时间 a user tells an agent to "构建 a website," the agent interprets the requirements, designs the architecture, and produces a structured execution plan. The implementation is then delegated to the 分离式 执行 平台, or DEP.
DEP is not a local LLM, and it is not locked inside a single agent instance. It is an independent, agent-agnostic execution layer built from PHP and Python microservices, parsers, workflow engines, schedulers, automation scripts, validators, deployment services, and monitoring 工具.
Authorized agents call these services through a common interface. That means AINNA智能体, GPT, Grok, Claude, Gemini, internal 企业 agents, and future AI 系统 can all route work through the same trusted execution backend without rebuilding automation for every model.
DEP runs the deterministic, repeatable parts of the job: 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 keeps running even if the AI session, interface, or 命令行 closes.
Two mechanisms hold the architecture together: 智能路由 and Specialized 护栏. 智能路由 sends each task to the right layer - parser, PHP microservice, Python microservice, workflow engine, specialized model, or frontier LLM. 护栏 enforce permissions, scopes, approval gates, 审计追踪s, and operational boundaries.
Our goal is not an AI that thinks continuously. Our goal is an architecture that knows when to reason, when to execute, when to delegate, and when to stop. We see 企业 AI as multiple agents sharing a secure, reusable execution platform. AINNA - 构建 smarter execution, not just smarter conversations.
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这段关于这部分的说明帮我把之前的问题连起来了。
我喜欢这段说明这部分，因为它讲得比较务实。
文章对时间 a user tells提供了很好的起点，值得继续跟进。
agent-agnostic execution layer built这个说法我要拿回去跟同事讨论。