Contemporary product analytics systems require users to pose explicit queries, such as writing SQL, configuring dashboards, or constructing funnels, before insights can surface. This pull-based paradigm creates a bottleneck: it requires both domain knowledge and technical fluency, and assumes practitioners know in advance which questions to ask. We argue that behavioral analytics should move from passive systems that answer queries to active systems that continuously detect and explain behavioral phenomena. We present the Behavioral Intelligence Platform (BIP), a system architecture that transforms raw event streams into automatically generated insights. BIP consists of four layers. First, Normalization and State Derivation (NSD) standardizes events and maps them to a semantic state hierarchy. Second, a Behavioral Graph Engine (BGE) models user journeys as absorbing Markov chains and computes transition probabilities, removal effects, and path quality metrics. Third, a Behavioral Knowledge Graph (BKG) and Detector System convert graph outputs into grounded behavioral facts and identify behavioral phenomena. Finally, a Grounded Language Layer constrains large language model outputs to verified facts, producing reliable narrative insights. We formalize the Behavioral Intelligence Problem, introduce a taxonomy of detectors for autonomous insight generation, and propose an interestingness score to prioritize insights under limited attention.

翻译：当代产品分析系统要求用户先提出显式查询（例如编写SQL、配置仪表盘或构建漏斗），才能呈现洞察。这种拉取式范式造成瓶颈：既需要领域知识和技术能力，又假设从业者预先知道该提出哪些问题。我们认为行为分析应从被动回答问题系统转向主动检测并解释行为现象的系统。本文提出行为智能平台（BIP），一种可将原始事件流自动转化为生成式洞察的系统架构。BIP包含四个层级：第一，标准化与状态推导层（NSD）标准化事件并将其映射至语义状态层次结构；第二，行为图引擎（BGE）将用户旅程建模为吸收马尔可夫链，计算状态转移概率、移除效应及路径质量指标；第三，行为知识图谱（BKG）与检测器系统将图输出转化为可验证的行为事实并识别行为现象；最后，接地语言层约束大语言模型输出至验证事实，生成可靠的叙事型洞察。我们形式化定义了行为智能问题，提出了用于自主洞察生成的检测器分类体系，并设计了兴趣度评分机制，以在注意力有限条件下对洞察进行优先级排序。