Agent vs Full-Stack: Chinese Hiring Data Says Full-Stack First, Then Agent

The past two years have made "Can programmers still do this work?" a frequent topic in the community, and a more specific and anxious question is: should technical transformation go toward Agent (intelligent agent) development or full-stack development? This article combines real JD (job descriptions) from the current recruitment market, salary data, and views from multiple frontline engineers to give a market analysis that is as objective as possible.

1. First Clarify the Concepts: What Is Agent, What Is Full-Stack

Agent (智能体), literally translated as "agent". For programmers, an Agent system is: an AI-driven program that can automatically plan tasks, call tools, and finally independently complete complex tasks.

For example: if you want to rent a house, the Agent will automatically search for listings, contact agents, verify whether the information meets requirements, and finally output an organized list for you — the entire process requires only one sentence of input from you.

Claude Code and various AI programming assistants we use daily essentially fall into the agent category. The technical layers supporting it include: large model (LLM) integration, tool-call encapsulation, and agent development frameworks like LangChain — these frameworks have already encapsulated model integration and tool calls, and their usage is not much different from frameworks like Vue and Spring, just with different configuration methods and thinking patterns.

Full-stack, on the other hand, is a technical set that enables independently completing a Web page, App, or mini-program project. Note that full-stack is not limited to specific languages: the backend can be Java, Node.js, Python; the frontend can be Vue, React; the App can be native or Flutter. The core judgment criterion is only one: can you run the entire project from development to deployment independently. The essence of full-stack is not "knowing everything" but global problem-solving ability.

2. Current Recruitment Market: Real JDs for the Two Directions

Agent Development Positions

Searching "Agent development" on recruitment platforms by latest order, it's worth noting HR activity and the number of positions. Current market characteristics are as follows:

• Salary: Most above 15K, with the mainstream concentrated at 20K–25K, and some positions at 30K–40K;
• Company size: More than half are small and medium companies with 20–99 people, with relatively few large companies;
• Requirements: Proficiency in at least one backend language, Java or Python; familiarity with any frontend framework, React or Vue; experience in LLM application development (having integrated Doubao, DeepSeek, GPT, Gemini, etc.); familiarity with OpenAI-style model APIs; at least familiarity with one agent development framework (such as LangChain); more than two years of development experience.

Java Full-Stack Positions

Salaries are also mostly above 15K, with the 15K–25K range being the most common, and companies of all sizes have demand, unlike Agent positions which are highly concentrated in small and medium companies. The typical requirements are: proficiency in Spring Boot / Spring Cloud / MyBatis; familiarity with any one of React or Vue; more than five years of development experience.

Two points worth noting: First, the "proficiency" in JDs shouldn't be taken literally — developers who truly master a framework wouldn't dare claim proficiency; being able to use it skillfully is enough. Second, full-stack positions have generally raised the threshold from "two years of experience" to "five years of experience," which indirectly shows that the programmer supply is indeed excessive.

Comparing the requirements also reveals a key fact: Agent development requirements are almost a superset of full-stack requirements — you must first be able to independently build an application, then add model integration and tool calls to it.

3. Trend Judgment: Business-Driven vs. Opportunity-Driven

Technical directions have always been driven by business. The strongest demand of companies right now is cost reduction and efficiency improvement — when the market has no incremental growth, a company's first choice to survive is cutting costs, which is also the root cause of large-scale layoffs.

Full-stack is business-driven. Companies need engineers who can independently deliver projects, and frontend engineers are also moving toward full-stack to survive, so competition is high, but demand is stable in the long term. Full-stack is a track where "running a little faster than others is enough" — you don't need to be the best; you only need to be a little stronger than the one eliminated first.

Agent is opportunity-driven. It is a product of the AI era, and opportunities are not necessarily stable. There are three reasons:

1. Doubtful value. When housing apps are turned into agents, they still search only their own data. This experience is not necessary for users, and getting them to pay is even harder.
2. Unclear corporate planning. AI iteration is too fast. As soon as a company decides to build an agent, new technology emerges — follow it and you have to spend money, don't follow it and you fear falling behind, like an arms race.
3. Need for ecosystem support. Truly valuable agents (such as one aggregating nationwide housing listings) depend on cross-enterprise data and cooperation, and currently, except for a very few fields that can reach high agreement, it is difficult to build an ecological closed loop.

4. Real Voices from the Community: Full-Stack Is the Foundation, Agent Is the Surprise Troop

Views from multiple frontline engineer communities are highly consistent with the above market analysis:

A technical leader with over ten years of full-stack experience said in an interview: "Agent is a tool, full-stack is an ability." Tools iterate continuously; abilities accumulate over the long term. Full-stack engineers' overall perspective, ability to locate production issues, and technology-selection judgment are things agents find hard to replace; agents can help you write code, but they cannot make architectural decisions for you, nor can they align requirements with product managers for you.

A more cautionary case on Juejin is a frontend engineer with twelve years of experience who was laid off. His backend skills were weak, and his motivation to learn Agent was actually to "avoid full-stack" — after two months of study he still hadn't finished and hadn't even started sending out his resume. The blogger 双越 (Shuang Yue) commented sharply: "Full-stack is the foundation, Agent is the surprise troop." On top of the full-stack foundation, you then strengthen Agent development capabilities; you must see this relationship and order clearly. Also "deduce execution from results" — write your resume first and test the waters by applying; if interview invitations come, finish learning quickly; if you send to ten companies and get no response, adjust direction in time rather than burying your head in hard work.

There are also many practical articles proving the two paths are converging: a Tencent Cloud case shows that with the help of main-control agent toolchains like WorkBuddy, an original 30-day system development was compressed to a 7-day launch; AI did not replace full-stack developers, but turned developers "from people who write code to people who command AI to write code." Recruitment data also confirms the convergence trend: in the last six months, the frequency of "React" in JDs dropped by 41%, but "React + Agent integration experience" rose by 293%.

5. Conclusion and Recommendations

Back to the original question: should you switch to Agent development or full-stack? My answer is: Full-stack first, then Agent — the two don't conflict; the routes overlap, and Agent is just the extra part on top of full-stack.

Specific recommendations:

1. Don't treat Agent as an escape hatch. If learning Agent is only to avoid weak backend skills, the end of this road is likely still not finding a job after two months of studying.
2. Deduce learning from results. Before starting, ask yourself: what can I build after learning this? How do I write my resume? Will the market accept it? Sending ten resumes to test the waters is far more effective than burying your head in three months of videos.
3. Beware of free traffic-driving courses. The high-view-count introductory videos on Bilibili are mostly traffic-generation content from training institutions; gambling your precious time on their teaching quality is extremely low value for money.
4. Watch the endgame variable. When operating systems integrate AI, software forms may completely change — and operating systems themselves are complete ecosystems. This change is worth noting, but worrying is useless; life goes on. In this era, continuously changing yourself is what matters most.