable integrations and reduce failed posts? Sign up for free today and explore Mallary.ai’s developer tools, logs, and best-practice guides to get started.