Unverified50% confidenceOpinionExact time
Agents based on large language models are prone to error accumulation and hallucination in long-chain tasks, and multi-agent collaboration amplifies this problem.
1
Sources
50%
Confidence
Long-term
Relevance
11/1/2026
First Seen
Sources
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Related Claims
UnverifiedDifferent multi-agent collaboration modes differ significantly in fault tolerance, observability, and development complexity.78% similarUnverified在多 Agent 并行模式下，若需求拆解存在歧义、任务边界不清晰，多个 Agent 会同时沿错误方向生成大量需返工代码76% similarUnverifiedIn the LLM era, multi-agent collaboration typically manifests as multiple AI instances with different role settings interacting through a message-passing mechanism.74% similarUnverifiedA single Agentic session may span dozens of operational steps involving multiple files and modules, significantly amplifying the problem of fragmented work records.74% similarVerifiedAgents possess capabilities for planning, tool invocation, memory, and autonomous iteration, distinguishing them from the single-round question-and-answer mode of traditional large language models.73% similar
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