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tasks will run in parallel where Gradle determines it’s appropriate to do so. Across project, and within a project (when configuration cache is enabled). Is there something about these tasks that isn’t parallel friendly? There isn’t a declarative way to ‘synchronize’, only a few hacky solutions depending on the nature of the challenge.

execOperations.exec {
    commandLine(
        "./gradlew",

I don’t want to spin up a new gradle daemon for each

execOperations.exec

yea. if it’s absolutely necessary to do that (suggest avoiding it at all costs), you could synchronize on something to prevent overlaps. Perhaps a BuildService can help with that.

why are you exec'ing gradle instead of using GradleBuild?

@ephemient https://gradle-community.slack.com/archives/CAHSN3LDN/p1667339458239099?thread_ts=1667339458.239099&cid=CAHSN3LDN

tasks.register<GradleBuild>("publishChild") {
    setDir("child")
    setTasks(listOf(":assembleArtifact"))
    startParameter.setProjectProperties(mapOf("publish.version" to version))
}

or a composite build would usually be a better solution. even with a single daemon, they can have different AGP versions etc. (although obviously the same Gradle version)

With a single daemon, there can still need compile errors with incompatible AGP versions

GradleBuild

is a bad idea, especially if you don't know the downstream builds, because it does not support composite builds. As I said in the other thread, use the tooling API instead. Same effect, less problems, just a bit less convenient usage. In both approaches AGP should not be touched as the tasks run isolated. I'm right now not sure about Gradle version with

GradleBuild

as I didn't use it for long time, but using the tooling API you can use different Gradle versions. For running the tasks sequentially, use a shared build service. That is not a hacky solution, but a clean one. Define a build service with max parallel usage of 1 and make the tasks use it. One of the use-cases for shared build services is to represent a common restricted resource, in this case CPU / Memory for example.