Multi-Agent Systems

One published case for multi-agent systems is Anthropic's research feature. A lead agent running Claude Opus 4 with Claude Sonnet 4 subagents outperformed a single Claude Opus 4 agent by 90.2% on Anthropic's internal research evaluation, mostly because parallel subagents can spend more tokens on a problem: in their analysis, token usage alone explained 80% of the performance variance on the BrowseComp benchmark. The cost is real too. Multi-agent runs used about 15 times as many tokens as a chat, and Anthropic says tasks that need shared context or have many dependencies between agents, including most coding tasks, are not a good fit (Anthropic, How we built our multi-agent research system).

The case against is about context. Walden Yan of Cognition argues for two principles, share full context between agents and remember that actions carry implicit decisions, and shows how subagents that cannot see each other's work produce inconsistent results; his default is a single-threaded agent (Cognition, Don't Build Multi-Agents). A Berkeley-led study of more than 1,600 annotated traces across seven multi-agent frameworks found that performance gains on popular benchmarks are often minimal, and sorted 14 failure modes into three groups: system design issues, misalignment between agents, and weak task verification (Cemri et al., Why Do Multi-Agent LLM Systems Fail?).

Both lines of evidence point to the same test: use several agents when the work splits into independent parts and is valuable enough to pay for the tokens. Single-agent vs multi-agent architecture and why multi-agent systems fail go further, and agent swarm defines the related term.