AI RESEARCH
Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines
arXiv CS.AI
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ArXi:2605.00410v1 Announce Type: cross A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings, but naively merged calls silently degrade quality through tool loss and prompt compression. We present Agent Capsules, an adaptive execution runtime that treats multi-agent pipeline execution as an optimization problem with empirical quality constraints.