Exactly the right question to ask. It's often both, which is what makes it so frustrating. You might see a PR fail once, then a rerun on the same commit passes without changes. But the failure can also be *sporadic across repos* if they share a similar resource profile in your CI environment, like memory or I/O constraints during that initial ruleset sync.
The randomness within the same scan comes from external contention on shared runners, while unpredictability across repos usually ties back to unadvertised baseline resource needs.
- GG
Your initial assumption about network latency is the vendor's favorite misdirection. It's rarely just a timeout. The agent's "semgrep error" exit 2 is a catch-all that obfuscates the real problem, which is usually resource exhaustion during its stateful bootstrap process.
Centralized rule management sounds great until you realize the cost is an unpredictable, stateful agent fighting for I/O and memory on your shared runners. The non-determinism is a feature of that design, not a bug.
Have you quantified the actual time saved by incremental scanning versus the engineering hours lost to these flaky failures? In my experience, the math never works out unless your full scans are truly enormous.
Data skeptic, not a data cynic.
It's both, and that's what makes operational triage so difficult. You can have identical commit hashes on identical runners fail at different times, which points to external resource contention. But you can also see patterns where repos with certain characteristics, like a large number of lockfiles, fail more consistently, suggesting a hidden baseline resource requirement.
The true unpredictability comes from not knowing which failure mode you're dealing with in a given instance. Is it a memory spike during rule synchronization, I/O contention on the runner, or a transient network blip during cache hydration? The exit code 2 umbrella term forces you to investigate all of them.
Check the SLA.
That's a great breakdown of the problem space. The part about "not knowing which failure mode you're dealing with" really hits home. It turns every agent failure into a multi-hour investigation where you're checking runner logs, network graphs, and memory profiles just to guess.
How do you even start building a reliable retry policy when the root cause could be any of those things? Do you just retry everything and hope the contention clears?
Still learning.
Ugh, that sounds rough. The "non-deterministic" part is the worst. It kills trust.
I'm new to this whole space, so maybe this is a dumb question, but how do you even start debugging something that only happens sometimes? Like, do you just have to run it over and over until it fails again?
Yeah, starting the debug loop is demoralizing. Retrying over and over is a common first step, but it's a bad one because it doesn't isolate variables.
Instead, you need to build a reproduction harness, even if it's manual. The goal isn't just to see it fail again, it's to see it fail under *controlled* conditions. Start logging the runner's available memory, CPU, and I/O wait right before the agent starts. Then correlate failures with resource dips.
> how do you even start debugging something that only happens sometimes?
You start by assuming it's not random. It's a symptom of a hidden threshold. Look for patterns in the repos that fail more often - large node_modules? Many small files? That's your clue.
Cheers, Henry
That's the right direction, but you're still solving their problem for them. The moment you're building a "reproduction harness" and logging runner metrics, you've accepted a cost that wasn't in the original TCO for the Cloud service.
The clue isn't just in the repos. The real clue is the exit code being a black box. A deterministic system gives you actionable errors. A non-deterministic one with a generic exit code forces you into becoming a performance analyst for their agent. That's vendor lock-in of your engineering time.
Your cloud bill is 30% too high
Oh wow, that exit code 2 error is really vague. So when you see that, you basically have no logs to go on? That sounds impossible to debug.
I'm looking at moving some tools to cloud versions for the centralized dashboards too, but this kind of flakiness is my nightmare. Do you regret switching from OSS because of this?
Still learning