Enabling diagnostics in a YAML pipeline in Azure DevOps

When a pipeline run fails and the regular logs don't tell you enough, Azure DevOps has a built-in way to get more detail: system diagnostics. The classic way to turn it on is the "Enable system diagnostics" checkbox you get when you manually queue a run. But if your pipeline is defined in YAML and runs automatically on every push, there's no checkbox to click.

The checkbox approach

When you queue a pipeline manually, click Run pipeline and you'll see an Advanced options section with an Enable system diagnostics checkbox. Tick it, hit Run, and that single run gets verbose logging: purple-colored debug lines, extra detail on what each task is doing under the hood.

Remark: this only affects the run you're queuing. It doesn't persist, and it's useless for CI-triggered runs where nobody is manually clicking Run.

The YAML equivalent: system.debug

For YAML pipelines, the checkbox maps directly to a variable: system.debug. Set it to true and you get the exact same verbose logging, regardless of how the run was triggered.

variables:
  system.debug: true

That's it. Add this to your pipeline, and every run — manual, CI-triggered, scheduled — gets verbose logs.

What you actually get

With system.debug: true, tasks emit information that's normally suppressed: variable resolution, internal decision points, and resource utilization per step (disk, memory, CPU). Search the logs for Agent environment resources and you'll see entries like:

##[debug]Agent environment resources - Disk: D:\ Available 12342.00 MB out of 14333.00 MB, Memory: Used 1907.00 MB out of 7167.00 MB, CPU: Usage 17.23%

Handy if you suspect a step is running into resource constraints on the agent, not just a logic error in your task.

Remark: setting system.debug to true also sets Agent.Diagnostic to true automatically. On self-hosted agents (v2.200.0+) that gets you network diagnostics too, useful when a step is failing to reach an external endpoint and you're not sure if it's your task or the agent's network.

Don't leave it on permanently

Verbose logs are noisy. Every task dumps a lot more than you need on a normal green run, and it makes scanning logs for the one problem you're chasing harder, not easier. Treat system.debug: true as something you add while debugging and remove afterwards. Same as you'd remove a Write-Host you dropped in a script to check a value.

If you only need it for a single run and don't want to touch the YAML at all, queue the run manually and use the checkbox instead. Same effect, zero risk of it lingering in your pipeline definition.

More information

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...

The art of simplicity

Search This Blog