ml engineering

TraceML asks whether coding agents can plan through real ML work

Coding agents look impressive on isolated tasks, but machine-learning work is messier: data changes, experiments fail, metrics mislead, and progress often depends on choosing the next test rather than writing the next function. TraceML is useful because it studies that planning layer instead of treating every software task like a short coding puzzle.

Why it matters: The watch point is whether tool makers start evaluating planning quality, not just final task success. A correct answer with a broken or unverifiable path is risky in real ML systems, where teams need to know what changed and why.