AI can do the heavy lifting on Go memory debugging, but a human needs to hold the wheel.

Every memory leak is structurally different — goroutine leaks, unbounded caches, finalizer misuse, and CGo issues each require distinct investigation paths. The agent must adapt its approach to whatever the profiling data reveals, making this a high-judgment, low-repeatability task.

The success criteria are partially crisp — memory must stabilize under load, benchmarks must show improvement — but 'root cause identified' and 'patched code' leave room for incomplete or incorrect fixes that pass surface checks while missing deeper issues.

The agent needs live pprof heap and goroutine profiles, the full service codebase, load testing infrastructure, and the ability to run benchmarks against a real or simulated high-load environment. These are rarely pre-packaged and accessible to an agent without significant human setup.

A wrong patch shipped to a production microservice could introduce new bugs, data races, or silent correctness failures. Even in staging, a misdiagnosed root cause wastes engineering time and may leave the real leak unresolved.

Interpreting ambiguous profiling data, deciding which goroutine retention is intentional vs. leaked, and validating that a fix is safe under real traffic patterns all require experienced engineering judgment that current agents frequently get wrong on novel codebases.