Power Platform Worst Case – Day 6: The YAML File That Ate My Solution

Power Platform Worst Case – Day 6: The YAML File That Ate My Solution

The Calm Before the Storm

It started innocently enough. I was working in the Microsoft Power Platform universe—building a solution with Power Automate flows, Power Apps canvas apps and some data stored in Dataverse. Day 6 was supposed to be the “integration day”: bring together the flows, package the solution, deploy to UAT, test the end-to-end scenario. Everything seemed on track.

I had versioned the solution, locked the stable build, and created a YAML pipeline in Azure DevOps to automate deployment. My thinking: “If I automate this, next time I’ll save time and avoid manual errors.” The YAML looked clean. The steps were clear. Build → export solution → import into UAT → publish. A few parameters. Easy.

But the thing about these automation files is: they betray you at the worst possible moment.

When YAML Turns from Helper to Villain

About halfway through the UAT import step, the deployment failed. On investigation: the pipeline had overwritten the UAT environment’s solution with an older version—effectively rolling back several days of development, deleting a key table, and causing several flows to break. In short: the YAML pipeline ate my solution.

Here’s how it happened: in the YAML I had a line that exported the “latest” solution package, but because I had forgotten to bump the version number, the “latest” was actually the previous stable build. Worse: the import task included a “force overwrite” option. So the old version replaced the new work. All because the YAML assumed too much. A tiny mis-indentation, or an out-of-date parameter, and the chain reaction began.

The reason this scenario is so painful in the Power Platform world: solutions in Dataverse are versioned, but not always clearly flagged in pipelines; flows and apps inside the solution may reference tables, columns or connections that the older version doesn’t contain; and UAT environments often aren’t backed up with the level of rigor we’d expect for production. The YAML file amplified the mistake by automating the wrong steps.

What Went Wrong – A Deep Dive

Assumptions hidden in YAML

My pipeline assumed:

• The exported solution package is always the correct one.
• The version numbering is enforced.
• The UAT environment is safe to overwrite.
• The import step will not break dependencies. These looked fine on paper—but the YAML gave them the appearance of guarantee. YAML files are deceptively simple: the mere indentation or missing parameter can change behavior. In fact many engineers complain that “YAML is killing your production systems.

Lack of version control guardrails

I had version control on the solution itself, but the pipeline didn’t enforce version bumps or validate that the exported package matched the latest commit. The YAML script simply took “export solution” from the artifact folder—without checking whether that artifact matched the right branch. The result: I ended up deploying a build from two days ago.

Environment risks

The UAT environment did not have a fresh backup. Because I was confident the pipeline was working, I skipped verifying a restore point. When the wrong solution replaced the correct one—and flows referencing missing tables failed—I had limited recourse. Restoring to pre-import state wasn’t trivial.

Over-automation without safe-net

I’d enabled “force overwrite” in the import step of the YAML. That’s what turned “wrong version” into “broken environment”. Automation is wonderful—but without human checkpoints, it becomes a wrecking ball when mis-configured.

Lessons Learned and Best Practices

Enforce versioning explicitly

Don’t rely on “latest” or “export whatever is in the folder”. Either include a version parameter in your YAML (e.g., solutionVersion: $(Build.BuildNumber)) or add a step that verifies the package version against the commit tag. Make versioning explicit, not implicit.

Add validation steps in the pipeline

Before importing into UAT, add a “dry run” or “validate solution” step. Check for missing dependencies, missing tables or flows referencing removed entities. If your platform supports it, run a comparison between the target solution and the package to catch drops or changes.

Back up your target environment

Automated deployment doesn’t mean “set it and forget it”. Before any import that overwrites, ensure a backup or snapshot exists. If something goes wrong, you don’t want to be looking at hours of manual fix-up.

Use fail-safe settings

Avoid “force overwrite” by default. The YAML should flag a warning or stop if the target version is lower than the package version, or if the package version mismatches a committed tag. If you must use overwrite, wrap it behind an explicit confirmation or gating step.

Document the YAML pipeline logic

Often YAML files accumulate logic over time and nobody remembers exactly what they do. Maintain documentation—what each step does, what assumptions it makes. That helps when you revisit the pipeline months later. YAML may look simple, but its semantics hide complexity. People online have described the pain of dealing with YAML pipelines in CI systems—and warned about how quickly things go sideways.

Recovery, Reflection, and Moving On

After the deployment meltdown, I spent the afternoon restoring UAT from the most recent snapshot, re-deploying the correct solution manually, and rebuilding a few flows that had broken. The cost: about five hours of dev/test time, a disappointed QA team, and a salted feeling of “automation betrayed me”.

However, the silver lining: this event forced us to strengthen our deployment processes. We added a YAML gating step that requires a developer to confirm the version and environment before importing. We also configured our solution exports to include version metadata and created an artifact repository for each build. We even added a simple PowerShell script that warns if the version in the package is lower than the version currently in the target environment.

On a deeper level, I learned humility. Automation is seductive—but it’s not infallible. The file that eats your solution doesn’t care about your deadlines or confidence. It just follows its logic. If that logic is wrong, you’re in for a bad time.

Respect the YAML, Don’t Fear It

In the world of the Power Platform, where solutions, flows, tables and connections are interwoven, the deployment process matters just as much as the development. A single mis-configured pipeline file can undo days of work in minutes. The “YAML file that ate my solution” was a wake-up call: automation can accelerate success—but also amplify mistakes.

Respect your YAML pipelines. Treat them like code. Version them, test them, review them. And above all, assume they will do exactly what you tell them—not what you intend them to do. Because when intent and instruction diverge, the YAML wins.

Source: Power Platform Worst Case – Day 6: The YAML File That Ate My Solution