How to Create AI Workflows: Ultimate Guide for Agency Owners and Freelancers

Artificial Intelligence (AI) workflows are becoming indispensable for businesses looking to automate repetitive tasks, improve efficiency, and deliver innovative solutions to their clients. Whether you’re an agency owner or a freelancer, understanding how to create AI workflows can open up new opportunities to expand your service offerings and attract high-value clients.

In this blog, we’ll walk you through the steps to create effective AI workflows, with a focus on their practical application. We’ll also include a detailed example where we demonstrate an AI-powered workflow to include custom tools into an online checkout process.

What Is an AI Workflow?

An AI workflow is a series of automated steps where data is processed, analyzed, and acted upon using artificial intelligence. These workflows integrate machine learning (ML) models, data processing pipelines, and automation frameworks to solve specific business problems. AI workflows can range from simple tasks like document classification to more complex use cases like predictive analytics or real-time personalization.

For agencies and freelancers, AI workflows can automate tedious processes like email, text or image generation, helping to save time while boosting productivity.

How are AI workflows different from AI agents?

AI workflows and AI agents differ primarily in their purpose and design. AI workflows are structured, predefined sequences of tasks or processes aimed at achieving specific objectives, often integrating multiple tools and systems, and require humans to define their flow.

In contrast, AI agents are autonomous systems that continuously learn, adapt, and make decisions in real-time, often using reinforcement learning or other self-improving methods. Workflows excel in predictable, repeatable tasks, while agents are better suited for dynamic, evolving environments requiring independent problem-solving. AI workflows are typically limited to the parameters set by their designers, while AI agents exhibit more flexibility and goal-oriented behavior beyond static instructions. Together, they complement one another, with workflows often being a component of an AI agent's broader strategy.

Why Should Agency Owners and Freelancers Invest in AI Workflows?

AI workflows are not just a buzzword—they deliver many benefits, including:

  • Increased Efficiency: Automate time-consuming tasks and let your team focus on high-value activities.
  • Improved Accuracy: Reduce human error by leveraging AI-driven tools that excel in repetitive or data-intensive operations.
  • Scalability: Handle large-scale operations effortlessly with AI models that can process massive datasets in real-time.
  • Enhanced Client Offerings: Attract new clients by offering cutting-edge AI solutions as part of your service portfolio.
  • Cost Savings: Lower operational costs by automating routine processes that would otherwise require manual effort.

Key Steps to Create AI Workflows

Here’s a step-by-step guide to building an AI workflow for your business:

Step 1: Define the Workflow Objective

The first step is to clearly define the problem you’re trying to solve. For example, are you looking to automate a process, analyze data, or enhance decision-making? Identify the inputs, outputs, and desired outcomes of the workflow.

Step 2: Collect and Preprocess Data

AI workflows thrive on data. Collect relevant data and preprocess it by cleaning, normalizing, and transforming it into a format suitable for your AI model or algorithm.

Step 3: Choose the Right AI Tools and Models

Select AI tools, frameworks, and machine learning models that align with your workflow’s requirements. For example, TensorFlow, PyTorch, and Scikit-learn are excellent for building custom models, while pre-trained APIs like OpenAI’s GPT or Hugging Face can handle tasks like language understanding.

Step 4: Develop and Automate the Workflow

Once the AI model is trained or selected, create a pipeline that automates the workflow. Use tools like FastAPI, Apache Airflow, or custom Python scripts to orchestrate the various steps.
Step 5: Test and Optimize

Run test scenarios to ensure the workflow operates as expected. Monitor performance metrics, fine-tune your model, and optimize the pipeline for speed and reliability.

Step 6: Deploy and Monitor

Finally, deploy your workflow in a production environment. Use monitoring tools to track its performance and quickly address any issues that arise.

Example: AI Workflow for Containerizing a CLI Tool

To illustrate how AI workflows can be implemented, let’s consider a practical use case: automating the containerization of a CLI tool using a FastAPI app.

Problem

Imagine you’re building an online checkout process where customers can integrate custom CLI tools to handle tasks like inventory management, data analysis, or generating reports. You need a workflow that:

  • Automatically researches the CLI tool’s dependencies and command-line arguments.
  • Creates a Docker container with a FastAPI app exposing the CLI tool as an API.
  • Makes the Docker image available for deployment.

Solution

Here’s how an AI workflow can achieve this:

Step 1: Input the CLI Tool Name

The workflow begins with the user providing the name of the CLI tool they want to containerize (e.g., my-cli-tool).

Step 2: Research Dependencies and Arguments

The workflow leverages an AI model to:

  • Search online documentation for the CLI tool.
  • Extract relevant command-line arguments and dependencies.
  • Validate the accuracy of the extracted information.

Step 3: Generate a Dockerfile

The AI generates a Dockerfile that includes:

  • The necessary base image (e.g., python:3.12).
  • Installation commands for dependencies.
  • Integration of the CLI tool into a FastAPI app.

Step 4: Create the FastAPI App

The AI creates a Python script with FastAPI routes to:

  • Accept user inputs for the CLI tool’s arguments.
  • Run the CLI tool using subprocess and return the output as an HTTP response.

Benefits of This AI Workflow

  • Automation: Eliminates the need for manual research and Dockerfile creation.
  • Scalability: Handles multiple CLI tools with minimal effort.
  • Integration: Easily integrates with existing online platforms or APIs.
  • Customizable: Supports advanced configurations for unique use cases.

How Your Agency Can Leverage AI Workflows

As an agency or freelancer, you can offer services to clients who need tailored AI workflows for their businesses. Examples include:

  • Automating e-commerce tasks (e.g., product recommendations, inventory management).
  • Enhancing SaaS platforms with AI-driven features.
  • Streamlining DevOps with AI-powered CI/CD pipelines.

By demonstrating expertise in AI workflows, you position your agency as a go-to partner for innovation-driven solutions.

Conclusion

Creating AI workflows doesn’t have to be complex. By following the steps outlined in this blog and leveraging tools like FastAPI and Docker, you can develop efficient and scalable workflows that solve real-world problems. Whether you’re an agency owner or freelancer, investing in AI workflows will help you stay competitive and deliver exceptional value to your clients.

Which workflow would you like to automate? If you’re ready to take your projects to the next level, our team at adagger specializes in building custom AI workflows tailored to your business needs. Contact us today to learn how we can help!