Introducing Agentic Workflows: The Next Evolution of Background Processing

Your application feels sluggish. Users are staring at loading spinners. Your servers are struggling under the load of long-running tasks like processing video uploads, generating end-of-month reports, or sending bulk emails. As a developer, you know the classic solution: offload this work to a background worker.

For years, this has been the standard playbook. You set up a message queue like RabbitMQ or Redis, write a separate worker service to consume tasks from that queue, and deploy it all to a new set of servers. It works, but it’s a heavy lift. You’re now managing complex infrastructure, wrestling with auto-scaling policies, and writing boilerplate code for retries and error handling.

What if there was a better way? What if you could get all the power of asynchronous task processing without the infrastructural headache? And what if you could take it a step further, orchestrating not just single jobs, but intelligent, multi-step processes?

Welcome to the next evolution of background processing: Agentic Workflows, made simple with worker.do.

From Clunky Queues to a Simple API

The core promise of a background worker is to make your main application faster and more responsive by handling heavy lifting elsewhere. But traditional setups often trade one kind of complexity for another. You’re no longer blocking the main thread, but you’re now a part-time DevOps engineer.

worker.do reimagines this entire experience. We believe that offloading a task should be as simple as an API call.

Instead of building and maintaining a fleet of servers and message brokers, you simply tell our platform what you need done. Consider this simple example for enqueuing a video processing job:

import { Dô } from '@do/sdk';

// Initialize the .do client with your API key
const dô = new Dô(process.env.DO_API_KEY);

// Enqueue a new job to be processed by a worker
async function queueVideoProcessing(videoId: string) {
  const job = await dô.worker.enqueue({
    task: 'process-video',
    payload: { 
      id: videoId,
      format: '1080p',
      watermark: true
    }
  });

  console.log(`Job ${job.id} enqueued successfully!`);
  return job.id;
}

With a few lines of code, you've handed off a complex task. Behind the scenes, the worker.do Agentic Workflow Platform takes care of everything else:

• Instant & Automatic Scaling: As your job queue grows, we automatically spin up more workers to handle the load. When the queue is empty, we scale down to zero. You get maximum processing power when you need it and save costs when you don’t.
• Built-in Resilience: Network blip? Temporary API outage? worker.do automatically retries failed jobs with a configurable exponential backoff strategy, ensuring that transient errors don't become critical failures.
• Zero Infrastructure: You manage your code. We manage the servers, queues, and scaling logic. This is background task processing as a service.

Beyond the Job Queue: The Power of Agentic Workflows

Simplifying job queues is a massive leap forward, but it’s just the beginning. The true power lies in moving from single, fire-and-forget tasks to coordinated, intelligent workflows. This is what we call an Agentic Workflow.

An Agentic Workflow is a process where an "agent" (a worker) can orchestrate a series of steps, make decisions, and interact with other services to complete a complex objective. It turns your backend processes from a simple to-do list into a smart, autonomous system.

Let's revisit our video processing example. A simple job queue might just transcode a video. An Agentic Workflow can manage the entire pipeline:

1. Trigger: A user uploads a new video.
2. Agent: process-video-pipeline is invoked.
3. Step 1: Transcode. The agent initiates multiple, parallel sub-tasks to create 1080p, 720p, and 480p versions of the video.
4. Step 2: Enrich. While transcoding, another sub-task calls an AI service to generate subtitles from the audio.
5. Step 3: Brand. Once transcoding is complete, an image processing task applies a watermark to each video version.
6. Step 4: Finalize. The agent waits for all sub-tasks to complete successfully. It then updates the video's status in your database and stores the new URLs.
7. Step 5: Notify. Finally, it triggers a separate send-notification agent to email the user that their video is ready.

This entire complex, multi-step, parallel process is defined as a single workflow. You don't enqueue five different job types; you invoke one agent and let it handle the orchestration. This is how you turn complex background processes into simple, manageable Services-as-Software.

Common Use Cases for Agentic Workflows

Any long-running or resource-intensive process is a perfect candidate for worker.do. This is especially true for tasks that involve multiple steps or conditional logic.

• ETL & Data Pipelines: Extract data from multiple APIs, transform it, run it through an analysis model, and load the results into your data warehouse.
• AI Model Inference: Run batch predictions on large datasets without tying up your production servers.
• E-commerce Order Fulfillment: Process a payment, reserve inventory, call a shipping provider's API, and send a confirmation email—all within one resilient workflow.
• Scheduled Tasks (Cron Jobs): Generate nightly reports, run database cleanup routines, or send weekly digest emails without managing a single cron tab.

Stop Building Queues, Start Building Workflows

The era of manually managing background worker infrastructure is over. The future of scalable, reliable backends is not just about offloading tasks—it's about orchestrating intelligent processes.

By providing a simple API for incredibly complex work, worker.do lets you focus on building features, not managing queues. You get the benefits of a powerful asynchronous backend with the simplicity of a modern cloud service.

Ready to simplify your background processing and build your first Agentic Workflow? Get started with worker.do today.

Frequently Asked Questions

What is a background worker?
A background worker is a process that runs separately from your main application, handling tasks that would otherwise block the user interface or slow down request times. Common examples include sending emails, processing images, or generating reports.

How does worker.do handle scaling?
The .do platform automatically scales your workers based on the number of jobs in the queue. This means you get the processing power you need during peak times and save costs during lulls, all without managing any infrastructure.

What types of tasks are suitable for worker.do?
Any long-running or resource-intensive task is a great fit. This includes video encoding, data analysis, batch API calls, running AI model inferences, and handling scheduled jobs (cron).

How are job failures and retries managed?
.do provides built-in, configurable retry logic. If a job fails, the platform can automatically retry it with an exponential backoff strategy, ensuring transient errors are handled gracefully without manual intervention.