Owning the Long Tail of Automation: Designing CI/CD Systems That Clean Up After Themselves
A few years ago, while optimizing our AWS deployment workflows, I identified a systemic issue that had both reliability and cost implications if left unchecked.
At the time, I was deploying a .NET Core application using build artifacts directly, no Docker, no container orchestration. The CI pipeline ran tests and validations, and once those passed, the CD pipeline built the application and pushed the artifacts to Amazon S3. Artifacts were tagged and separated by environment (staging and production), and S3 also served as our rollback mechanism, allowing us to redeploy a previous version quickly if a release failed. Nexus was considered, but S3 was chosen for its simplicity and tight integration with AWS, without an additional infrastructure overhead.
The system worked, but like many early-stage automation decisions, it had a long-term side effect.
We had 14 applications at the time, each with both staging and production environments. Over a few months, every environment accumulated 20+ artifacts. That meant well over 560 artifacts stored already, and growing linearly with every deployment. There was no retention policy, no cleanup mechanism, and no visibility into how fast this was scaling. While S3 storage is relatively inexpensive, at this rate we were heading toward hundreds to thousands of stale artifacts within a year, introducing unnecessary cost, clutter, and operational risk during incident response or rollbacks.
From an SRE perspective, this violated two principles I care deeply about:
Automation should not introduce long-term operational debt
Systems must be self-maintaining, not just self-deploying
Rather than relying on manual cleanup or tribal knowledge, I designed an automated, low-risk solution.
I implemented an AWS Lambda function with least-privilege IAM access, scoped strictly to artifact buckets. The function was triggered monthly by a cron job and on execution, the function:
Enumerated all artifact S3 buckets across the account.
Detected whether new artifacts had been added since the last run, exiting early if no changes were found.
Sorted artifacts by creation timestamp.
Retained only the latest 10 artifacts per environment, deleting all older ones.
This approach preserved rollback safety while enforcing a clear retention policy. It also ensured deterministic behavior, no deletions unless a newer artifact existed, and no assumptions baked into deployment pipelines.
The impact was immediate and measurable:
~70–80% reduction in stored artifacts across environments
Predictable, bounded S3 storage growth
Elimination of manual cleanup tasks
Cleaner rollback workflows during incidents
Long-term cost savings with zero developer involvement
Just as important as the code was the leadership follow-through. I documented:
The original problem and risk assessment
The retention logic and safeguards
IAM design decisions
Operational behavior under different deployment scenarios
This ensured the solution was understandable, auditable, and maintainable by the wider team.
From an SRE and leadership standpoint, this wasn’t just about cleaning up S3 buckets, it was about owning the full lifecycle of automation, anticipating second-order effects, and leaving systems better than I found them. Once deployed, this became another class of operational concern that engineers simply didn’t have to think about again, which, to me, is the mark of effective DevOps and SRE work.
Image credit: www.sonassystems.com
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