Deconstructing the LLM Lineage: From LLM to Agent, It's All Context Patching

Over the past three years, at least six core concepts have emerged in the LLM space, but strip away the packaging and only one fact remains: the LLM itself only does next-word prediction. All new technologies like Agents and RAG are essentially just patching a machine that only knows how to fill in the blanks.

What this is

We note that while the tech circle coins new terms daily, the main thread is actually extremely clear. The core capability of a Large Language Model (LLM) is predicting the next token based on preceding text; it has no memory and no knowledge. To compensate for this flaw, technological evolution has unfolded along the line of "stuffing information into the context pocket": the first patch is Memory, stuffing chat history in to prevent amnesia; the second is RAG (Retrieval-Augmented Generation, i.e., querying an external knowledge base), stuffing private documents in to prevent hallucinations; the third is Function Calling, making it output JSON instructions that programs can read; the fourth is MCP (Model Context Protocol, i.e., the USB interface for AI to call tools), unifying tool standards to make integration easier. When these four patches are applied, an Agent—capable of thinking independently, researching, and calling software to do work—is born.

Industry view

This evolutionary logic is seen by the industry as the necessary path for AI to move from a "chat companion toy" to a "productivity tool," and the emergence of protocols like MCP makes the division of labor in the tool ecosystem possible. But it is worth our concern that there are also obvious objections and worries in the industry: most Agent projects on the market currently are just "API-wrapper automation," with processes hardcoded into scripts that fall into infinite loops upon the slightest anomaly. At the same time, stuffing massive amounts of retrieved information and tool feedback into the context window easily leads to scattered model attention and a rebound in error rates. This is the fundamental reason why current Agent projects have low deployment rates and dare not directly face complex commercial scenarios.

Impact on regular people

For enterprise IT: The cost of integrating AI tools will substantively drop. Unified protocols like MCP give legacy enterprise systems a plug-and-play "AI plug," eliminating the need to write separate integration code for each model.

For the individual workplace: Rather than anxiously memorizing new terminology, it's better to view AI as an "intern who needs to be fed data and given clear instructions." Mastering how to effectively stuff information into its context is the true moat.

For the consumer market: Future AI applications will evolve from "chat boxes" to "errand runners." Agents capable of automatically calling various software to complete closed-loop tasks will gradually replace existing pure chatbots.