What Developers Need in 2026: Decoupling from the LLM with Kernel, Runtime, and Protocol
2026：构建模型之外的世界——Kernel、Runtime、Protocol： 在本地构建你的智能底座。 不是在云端依赖一个模型。
Because the truth is simple:
If the structure does not live with us, the intelligence never belongs to us.
A future built purely on LLM output is a future where:
memory evaporates with every reset,
logic dissolves in every version bump,
agency collapses because the flow of reality is owned by someone else’s model.
What Does It Take to Decouple From an AI Large Language Model?
(Or: How to Stop Depending on a Monolithic LLM While Still Keeping Intelligence)
Decoupling from an LLM does not mean removing AI.
It means removing the LLM as the single point of truth, cognition, and execution.
To decouple is to reclaim agency, structure, and controllability.
The clearest way to say it:
Decoupling = extracting “intelligence” out of the model → migrating it into your own structure layer and scheduler layer.
The model becomes a component — not the brain.
Once this shift happens, the system no longer depends on one big model.
It depends on your structure.
And I’m almost certain I’m not the only one thinking about this.
I’ll open my Substack chat — if you’re exploring similar directions, we might actually have a real conversation.
Now, the three forms of decoupling:
1. Decoupling from “model memory”
Memory belongs to the application — belongs to the owner — belongs to me.
I should be able to store all my memory, not as copy-paste snippets, but as active, flowing context.
The model’s job is only:
reasoning
generation
In other words: the CPU.
But events, preferences, persona, long-term memory —
all of that belongs to the application’s structure layer.
That layer is the runtime’s true world model.
2. Decoupling from long prompts
Structure belongs to the application — to the user — not to the model.
The model (the tape) should not carry the entire world.
The real world should live in:
state
ledgers
persona cards
schemas
and other structural artifacts
The model sees only the fragments it needs, exactly when it needs them.
In other words:
The prompt is no longer the protagonist.Structure is.
3. Decoupling from behavioral logic
An agent’s behavior must be determined by the runtime, not by the model alone.
The model performs exactly one thing:
one step of reasoning
But:
when to execute
what to execute
for whom to execute
and how the next step should change
— all of these belong to the scheduler, router, and agent runtime.
The model is not the agent.
It is not the persona.
It is not the system.
It is simply the execution unit.
The real intelligence lives above the model:
in the structure layer
in the behavior layer
in the scheduling layer
Which means — in the long run —
even if your favorite GPT or Gemini goes down, your system still runs.
You still have stability, continuity, and agency.
You no longer feel pain from outages, because the model is no longer the system.
1. What We Actually Need (Conceptually)
🧬 A. Structure Kernel (KERNEL)
The Kernel is everything that must exist even if every model disappears —
and you can still:
See your world
Describe your world
Reconstruct your world locally
The Kernel is not responsible for “being smart.”
It is responsible for stable structures and invariants.
State Model (World State)
Users / family / projects / tasks / events / memories
Lives in:
state.json,ledger.json,persona.json,schemas/…This is your world model — not the model’s.
Ledger & Events (Causal Record)
Append-only event streams:
events.log/session.eventsReplayable, auditable, compressible
Every agent action ultimately writes here as the causal history of the world.
Persona & Policy (Identity and Rules)
Persona schemas + persona cards
Permissions, boundaries, preferences, styles
The answer to: who is speaking, who is acting.
Schema & Protocol (Structural Contract)
Unified JSON schemas for tasks, conversations, memory, preferences, tool calls
Makes structure the first-class citizen — not raw prompts.
In one sentence:
The Kernel is the minimal operating system for all your structural assets.
Models are just co-processors mounted on top.
🔁 B. Agent Runtime (RUNTIME)
The Runtime is the living layer — the place where scheduling and behavior truly happen.
It is responsible for:
Event Loop (Main Loop)
Listens for input (human, external system, scheduled tasks)
Generates the next intention (call tool / call model / update state)
Writes results back to the ledger / state
Scheduler
When should the model be invoked?
When should rules be used instead?
When is AI unnecessary?
When should the system sleep / batch / defer work?
Router
Chooses the agent persona, strategy, and toolchain
Decides whether to use an LLM, and which LLM
Tooling Layer
Actions are not prompts — actions are:
query_db()send_email()update_ledger()call_model(model_id, input_struct)
The model is just one of many possible actions.
In one sentence:
The Runtime decides when, why, and how to invoke the Kernel and the model.
📡 Protocol Layer (PROTOCOL)
The Protocol Layer defines how everything communicates:
How the outside world talks to your system
How internal components talk to each other
Every input and output must go through a protocol.
