Table of Contents
- Parallel Tool Use Hiding I/O Latency
- Hypothesis Generation to Strategic Exploration
- Parallel Evaluation for Robust Governance
- Speculative Execution for Hyper-Responsive Agents
- Hierarchical Agent Teams for Superior Quality
- Competitive Agent Ensembles
- Agent Assembly Line for High-Volume Processing
- Decentralized Blackboard Collaboration
- Redundant Execution for Fault Tolerance
- Parallel Query Expansion to Maximize Recall
- Sharded & Scattered Retrieval
- Parallel Hybrid Search Fusion for High-Fidelity Context
- Parallel Context Pre-processing for Accuracy Gains
- Multi-Hop Retrieval for Deep Reasoning
primarily focusing on parallelism and intelligent orchestration.
It argues that by breaking down complex tasks, leveraging specialized agents, and optimizing execution flows, AI systems can achieve superior performance, reliability, and accuracy compared to monolithic approaches.
核心方法: 1.breaking down complex tasks 2.leveraging specialized agents 3.optimizing execution flows
performance, reliability, and accuracy compared to monolithic approches.
Parallel Tool Use Hiding I/O Latency
A sequential process would take 10–30 seconds, while a parallel one would still take only 2–3 seconds.
Hypothesis Generation to Strategic Exploration
Finally, a “judge” agent evaluates the competing solutions and selects the single best one. This creates a system that is more robust, more creative, and less likely to get stuck on a suboptimal path.
Parallel Evaluation for Robust Governance
- 问题: 依赖单个AI模型进行复杂内容的评估（例如，广告文案、新闻稿、政策文件）时，该模型可能存在偏见、知识盲区或能力不足，无法全面考虑所有相关因素。例如，一个擅长判断品牌调性的AI可能不擅长事实核查，反之亦然。这可能导致评估结果不全面、不准确，甚至产生风险。
- 解决方案: 该模式通过引入一个“专家小组”——多个具有不同专业视角（如事实核查、品牌调性、法律合规、风险评估）的AI评论员，确保内容从多个维度得到全面、深入的审查。每个AI专注于其擅长的领域，大大提高了评估的覆盖面和专业性。
Speculative Execution for Hyper-Responsive Agents
While the agent is thinking, the system makes an educated guess about the upcoming action and starts it.
Hierarchical Agent Teams for Superior Quality
question: But complex tasks are often unexpected or unseen, requiring the agent to decide what and when to execute, introducing a delay between planning and action.
Competitive Agent Ensembles
question: In an agentic solution, each AI agent has its own biases, strengths, and weaknesses.
Agent Assembly Line for High-Volume Processing
本质是个MQ队列.
Decentralized Blackboard Collaboration
question : But what about problems where the solution path is not known in advance? For complex sense-making or analysis tasks, a more flexible, adaptive approach is required. 本质: 共享记忆, 事件驱动
Redundant Execution for Fault Tolerance
本质: 用钱买稳定性.
Parallel Query Expansion to Maximize Recall
本质: 扩大用户的语意范围,提高召准确度
Sharded & Scattered Retrieval
大库的问题: 筛选的不精准, 相似度为89.99的可能有1000个
本质: 按领域分库, 精准查找
Parallel Hybrid Search Fusion for High-Fidelity Context
本质: 相似不等于相关, 扩大召回
Parallel Context Pre-processing for Accuracy Gains
上下文腐化问题: However, passing this large, often noisy, collection of documents directly into the final generator LLM context window is problematic.
Multi-Hop Retrieval for Deep Reasoning
原文: https://levelup.gitconnected.com/building-the-14-key-pillars-of-agentic-ai-229e50f65986
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