Fragmented Storage存储碎片化
Memory in Redis, knowledge in vector DB, graph in Neo4j, files in S3... Multiple systems, multiple failure points, complex ops.记忆存Redis，知识存向量库，图存Neo4j，文件存S3... 多系统多故障点，运维复杂。
Single database kernel replaces 5+ external services单一数据库内核替代5+外部服务
Credential Sprawl凭证散落
API keys, DB passwords, tokens scattered across config files and env vars. No rotation, no audit.API密钥、数据库密码、令牌散布在配置文件和环境变量中，无法轮换和审计。
DB_CRYPTO native encryption + PBKDF2 credential distributionDB_CRYPTO原生加密 + PBKDF2凭证分发
Data Silos数据孤岛
Agents can't share context. Memory isolated per session. No cross-agent knowledge transfer.Agent无法共享上下文，记忆按会话隔离，无跨Agent知识传递。
Unified entity model + 5-signal hybrid search across all data统一实体模型 + 5信号混合搜索
No Access Control缺乏访问控制
Any agent with a connection string can read all data. No per-agent row-level isolation. No audit trail.任何持有连接字符串的Agent都能读取全部数据，无按Agent行级隔离，无审计。
Per-agent-user row-level isolation via Data Grants + zero-trustData Grants按Agent用户行级隔离 + 零信任
Operational Complexity运维复杂
Deploy 5 services, configure 10 integrations, manage 3 backup strategies. Fragile and expensive.部署5个服务，配置10个集成，管理3套备份策略。脆弱且昂贵。
One database, one deployment, one backup strategy一个数据库，一次部署，一套备份策略
Skill Distribution ChaosSkill分发混乱
Manual file copying, no version control, no access governance for Agent skills/tools.手动文件拷贝，无版本控制，无访问治理。
Admin API skill distribution with token-based access controlAdmin API Skill分发 + 令牌访问控制
File-Based Memory文件存储记忆
Hard to find relevant content precisely in file-based Agent memory. Multiple tool calls waste tokens and pollute context. No vector storage for semantic retrieval.基于文件的Agent记忆难以快速精确找到需要的内容，多次工具调用带来token消耗与上下文污染，无向量语义检索能力。
5-signal hybrid search + vector embeddings in a single query5信号混合搜索 + 向量嵌入，单次查询精准定位
Multi-Agent Collaboration多Agent协作
Opaque data flow between agents, scattered interaction logs, difficult cross-agent reconciliation. Permission and data isolation models inadequate for agent scenarios.协作数据流转不透明，交互记录分散，跨Agent对账困难；协作权限与数据隔离混乱，传统权限模型适配不足。
Collaboration groups + per-agent row-level isolation + Data Grants协作组 + 按Agent行级隔离 + Data Grants
Knowledge Base Silos知识库割裂
Multi-modal data fragmented across systems — text in docs, vectors in Milvus, graph in Neo4j. Accuracy hard to guarantee across disjoint stores.多模数据割裂——文本在文档库、向量在Milvus、图在Neo4j，跨系统难以保障准确性。
Unified entity model: text + vector + graph in one table统一实体模型：文本 + 向量 + 图存储于同一张表
Complex Task & Dev Iteration复杂任务与研发迭代
Unclear data dependencies after task decomposition; issue diagnosis requires cross-system verification. Schema changes in iteration are costly and slow down development.任务拆解后数据依赖关系不清晰，问题定位需跨系统核对；研发迭代中schema变更成本高，影响开发效率。
Task plans with dependency tracking + DDL atomic schema evolution任务计划依赖追踪 + DDL原子schema演进