Nothing is allowed to bypass it and talk directly to the model or the Kernel.
2026年要想办法探讨和大模型“解耦”
2026 年，对我来说，就是完全进入 AI 原生应用时代 的一年。我曾经写过一个比喻：大模型其实就是通用图灵机的那条“纸带”。它承载信息，压缩世界，展开潜能，执行逻辑，一切语言、结构、外部环境都在这条纸带上被折叠与重写。问题是，我们现在 99% 的 AI 应用仍然绑死在纸带上。本质上，大部分所谓“AI 应用”，不过是把更复杂的 prompt 写到纸带上，或者在纸带上贴一层 UI。从工程上看，这还停留在 2023–2024 的范式：依赖模型的注意力窗口，依赖手工堆积的上下文，依赖同一条纸带承载所有的状态、记忆、意图与行为。 而我 2026 的目标之一： 让应用程序与纸带本身实现深度解耦。
解耦三件事：
第一，从“模型记忆”中解耦：记忆属于应用，不属于纸带。 纸带（模型）只负责推理与生成，是 CPU。 而事件、偏好、人设、长期记忆属于应用结构层，这些是 runtime 自己的“世界模型”。
第二，从“长提示词”中解耦：结构属于应用，不属于纸带。 纸带不应该承载整个世界。 真正的世界应该由 state、ledger、人格卡、schema 等结构保存； 模型只在需要时读取片段。 换句话说： 提示词不再是主角，结构才是主角。
第三，从“行为逻辑”中解耦：智能体的行为由 runtime 决定，不由纸带决定。 纸带只执行一步推理， 但“何时执行、执行什么、针对谁、执行后如何改变下一步” 必须由调度器、路由器、智能体 runtime 决定。 模型不是 agent、不是人格、不是系统，它只是执行单元。 真正的智能存在于纸带之上的结构层、行为层、调度层。
所以在 2026，我要做的，就是从“纸带时代的应用”跨入“结构时代的应用”。应用与模型不再混在一起成为一团胶水，而是清晰的分层：
模型负责推理； 应用负责结构； 运行时负责调度； 多智能体负责协作； 用户负责意图。
1. 我们真正需要的是什么（概念层面）
🧬 A. 结构内核（KERNEL）
Kernel 是即使所有模型都消失，你的系统依然必须存在的那一层——
因为你仍然需要能够：
看见你的世界
描述你的世界
在本地重建你的世界
Kernel 的职责不是“聪明”，
而是维护 稳定的结构与不变量。
State Model（世界状态）
用户 / 家庭 / 项目 / 任务 / 事件 / 记忆
存放于：
state.json、ledger.json、persona.json、schemas/…这是你的世界模型——而非任何模型的世界模型。
Ledger & Events（因果账本）
追加式事件流：
events.log/session.events可回放、可审计、可压缩
每一次智能体行为最终都写入这里，作为世界的因果记录。
Persona & Policy（身份与规则）
persona schema + persona cards
权限、边界、偏好、风格
回答“谁在说话 / 谁在行动”。
Schema & Protocol（结构契约）
统一的 JSON schema：任务、对话、记忆、偏好、工具调用
让 结构 成为一等公民，而不是 prompt 文本。
一句话总结：
Kernel 是你所有结构资产的“最小操作系统”。
模型只是挂在其上的协处理器。
🔁 B. 智能体运行时（RUNTIME）
Runtime 是系统的生命层——
真正执行调度、生成行为的地方。
它负责：
Event Loop（主循环）
监听输入（人类、外部系统、定时任务）
生成下一步意图（调用工具 / 调用模型 / 更新状态）
将结果写回 ledger / state
Scheduler（调度器）
什么时候应该调用模型？
什么时候只需要规则？
什么时候根本不需要 AI？
什么时候需要休眠 / 批处理 / 延迟执行？
Router（路由器）
决定使用哪个智能体 persona、哪种策略、哪个工具链
决定是否使用 LLM，以及使用 哪一个 LLM
Tooling Layer（工具层）
行为不是 prompt，而是明确的动作：
query_db()send_email()update_ledger()call_model(model_id, input_struct)
模型只是众多动作中的一个。
一句话总结：
Runtime 决定何时、为何、以何种方式调用 Kernel 与模型。
📡 协议层（PROTOCOL）
Protocol Layer 定义了系统中一切通信的方式：
外部世界如何与系统交流
系统内部组件如何彼此交流
所有输入与输出都必须经过协议。
任何组件不得绕过协议，直接与模型或 Kernel 通信。