This week, a 10,000-word article on Juejin went viral, cleanly breaking down 'AI Agent' — which is itself a signal: the Chinese tech community's understanding of Agent is still stuck at the literacy-for-non-developers phase.
What this is
The article offers a clean definition:
Agent = LLM (brain) + Planning (decompose tasks) + Memory (no goldfish brain) + Tools (hands and feet) + Execution Loop (observe → think → act → observe again)
Put plainly, Agent is no longer 'a chattier ChatGPT' — it's a 'program that does things,' able to break down its own steps, call its own tools, and adjust based on feedback. Its fundamental difference from an LLM is whether it can act — whether it can call APIs, submit code, and handle exceptions on its own.
The original text sums up LLMs as 'conditional probability generators': given the context, guess the next token. Strong at generation, weak at action. So even plugging in a search API only takes the first step; real tasks require multi-step decision-making, state management, exception handling, and dynamic planning — and that's the underlying reason Agents emerged.
Industry view
The bullish camp says this 'five-part formula' is the most memorable engineering framework of 2026. It translates the rhetoric from Bill Gates (who pegged Agent as a platform-level opportunity), OpenAI, Salesforce, Alibaba, and ByteDance down to a decomposable component layer. Planning, memory, tools, and loop — each is an independently optimizable engineering module, giving product differentiation a shared language at last.
But cooler heads raise sharp criticism: Andrew Ng publicly voiced concerns about Agent deployment difficulty a year ago, and that judgment still holds. The fact that a literacy piece can go viral is itself a reverse signal. The genuinely hard parts — how to keep Agents from going off the rails inside enterprise workflows, how to evaluate output, how to calculate ROI — literacy content never covers. Products that wrap an LLM in a thin shell and dare to call themselves Agent have numbered no fewer than 50 over the past 12 months.
Another widely overlooked risk: MCP (Model Context Protocol, the standard connection protocol between models and external tools/data) has been a de facto standard for less than a year, and the ecosystem is still in violent reshuffling. An Agent built on one vendor's protocol today may need half its code rewritten next year.
Impact on regular people
For enterprise IT: It's too early to talk about 'procuring an Agent platform.' We recommend listing high-frequency workflows first, picking 3–5 to run a POC (proof of concept), and distinguishing 'tool problems' from 'process problems.'
For individual careers: 'Agent replacing white-collar workers' won't happen within two years. Right now, you're more like a 'human Agent' — decomposing steps yourself, copy-pasting yourself, judging yourself — which precisely shows that judgment and process-decomposition skills are scarcer than any AI tool.
For consumer markets: Consumer-grade Agent (auto-restaurant-booking, auto-tax-filing, auto-price-comparison) hasn't crossed the 'usable' threshold. Before paying, we suggest waiting for independent third-party reviews rather than vendor demos.