Our original automation platform was organized entirely around "workflows."
Each workflow was a custom sequence of Python scripts glued together by routing rules defined in spreadsheets. Whenever a new business request landed on my desk, my natural, developer-instinct response was to build another workflow.
At first, this felt like progress.
Then, the script library started exploding. Interestingly, the number of workflows wasn’t growing because our business operations were suddenly getting more complex. It was growing because our platform had no way to express reuse.
Every New Workflow Looked Familiar
Once you build enough automation pipelines, a frustrating pattern emerges: the workflows look entirely different on paper, but the underlying code does the exact same thing.
One workflow might fetch incoming offers from an email attachment. Another might scrape them from a partner portal. A third might pull them from an API.
While the high-level operational flows differed, the individual execution steps were virtually identical. Yet, instead of reusing an existing script, the path of least resistance was always to copy-paste the code, tweak a few hardcoded variables, and save it as a new file.
Our codebase was slowly drowning in near-identical duplicates of the same core logic. We didn't have a business complexity problem; we had an abstraction problem. Our platform made duplication easy and reuse incredibly painful.
The Wrong Level of Abstraction
Organizing an automation engine around "workflows" seems like the obvious choice. But it turns out to be the wrong foundation.
Workflows are highly volatile. Business processes change constantly. Steps are reordered, new third-party integrations are swapped in, and compliance rules evolve.
What remains remarkably stable, however, are your capabilities:
- Fetch raw files.
- Validate schemas.
- Extract text.
- Generate embeddings.
- Send notifications.
These capabilities are the fundamental building blocks. Once you decouple them from any specific business process, a workflow ceases to be custom code. It becomes nothing more than a declarative map showing how these independent blocks connect.
This simple realization completely inverted our architecture.
From Script to Component
To make this work, we stopped treating Python scripts as standalone, ad-hoc programs. Instead, we transformed them into reusable components.
Under this new model, every script was required to declare its own manifest:
- What input arguments does it require?
- What data types does it return?
- What environment variables or credentials does it depend on?
- How should it be categorized in our system?
[Legacy Script] ───> Hardcoded parameters, fragile execution
[Modern Component] ───> Self-documenting manifest, typed inputs/outputs, isolated run
Instead of manually writing documentation for these parameters, the manifest was the documentation. When our application booted up, it automatically scanned the codebase, discovered these components, and registered their metadata.
Suddenly, we didn't need to write custom HTML forms or manage hardcoded dropdowns for our operational dashboard anymore. The administrative UI looked at the component's metadata and generated the configuration forms automatically.
Workflows Became Data
Once our scripts could describe themselves, we no longer had to write code to build a workflow.
A workflow transitioned from an imperative sequence of execution files into a purely declarative dataset. It became a collection of tasks, where each task simply referenced a reusable component and mapped its inputs to the outputs of previous tasks.
The execution engine became entirely domain-agnostic. It no longer knew—or cared—about "offers," "bank validations," or "publishing rules." It only understood:
- Tasks and their execution states.
- Direct acyclic graphs (DAGs) and dependencies.
- Routing conditions and error handling.
We successfully evicted business logic from the core orchestrator. The platform remained entirely generic, while the business processes lived purely in the configuration.
A New Definition of "Shipping a Feature"
This architectural pivot completely changed what it meant to add a new feature to our system.
Previously, supporting a new operational flow was an exhausting, multi-step chore. I had to write the core Python script, build a custom configuration screen, write validation logic, manually update documentation, and manually wire up database entries.
Today, adding a feature is almost entirely a declarative exercise:
- Write the capability: Implement the single-purpose Python component.
- Declare the manifest: Define its inputs, outputs, and validation rules in code.
- Deploy: The platform automatically registers the new capability, generates the configuration UI, and makes it instantly available to be dropped into any workflow.
The platform isn't getting larger or more complex as we scale. It is getting more generic. Every new capability we write increases the expressive power of our system without adding a single ounce of architectural bloat.
The Platform is the Product
At some point during this journey, the actual business workflows we were running stopped being the most interesting part of the project. We realized our primary engineering effort was no longer writing automation scripts—it was building an orchestration platform.
The core challenge shifted from writing business logic to designing robust abstractions that allowed scripts to be safely discovered, validated, scheduled, and monitored.
Individual workflows will always come and go as business needs pivot. But the platform remains.
And once you successfully transition to this model—where scripts are reusable capabilities and workflows are simply structured data—you run headfirst into a massive, classic systems engineering problem. Executing a single Python script sequentially is trivial. Executing hundreds of stateful, parallel tasks across complex DAGs while handling retries, race conditions, rate limits, and failure recovery is a different beast entirely.
In my next post, we’ll look at how we built a distributed, lightweight runtime designed to execute these declarative workflows safely and concurrently.