2026 年的程序员行业，可以用一句话概括：纯写代码的岗位在收缩，但“会解决问题、会用 AI 落地”的开发者反而更值钱。
未来程序员大概会分成几类：
- AI 协作型开发者：会用 AI 工具提效，把重复工作交给 AI，自己做架构、评审、联调和兜底。
- AI 应用/Agent 开发者：把大模型接到真实业务里，做智能客服、自动化流程、知识库、工具调用等。
- 架构与系统工程师：负责性能、稳定性、安全、可扩展性，这些不是 AI 能单独承担的责任。
- 业务+技术复合型人才：懂行业流程，能把模糊需求拆成可落地的技术方案。
- 传统纯编码岗位：不会消失，但竞争会更激烈，薪资和机会会更分化。
面对如此艰难的现状，如何实现了一个调用大模型进行智能对话的 Agent 基础框架
agent 就像一个"智能对话机器人"的启动器。它首先加载你电脑里保存的密钥（API Key），然后连接到 DeepSeek 的大模型服务（用的是 deepseek-flash 这个模型）。接着，它向模型发送一条系统指令（"你是一个有用的助手"）和一条用户消息（"Hello"），让模型生成回复。最后，它把模型生成的回答打印出来。
具体来说，这段代码具备以下几个核心能力：
- 安全调用大模型：通过环境变量读取 API 密钥和基础地址，避免把敏感信息硬编码在代码里，这是工程上的好习惯。
- 发起智能对话：它构建了一个标准的对话结构，包含系统角色设定（system）和用户输入（user），让模型理解自己的身份和任务。
- 开启深度思考模式：代码中设置了 reasoning_effort="high" 和 extra_body={"thinking": {"type": "enabled"}}，这意味着模型在回答前会进行更深层次的推理和思考，适合处理复杂问题。
- 同步获取回复：stream=False 表示它会等待模型完整生成回复后再一次性返回结果，而不是逐字流式输出。
简单来说，这就是一个最基础的 AI Agent 雏形——它能接收用户输入，交给大模型思考处理，然后把结果返回给你。你可以把它理解为一个"智能问答的最小可行版本"，后续可以在此基础上扩展更多功能，比如添加记忆、工具调用、多轮对话等。
具体实现如下：
# Please install OpenAI SDK first: `pip3 install openai`
# dotenv
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI(
api_key=os.environ.get('DEEPSEEK_API_KEY'),
base_url=os.environ.get('DEEPSEEK_BASE_URL'),
)
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Hello"},
],
stream=False,
reasoning_effort="high",
extra_body={"thinking": {"type": "enabled"}},
)
print(response.choices[0].message.content)
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The programmer industry in 2026 can be summed up in one sentence: purely code-writing jobs are shrinking, but developers who “can solve problems and land with AI” are more valuable.
There are probably several categories of programmers in the future:
- AI Collaborative Developer: This will be done using the AI tool, handing over duplicate work to AI, doing its own architecture, evaluation, coordination and bottom-up.
- AI Application/Agent Developer: Bringing LLM into real business for smart passenger service, automated processes, knowledge base, tool call, etc.
- Structure and Systems Engineer: Responsible for performance, stability, security, scalability, which is not the responsibility of AI alone.
- Business + Technological Complex: Knowledge of industrial processes that can break vague demand into off-the-shelf technological options.
- Traditional net-coding posts: will not disappear, but competition will increase and pay and opportunities will become more polarized.
In the face of such a difficult situation, how can an Agent basic framework for intelligent dialogue with LLM be achieved
Angent is like the starter of an intelligent dialogue robot. It first loads the key (API Key) that you keep in your computer, and then connects to the LLM service in DeepSeek (a model of Deepseek-flash). And then it sends the model a system command ("You're a useful assistant") and a user message ( "Hello") to make the model respond. Finally, it printed out the responses that the model generated.
Specifically, the code has the following core competencies:
- LLM: It is a good project practice to read API keys and base addresses through environmental variables and to avoid coding sensitive information into code.
- Initiating intelligent dialogue: it has built a standard dialogue structure that includes system role setting (systemstem) and user input (user) to enable models to understand their identities and tasks.
- Opens the deep thinking mode: The code sets out the deeper reasoning and thinking that models are suitable for complex issues.
- Synchronize the response: stream = False indicates that it will wait for the complete model to generate the response and return the result again, rather than the output by word.
In short, this is the most basic AI Agent prototype -- it can receive user input, give it to LLM for thought and then return it to you. You can interpret it as a "lowest possible version of smart questions and answers" on the basis of which follow-up can expand more functions, such as memory addition, tool call, multi-cycle dialogues, etc.
This will be achieved as follows:
# Please install OpenAI SDK first: `pip3 install openai`
# dotenv
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI(
api_key=os.environ.get('DEEPSEEK_API_KEY'),
base_url=os.environ.get('DEEPSEEK_BASE_URL'),
)
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Hello"},
],
stream=False,
reasoning_effort="high",
extra_body={"thinking": {"type": "enabled"}},
)
print(response.choices[0].message.content)
Welcome to the chicken brother!
Auto-fetched and translated by AI Atlas. All rights belong to the original author.