[funfunfunction]

Cool project. A team at work was building something similar to internal use.

I'm curious how this compares to just using Claude Code directly and giving it a dump of the agent traces? It seems like Claude could probably do some of the same diagnostics / trace grouping to identify failure patterns. Why use a custom harness?

[mikepollard_dev]

Yeah, fair question. For a small number of traces just dumping them into Claude Code can work well.

However, once you're at production scale the problem changes. You can't always fit 10,000+ traces in Claude Code and still have it be effective especially when the relevant pattern of agent failures may only become apparent when you pass that many in. That's where the RLM based methodology helps. HALO recursively decomposes the trace data into smaller investigations, analyzes those sub-pieces, and then synthesizes those up to determine the recurring harness-level failure modes better than Claude Code or Codex ever could at a large scale.

[ilusion]

I'm very curious to see a benchmark for this - have toyed with the idea myself but haven't put in the hard work to test these hypothesis on extracting learning signal from deep-agent traces.

[funfunfunction]

There's some benchmarks in the repo for AppWorld. Looks promising