Building Resilient Systems: Error Handling and Retries in Triggers.do

In a perfect world, APIs always respond, networks never drop packets, and services are never down for maintenance. In the real world, distributed systems are inherently unpredictable. For any platform built on event-driven automation, the question isn't if an error will occur, but when—and more importantly, what happens next?

A simple "fire-and-forget" webhook might be easy to set up, but it's brittle. A single transient network glitch or a downstream service deployment can cause a critical event—like a new customer order or a vital system alert—to be lost forever. This is where building for resilience becomes non-negotiable.

Triggers.do isn't just about starting workflows; it's about ensuring they complete successfully. It provides a robust, developer-friendly toolkit for building fault-tolerant systems from the ground up, turning potential failures into managed outcomes. Let's explore how.

Beyond "Fire-and-Forget": The Need for Intelligent Error Handling

The core of any event-driven system is the trigger: LISTEN. FILTER. EXECUTE. But what happens if the "EXECUTE" step fails?

Consider a standard webhook-based process:

1. Stripe sends a webhook for a new high-value order.
2. Your custom endpoint receives it.
3. Your code attempts to call three separate internal services: Inventory, Shipping, and CRM.

What happens if the Shipping API is temporarily unavailable? The entire process halts. Without a sophisticated retry mechanism, that order is now in an inconsistent state, requiring manual intervention. Triggers.do solves this by baking in resilience as a first-class feature.

Intelligent Retries with Exponential Backoff

Bombarding a struggling service with immediate, repeated requests is a recipe for disaster. It can exacerbate the problem, leading to a cascading failure. The smarter approach is to use retries with exponential backoff.

This strategy means that if a workflow step fails, Triggers.do will wait a short period before trying again. If it fails a second time, it waits a longer period, and so on. This gives the downstream service time to recover without being overwhelmed.

You can define this behavior directly in your trigger configuration, turning complex infrastructure code into a simple, declarative property.

import { Trigger } from 'triggers.do';

const newOrderTrigger = new Trigger({
  name: 'Resilient High-Value Order Fulfillment',
  event: 'order.created',
  source: 'stripe-webhook',
  filter: {
    condition: 'event.data.amount > 100'
  },
  // Define a retry policy for the handler
  retry: {
    policy: 'exponential',
    maxAttempts: 5,        // Try up to 5 times
    initialDelay: '5s'     // Start with a 5-second delay
  },
  handler: async (event) => {
    // This handler will be retried on failure
    console.log(`Starting workflow for order: ${event.data.orderId}`);
    // await callToShippingAPI(event.data); // This might fail
    return {
      workflow: 'order-processing',
      input: event.data
    };
  }
});

With this configuration, a temporary hiccup in your order-processing workflow won't cause a catastrophe. Triggers.do manages the retry loop automatically, ensuring the process eventually completes.

The Safety Net: Dead-Letter Queues (DLQs)

But what if an error is permanent? A malformed customer ID or a bug in the code won't be fixed by retrying. For these situations, you need a safety net to prevent data loss.

After all retry attempts are exhausted, Triggers.do can automatically route the original event and the details of the failure to a Dead-Letter Queue (DLQ). A DLQ is a dedicated holding area for events that could not be processed successfully.

This provides two massive benefits:

1. No Event is Lost: The critical information is safely stored, not discarded into the void.
2. Analyzability and Replayability: Your development team gets a clear queue of failed jobs, complete with the original event payload and error logs. They can analyze the root cause, deploy a fix, and potentially re-process the events from the DLQ.

This transforms a system failure from a data-loss incident into a recoverable, auditable operational task.

Granular Control: Differentiating Error Types

Triggers.do gives you the power of code, meaning you can handle errors with nuance. Not all errors are candidates for a retry. A 503 Service Unavailable error is transient and perfect for a retry, but a 400 Bad Request indicates a problem with the data itself, and retrying will only yield the same result.

Within your handler function, you can use standard try...catch blocks to inspect errors and decide on a course of action. You can purposefully throw an error to trigger the platform's retry policy or catch an error to handle it gracefully without failing the entire run.

// ... inside the handler function
handler: async (event) => {
  try {
    // Call a service that might fail
    const result = await inventoryService.reserveItem(event.data.sku);
    return { workflow: 'process-shipment', input: result };
  } catch (error) {
    // Check for a non-recoverable error
    if (error.statusCode === 404) {
      console.error(`Item not found, not retrying: ${event.data.sku}`);
      // Send to a manual review workflow instead
      return { workflow: 'manual-inventory-check', input: event.data };
    }
    // For all other errors (like 5xx), re-throw to trigger a retry
    throw error;
  }
}

This level of control allows you to build sophisticated, intelligent workflows that react appropriately to the specific nature of any problem.

Business-as-Code Starts with Resilience

True business automation isn't just about connecting A to B. It's about creating reliable, self-healing systems that you can trust with your most critical processes. By providing built-in retries, dead-letter queues, and the granular control of code, Triggers.do lets you stop worrying about failure modes and start focusing on your business logic.

Ready to build more robust, resilient event-driven workflows? Explore Triggers.do and see how true Business-as-Code begins.