aifollow.news
Back Rohan Paul
Rohan Paul· @rohanpaul_ai · X· · Original publication time AI score54

Enterprise workflow evaluation finds higher token costs and limited success gains for multi-agent setupsMachine translation

AI introduction

A post describing an Amazon paper says the evaluation tested eight agent variants across 13 enterprise procedures on Claude Opus 4.8, keeping tasks and mock tools fixed. Multi-agent or skill setups used 1.4 to 4.7 times as many tokens as a single agent, mostly for small gains; one initial multi-agent build had a lower task success rate.

Original post · Original

The article text is unavailable in this language; an existing version is shown.

@rohanpaul_ai

– https://t.co/fHSfCkyXOQ

Title: "Agent Architecture Cost-Success Trade-offs: A Controlled Evaluation across Enterprise-Style SOPs"

ReplyRohan Paul@rohanpaul_ai
New Amazon paper finds that for enterprise workflows, a single agent is usually the best default, because multi-agent setups cost far more for small gains. Teams automating long, branching business procedures can pick a single agent, an agent with skills, an orchestrator with workers, or a swarm of specialists. Little controlled evidence exists for choosing among them. They ran 4 agent designs, as 8 variants, on Claude Opus 4.8 across 13 enterprise procedures. Tasks and mock tools stayed fixed, so only the architecture changed. Every multi-agent or skill setup used 1.4x to 4.7x the tokens of the single agent, mostly for small gains. A first multi-agent build lost to the single agent, 58.2% to 67.4% task success, while a repaired version won but cost the most tokens. Start with a single agent, measure success per workflow, and fix implementation bugs before paying for a more complex architecture.
View this post on X ↗

Source: Rohan Paul · x.com

Research
Found an error? Send a correction