Study quantifies when multi-agent LLM coding is reliable for qualitative analysis

Researchers examined how AI coding agents collaborate, disagree, and converge when performing multi-coder qualitative coding, a task where their usefulness has been assumed but rarely measured. The paper reports empirical results on the settings and conditions under which multi-agent LLM coding performs dependably, and highlights the gaps that limit its reliability. It frames these findings as both challenges and opportunities for building better LLM-assisted qualitative research tools.