When I started working more deeply with AWS and DevOps, one thing became very clear to me. A lot of time goes into doing the same setup again and again. Different use cases, different names, but mostly the same services and patterns underneath. Every time, someone had to create repos, write build scripts, and make sure services were created in the right order.
I wanted to see if this could be simplified. Instead of starting with infrastructure or code every time, I started thinking — what if everything started with a simple request?
That thought pushed me to experiment with a request-driven pipeline that could handle most of the repetitive work on its own. This was not something I built for a client or delivery work. It was something I explored out of curiosity and frustration with manual steps.
What was not working for me
Before automation, the usual flow looked like this:
First, we would try to understand what services were needed.
Then repositories would be created manually, often by copying an existing one.
Build and deployment scripts were written or reused with small changes.
Finally, AWS services were deployed, but only after figuring out which one had to come first.
This worked, but it was slow. It also depended too much on people remembering the right sequence. If something was missed, the deployment failed and we had to go back and fix it.
Starting from a simple request
Instead of starting from infrastructure, I decided to start from inputs.
I created a simple form where someone could enter:
- Service names
- Type of AWS services required
- Basic configuration details
Once the form was submitted, it triggered a CI/CD pipeline. From that point on, there was no manual setup. The pipeline used the inputs as variables and replaced them dynamically in the code and templates.
This alone removed a lot of repeated effort.
How the pipeline behaved
At a high level, the pipeline did the following:
- Took the request inputs and validated them
- Created a repository with a predefined structure
- Generated build and deployment scripts
- Triggered resource creation using CloudFormation templates and AWS SDKs
One important thing I learned early was that sequencing mattered a lot. For example, API Gateway needed to exist before Lambda integrations could work properly. So the pipeline had to be very clear about the order in which resources were created.
Once I fixed the sequencing logic, deployments became far more predictable.
Things I didn’t expect
Some of the harder problems were not technical.
Input validation was tricky. If one variable was missing or incorrectly mapped, the pipeline would fail in a way that wasn’t always obvious. Debugging those failures forced me to improve error handling and logging.
I also had to think more carefully about flexibility. I didn’t want the pipeline to work for just one scenario. I wanted it to be reusable without becoming too complex.
What this taught me
This experiment changed how I look at automation.
It’s easy to focus on tools, but the real value came from designing the flow correctly. Once the design was right, the tools became secondary. I also realised how powerful small, well-structured inputs can be when they drive infrastructure through code.
Most importantly, it pushed me to think about consistency and guardrails instead of one-off solutions.
Closing thoughts
This started as a learning exercise, but it helped me understand how request-driven automation can simplify DevOps workflows in AWS. It made setups faster, more consistent, and easier to reason about.
More than anything, it changed how I approach automation — not as something that replaces people, but as something that removes repetitive work so teams can focus on better decisions.
About me
Hi I'm Nivetha Gopi - Sr. Solutions Architect with hands-on experience supporting, automating, and optimizing mission-critical workloads on AWS. I work closely with cloud infrastructure, DevOps, and automation, and strongly believes that growth comes not just from what we know, but from how effectively we figure out what we don’t.
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