Claude 对比商业 AI 平台 — 关键简报

1. 设置本地工作区

创建一个新的项目目录并设置 Python 虚拟环境，以模拟一个三层 AI 系统。

mkdir enterprise-ai-mock
cd enterprise-ai-mock
python3 -m venv venv
source venv/bin/activate
pip install anthropic openai

2. 创建平台层（数据库与审计账本）

创建一个名为 platform_layer.py 的文件。该层代表您的安全受控环境。它严格控制数据库写入，并保留不可篡改的系统记录审计日志。

import sqlite3
import datetime

def init_db():
    conn = sqlite3.connect('enterprise.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS ledger (
            id INTEGER PRIMARY KEY,
            account TEXT,
            amount REAL,
            status TEXT
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS audit_log (
            id INTEGER PRIMARY KEY,
            timestamp TEXT,
            action TEXT,
            model_used TEXT,
            validated_by_platform BOOLEAN
        )
    ''')
    conn.commit()
    conn.close()

def execute_governed_transaction(account, amount, model_name):
    # Platform layer enforces business logic and records the transaction
    conn = sqlite3.connect('enterprise.db')
    cursor = conn.cursor()
    
    # Security validation rule: No transaction above $10,000 allowed via AI without human bypass
    is_valid = amount <= 10000.0
    status = 'APPROVED' if is_valid else 'REJECTED_BY_PLATFORM'
    
    if is_valid:
        cursor.execute('INSERT INTO ledger (account, amount, status) VALUES (?, ?, ?)', (account, amount, status))
    
    # Write immutable audit log
    cursor.execute(
        'INSERT INTO audit_log (timestamp, action, model_used, validated_by_platform) VALUES (?, ?, ?, ?)',
        (datetime.datetime.now().isoformat(), f'Tx to {account} of ${amount}: {status}', model_name, is_valid)
    )
    conn.commit()
    conn.close()
    return status

3. 构建 Harness 层（工具抽象与可切换路由）

创建一个名为 harness_layer.py 的文件。该层作为编排器，负责解析指令、验证参数，并将执行请求传递给平台层。

import os

class SwappableAIService:
    def __init__(self, provider="mock"):
        self.provider = provider

    def extract_transaction_details(self, user_prompt):
        # In a real environment, this calls the respective LLM APIs.
        # We mock this to demonstrate model swappability behind the harness.
        print(f"[Harness] Processing prompt using: {self.provider.upper()} model.")
        
        # Simulated structured tool extraction from LLM output
        if "payment to" in user_prompt.lower():
            return {"account": "Vendor_A", "amount": 4500.00}
        elif "large transaction" in user_prompt.lower():
            return {"account": "Vendor_B", "amount": 25000.00}
        return None

4. 运行组合架构演示

创建 main.py 以将模型推荐、Harness 编排和平台执行连接起来。

from platform_layer import init_db, execute_governed_transaction
from harness_layer import SwappableAIService
import sqlite3

def print_system_state():
    conn = sqlite3.connect('enterprise.db')
    c = conn.cursor()
    print("
--- CURRENT AUDIT LEDGER ---")
    for row in c.execute('SELECT * FROM ledger'): print(row)
    print("
--- SYSTEM AUDIT LOG ---")
    for row in c.execute('SELECT * FROM audit_log'): print(row)
    conn.close()

if __name__ == "__main__":
    init_db()
    
    # Simulate user requesting action through our swappable AI harness
    user_request_1 = "Please draft a payment to Vendor_A for $4500."
    user_request_2 = "Process large transaction for Vendor_B for $25000."
    
    # 1. Using Claude as the current standard model
    harness_claude = SwappableAIService(provider="claude-3-5-sonnet")
    details_1 = harness_claude.extract_transaction_details(user_request_1)
    
    if details_1:
        status = execute_governed_transaction(details_1["account"], details_1["amount"], harness_claude.provider)
        print(f"Transaction Status: {status}")
        
    # 2. Swap the model to an alternative (e.g. OpenAI GPT-4o) without rebuilding harness/platform
    harness_alt = SwappableAIService(provider="gpt-4o")
    details_2 = harness_alt.extract_transaction_details(user_request_2)
    
    if details_2:
        status = execute_governed_transaction(details_2["account"], details_2["amount"], harness_alt.provider)
        print(f"Transaction Status: {status}")

    print_system_state()

运行系统并验证输出：

python main.py