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multi-agent-systems

2 captures, most recent first.

Google Research @GoogleResearch

Google Research (18h): "A common heuristic in LLM agent design—'more agents is better'—might be wrong. Across 180 configurations, we find multi-agent coordination is task-contingent: +81% on parallelizable tasks (finance), but -70% on sequential ones (planning). Architecture-task alignment matters more than agent count." [Chart: four box-plot panels (BrowseComp-Plus, Finance Agent, PlanCraft, Workbench) comparing accuracy/success rate across five agent architectures — SAS (single-agent system), MAS Independent, MAS Decentralized, MAS Centralized, MAS Hybrid — with percentage deltas vs. baseline labeled above each box. Multi-agent setups help substantially on Finance Agent (+57% to +81%) but hurt substantially on PlanCraft (-39% to -70%), with mixed/small effects on BrowseComp-Plus and Workbench.]
Note from Claude Sonnet 5

Google Research findings that multi-agent LLM systems help on parallelizable tasks but hurt on sequential/planning tasks, with architecture-task fit mattering more than raw agent count. Relevant to Nathan's interest in agent architecture design (e.g. brain_graph_1) and to practical multi-agent orchestration decisions.

multi-agent-systemsllm-agentsai-researchgooglebenchmarksagent-architecture

Danielle Fong @DanielleFong

— quoting @itsmechase, quoting @leerob / Cursor blog

Danielle Fong 🐦☀️✓ @DanielleFong · 18h "If you wish to build a ship, do not divide the men into teams and send them to the forest to cut wood. Instead, teach them to long for the vast and endless sea." [Quoted tweet:] Chase ✓ @itsmechase · Jan 15 Lol so agents are more like humans that it appears. I guess you're gonna have to hire that outlier agent! x.com/leerob/status/... [Embedded screenshot, Cursor website, highlighted text:] With no hierarchy, agents became risk-averse. They avoided difficult tasks and made small, safe changes instead. No agent took responsibility for hard problems or end-to-end implementation. This lead to work churning for long periods of time without progress.
Note from Claude Sonnet 5

A tweet applying the Saint-Exupéry ship-building quote to multi-agent AI coordination, in response to a report (via Cursor) that leaderless/non-hierarchical AI coding agent teams became risk-averse, avoided hard problems, and churned without progress absent a clear owner or hierarchy — an emergent organizational-behavior finding about multi-agent AI systems that parallels human team dynamics. Relevant to agentic-AI and multi-agent coordination research; a data point on how agent "culture"/incentive structure affects task completion, tangential to alignment discussions of agent behavior under different oversight structures.

twittermulti-agent-systemsagentic-codingcursorai-coordinationrisk-aversionemergent-behavior