为 AI Agent 设计三层记忆架构，替代单一聊天记录的检索逻辑

为 AI Agent 设计三层记忆架构，替代单一聊天记录的检索逻辑 targets a $4.2B = 70,000 AI-agent product teams x $6,000 ACV. Assumes global pool of 70k teams building agent-like features in mid-market and enterprise, each willing to pay for dedicated memory infrastructure and SLA. total addressable market with medium saturation and a year-over-year growth rate of 35% to 60% CAGR in AI developer platform spending, driven by agent adoption.

Key trends driving demand: agent-adoption -- more products embed autonomous agents that run recurring workflows, increasing demand for persistent, structured memory.; cheap-embeddings-and-vector-stores -- lower cost for embeddings and hosted vector DBs enables multi-layer retrieval architectures at scale.; framework-maturation -- frameworks like LangChain and Hermes Agent standardize agent patterns, creating integration points for opinionated memory stacks..