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时间 a user gives an instruction such as “构建 a website,” the AI 智能体 interprets the requirements, designs the architecture, and creates a structured execution plan. The actual work is then delegated to the 分离式 执行 平台, or DEP.

DEP is not a local LLM, and it is not embedded inside a single AI 智能体. It is an independent, agent-agnostic execution platform composed of PHP and Python microservices, parsers, workflow engines, schedulers, automation scripts, validators, deployment services, and monitoring 工具.

Any authorized AI 智能体 can call these services through a common interface. This allows AINNA智能体, GPT, Grok, Claude, Gemini, internal 企业 agents, and future AI 系统 to share the same trusted execution layer without rebuilding the underlying automation for every model.

DEP handles deterministic and repeatable work such as project scaffolding, CRUD and API generation, database migrations, validation, testing, deployment, backup, health monitoring, scheduled jobs, and infrastructure automation. Once a task is submitted, it can continue running even after the AI session, interface, or 命令行 is closed.

The architecture is supported by 智能路由 and Specialized 护栏. 智能路由 sends each task to the most appropriate layer-parser, PHP microservice, Python microservice, workflow engine, specialized model, or frontier LLM, while 护栏 enforce permissions, scopes, approval gates, 审计追踪s, and operational boundaries.

Our goal is not to build an AI that thinks continuously. Our goal is to build an architecture that knows when to reason, when to execute, when to delegate, and when to stop. We believe the future of 企业 AI will be built on multiple AI智能体 working through a shared, secure, and reusable execution platform. AINNA - 构建 smarter execution, not just smarter conversations.