Agent 技术趋势

Agent 技术正以前所未有的速度进化。本章总结 2024-2025 年的关键趋势，并展望 Agent 的未来发展方向。

技术演进路线

2025 年关键趋势

趋势一：Agent 协议标准化

"""
MCP (Model Context Protocol) 协议示例
"""
from dataclasses import dataclass, field
@dataclass
class MCPTool:
"""MCP 工具定义"""
name: str
description: str
input_schema: dict
def to_spec(self) -> dict:
return {
"name": self.name,
"description": self.description,
"inputSchema": self.input_schema,
}
@dataclass
class MCPResource:
"""MCP 资源定义"""
uri: str
name: str
mime_type: str = "text/plain"
class MCPServer:
"""简化 MCP 服务实现"""
def __init__(self, name: str, version: str = "1.0"):
self.name = name
self.version = version
self.tools: dict[str, MCPTool] = {}
self.resources: dict[str, MCPResource] = {}
self._handlers: dict[str, callable] = {}
def register_tool(self, tool: MCPTool, handler: callable) -> None:
self.tools[tool.name] = tool
self._handlers[tool.name] = handler
def register_resource(self, resource: MCPResource) -> None:
self.resources[resource.uri] = resource
def handle_request(self, method: str, params: dict) -> dict:
"""处理 MCP 请求"""
if method == "tools/list":
return {"tools": [t.to_spec() for t in self.tools.values()]}
elif method == "tools/call":
tool_name = params.get("name", "")
handler = self._handlers.get(tool_name)
if handler:
result = handler(params.get("arguments", {}))
return {"content": [{"type": "text", "text": str(result)}]}
return {"error": f"Tool not found: {tool_name}"}
elif method == "resources/list":
return {
"resources": [
{"uri": r.uri, "name": r.name, "mimeType": r.mime_type}
for r in self.resources.values()
]
}
return {"error": f"Unknown method: {method}"}

趋势二：Agent 评估标准化

趋势三：垂直 Agent 深化

三种 Agent 范式演进

"""
Agent 范式对比
"""
from dataclasses import dataclass
@dataclass
class AgentParadigm:
"""Agent 范式"""
name: str
era: str
characteristics: list[str]
limitations: list[str]
examples: list[str]
PARADIGMS = [
AgentParadigm(
name="工具增强 LLM",
era="2023-2024",
characteristics=[
"LLM 作为核心推理引擎",
"通过 Function Calling 使用工具",
"单轮或少轮交互",
],
limitations=[
"规划能力有限",
"难以处理长期任务",
"错误恢复能力弱",
],
examples=["ChatGPT Plugins", "GPT Actions"],
),
AgentParadigm(
name="Agentic 系统",
era="2024-2025",
characteristics=[
"多步规划与执行",
"自我反思与修正",
"多 Agent 协作",
"流程图/状态机编排",
],
limitations=[
"依赖精心设计的 Prompt",
"成本高（多次 LLM 调用）",
"可控性仍有挑战",
],
examples=["LangGraph", "CrewAI", "Claude Computer Use"],
),
AgentParadigm(
name="自主 Agent（展望）",
era="2026+",
characteristics=[
"长期目标追踪",
"自主学习和适应",
"Agent 网络协作",
"人类般的工具发现与使用",
],
limitations=[
"安全与对齐挑战",
"计算成本",
"信任与可解释性",
],
examples=["Devin（初期形态）", "Agent Protocol 构想"],
),
]
def print_evolution():
"""打印范式演进"""
for p in PARADIGMS:
print(f"\n{'='*50}")
print(f"📌 {p.name} ({p.era})")
print(f"  特征: {', '.join(p.characteristics[:2])}")
print(f"  局限: {', '.join(p.limitations[:2])}")
print(f"  代表: {', '.join(p.examples)}")

未来展望

Agent 开发者学习路径

本章小结

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