Why Go for production agentic AI
Stdlib over libraries, single binary over framework, fail-closed defaults over forgiveness. The boring-on-purpose case for choosing Go to ship a multi-agent system into a regulated environment.
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Stdlib over libraries, single binary over framework, fail-closed defaults over forgiveness. The boring-on-purpose case for choosing Go to ship a multi-agent system into a regulated environment.
Go's embed.FS bundles files into the binary at compile time. The pattern collapses what would be a multi-artefact deploy into one binary. Three places it pays back daily.
GOMEMLIMIT tells the Go runtime to keep memory below a soft cap by running GC harder when it's close. For containers with hard memory limits, this prevents OOM kills. The setting every Go service in K8s should have.
Go 1.21 added structured logging to the stdlib (slog). For a codebase with three or four logging-library generations layered on top of each other, the migration is a productive afternoon.
Patterns I confidently recommended five years ago that I'd argue against today. The list of "things you used to do in Go that don't pay back anymore."
Range-over-function landed in Go 1.23. `iter.Seq` lets you write iterators that compose. The patterns that pay back; the ones that don't.
Fan out to N agents; first error cancels the rest; collect successful results. errgroup is the right tool for this; the patterns are concise but worth getting exactly right.