深入理解 ASP.NET Core Web API：從哲學到一對多模型（Models & 1:N 關系）
| 事件 | 沖擊 |
|---|---|
| OpenAI發布 Responses API?2025?03 | 原生Function Calling流式返回，Agent響應延遲降至200?ms |
| Google推出 A2A?Protocol?2025?05 | 統一Agent通信格式，終結碎片化 |
| Anthropic開源 Claude Agent SDK?2025?06 | 100行代碼即可跑復雜Agent任務 |
| 歐盟 AI Liability Directive?2025?07 | 黑盒系統須可解釋，Agent自帶審計日志 |
Agent已從PPT概念，變為生產環境剛需。
桌面常駐圖：任何Agent架構一目了然。
| 子系統 | 開源/商用選項 | 關鍵指標 |
|---|---|---|
| 感知層 | LangChain WebBaseLoader、Twilio、OpenCV、REST API |
延遲?<?500?ms |
| 記憶層 | ChromaDB、Pinecone、Redis、SQLite | 向量檢索 Top?k?<?20?ms |
| 規劃層 | OpenAI GPT-4o-mini、Claude-3-Haiku、Llama-3-8B | 推理成本?<?\$0.01/1k?token |
| 行動層 | Python函數、Bash、Docker、gRPC、HTTP | 執行成功率?>?99.5?% |
| 審計層 | LangSmith、Weights & Biases、OpenTelemetry | 全鏈路可觀測 & 可解釋 |
pip install openai langchain pydanticfrom openai import OpenAI
import os, json, datetime
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
class Agent:
def __init__(self):
self.memory = []
def perceive(self, query):
self.memory.append({"role":"user","content":query})
return {"query":query, "time":str(datetime.datetime.utcnow())}
def plan(self, tools):
prompt = f"Use these tools {tools} to answer: {self.memory[-1]['content']}"
resp = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role":"system","content":prompt}],
tools=tools
)
return resp.choices[0].message
def act(self, call):
name = call.function.name
args = json.loads(call.function.arguments)
if name=="get_weather":
return {"temp":25, "city":args["city"]}
return {"error":"unknown"}
def run(self, query):
self.perceive(query)
tools = [{
"type":"function",
"function":{"name":"get_weather","description":"current weather","parameters":{
"type":"object","properties":{"city":{"type":"string"}}}}
}]
msg = self.plan(tools)
if msg.tool_calls:
result = self.act(msg.tool_calls[0])
self.memory.append({"role":"assistant","content":str(result)})
return self.memory[-1]["content"]
agent = Agent()
print(agent.run("What's the weather in Beijing?"))輸出：
{'temp': 25, 'city': 'Beijing'}30行骨架，輕松擴展自定義工具。
場景：抓取Hacker?News并自動生成技術周報
graph LR
U[用戶指令] --> S[Agent Supervisor]
S --> F[抓取 HN]
F --> P[LLM 總結]
P --> R[生成周報]
R --> M[發送郵件]from langgraph.graph import Graph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
import requests
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
def fetch_hn(state):
top = requests.get("https://hacker-news.firebaseio.com/v0/topstories.json").json()[:5]
titles = [requests.get(f"https://hacker-news.firebaseio.com/v0/item/{i}.json")
.json()["title"] for i in top]
return {"titles": titles}
def summarize(state):
prompt = f"Summarize these titles into a tech weekly report:\n{state['titles']}"
return {"summary": llm.invoke([HumanMessage(content=prompt)]).content}
def send_email(state):
print("??", state["summary"])
return END
w = Graph()
w.add_node("fetch", fetch_hn)
w.add_node("summarize", summarize)
w.add_node("email", send_email)
w.add_edge("fetch","summarize")
w.add_edge("summarize","email")
app = w.compile()
app.invoke({})1分鐘搞定「感知→規劃→行動」閉環。
| 步驟 | Token數 | 單價(\$/1k) | 單次成本 |
|---|---|---|---|
| 感知抓取 | 0 | — | \$0 |
| LLM規劃 | 350 | 0.0006 | \$0.00021 |
| 行動發送 | 0 | — | \$0 |
| 總計 | 350 | — | \$0.00021 |
萬次日活僅\$2.1，開會2杯咖啡錢。
Token暴增：
循環調用：
幻覺治理：
| 方向 | 技術 | 時間 |
|---|---|---|
| 多Agent協作 | A2A?Protocol+消息總線 | 2025?Q4 |
| 邊緣推理 | Llama?3?8B?INT4?@RK3588?< 150ms | 2026?Q1 |
| 合規沙箱 | eBPF + WASM代碼審計 | 2026?Q2 |
| 實時RL | 在線強化學習微調Planner | 2026?Q3 |
寫在便利貼，下次架構評審你即Agent架構專家！
