自动化垂直智能体的验证流程 | AI生成和翻译
简短回答：你基本正确。可扩展的方式是构建垂直的、编码化的智能体来调用LLM API，并配合强大的自动化验证。你仍然需要”异常时人工介入”的循环，而非”完全无需人工”，因为现实情况包括API故障、模型漂移和边缘案例。但对于范围明确且具备良好检查机制的任务，你可以实现全自动化运行。
以下是可实施的实用蓝图。
实现”全自动化”的真正要素
- 确定性框架：代码计划/模板只需生成一次，然后进行参数化；不要每次运行都”从头开始重新提示”
- 类型化、模式化的输入输出：强制LLM返回JSON格式，并在使用前由代码进行验证
- 基础检查：通过工具、编译器、单元测试、代码检查器、HTTP模拟、SQL模式等验证输出
- 风险门控：仅在置信度和检查通过时自动应用更改；否则创建审查工单/PR
- 可观测性：日志、追踪、成本/延迟监控、成功率、漂移/警报阈值
- 深度防御：多模型交叉检查、自一致性投票、沙箱执行、差异大小限制、白名单/黑名单
参考架构（精简而可靠）
- 控制器：从队列（Kafka/SQS/Redis/简单数据库）读取任务
- 规划器（LLM）：将任务转化为具体计划和结构化步骤
- 执行器（工具+必要时使用LLM）：代码编辑、API调用、文件操作
- 验证器：模式/类型检查、静态分析、单元测试、黄金测试
- 策略引擎：决定自动合并或”需要人工介入”
- 报告器：创建PR、生成问题、发送Slack/邮件摘要
实际有效的验证模式
- JSON模式验证+重试直至有效
- AST解析+基础语义检查（确保类/方法存在）
- 运行ruff/mypy/flake8，pytest（含覆盖率阈值），bandit安全扫描
- 文本/数据：正则表达式不变量、参考答案、BLEU/ROUGE阈值或定制业务规则
- 调用外部系统：先进行模拟/桩测试；生产环境金丝雀部署采用只读或影子模式；然后逐步推广
入门：Python”垂直智能体”框架
该框架支持并行执行任务，强制JSON输出，进行验证，运行本地检查，并自动应用更改或创建PR。请将call_llm()桩函数替换为您的提供商/路由器。
import asyncio, json, os, re, subprocess, time
from dataclasses import dataclass, asdict
from typing import Any, Dict, Optional, List, Tuple, Callable
# ---------- 任务规范 ----------
@dataclass
class Task:
id: str
kind: str # 例如："refactor", "write_test", "doc_summarize"
repo_path: str
target: str # 文件/路径/模块或URL
spec: Dict[str, Any] # 自由格式详细信息
# ---------- LLM调用（在此替换为您的路由器） ----------
async def call_llm(system: str, user: str, schema_hint: str, max_retries=3) -> Dict[str, Any]:
"""
返回结构化JSON。真实实现：Anthropic/OpenAI/Gemini/Mistral路由器配合
工具强制/JSON模式/'respond_with_schema'等功能
"""
last_err = None
for _ in range(max_retries):
# >>> 替换为实际的JSON模式API调用 <<<
fake = {"plan": ["edit file", "run tests"], "edits":[{"path":"foo.py","patch":"print('ok')\n"}], "confidence": 0.92}
try:
# 提前验证基础字段
if not isinstance(fake.get("edits"), list): raise ValueError("bad edits")
return fake
except Exception as e:
last_err = e
raise RuntimeError(f"LLM failed to produce valid JSON: {last_err}")
# ---------- 验证器 ----------
def json_schema_validate(payload: Dict[str, Any]) -> Tuple[bool, str]:
if "edits" not in payload: return False, "missing edits"
for e in payload["edits"]:
if "path" not in e or "patch" not in e:
return False, f"bad edit item: {e}"
return True, "ok"
def apply_patch(repo_path: str, path: str, patch: str) -> None:
abs_path = os.path.join(repo_path, path)
os.makedirs(os.path.dirname(abs_path), exist_ok=True)
with open(abs_path, "a", encoding="utf-8") as f:
f.write("\n" + patch)
def run_cmd(cmd: List[str], cwd: Optional[str]=None, timeout: int=300) -> Tuple[int, str]:
proc = subprocess.Popen(cmd, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
try:
out, _ = proc.communicate(timeout=timeout)
except subprocess.TimeoutExpired:
proc.kill()
return 124, "timeout"
return proc.returncode, out
def static_checks(repo_path: str) -> Tuple[bool, str]:
# 替换为您的工具：ruff, mypy, eslint, mvn test, gradle等
codes = []
outputs = []
# Python检查示例；如果工具缺失则跳过
for cmd in [["python","-m","py_compile","."]]:
rc, out = run_cmd(cmd, cwd=repo_path)
codes.append(rc); outputs.append(out or "")
ok = all(rc == 0 for rc in codes)
return ok, "\n".join(outputs)
def unit_tests(repo_path: str) -> Tuple[bool, str]:
# 根据需要替换为pytest/mvn/gradle/npm test
if not os.path.exists(os.path.join(repo_path, "tests")):
return True, "no tests dir, skipping"
rc, out = run_cmd(["pytest","-q"], cwd=repo_path)
return rc == 0, out
def policy_decision(confidence: float, static_ok: bool, tests_ok: bool, max_diff_lines: int, diff_lines: int) -> str:
if confidence >= 0.9 and static_ok and tests_ok and diff_lines <= max_diff_lines:
return "AUTO_APPLY"
return "REVIEW"
def compute_diff_size(repo_path: str) -> int:
rc, out = run_cmd(["git","-c","color.ui=never","diff"], cwd=repo_path)
if rc != 0: return 10**9
return len(out.splitlines())
# ---------- 工作器 ----------
async def worker(task: Task, max_diff_lines=800) -> Dict[str, Any]:
system_prompt = "You are a strict code agent. Output JSON only and follow the schema."
user_prompt = json.dumps(asdict(task), ensure_ascii=False)
schema_hint = '{"plan":[str], "edits":[{"path":str,"patch":str}], "confidence": float}'
payload = await call_llm(system_prompt, user_prompt, schema_hint)
ok, why = json_schema_validate(payload)
if not ok:
return {"task": task.id, "status":"FAILED", "reason": f"schema: {why}"}
# 在沙箱分支中应用编辑
run_cmd(["git","checkout","-B", f"agent/{task.id}"], cwd=task.repo_path)
for e in payload["edits"]:
apply_patch(task.repo_path, e["path"], e["patch"])
run_cmd(["git","add","-A"], cwd=task.repo_path)
run_cmd(["git","commit","-m", f"agent: {task.kind} {task.target}"], cwd=task.repo_path)
# 验证
static_ok, static_out = static_checks(task.repo_path)
tests_ok, tests_out = unit_tests(task.repo_path)
diff_lines = compute_diff_size(task.repo_path)
decision = policy_decision(payload.get("confidence",0.0), static_ok, tests_ok, max_diff_lines, diff_lines)
result = {
"task": task.id,
"decision": decision,
"confidence": payload.get("confidence"),
"static_ok": static_ok,
"tests_ok": tests_ok,
"diff_lines": diff_lines,
}
if decision == "AUTO_APPLY":
# 合并到main；或通过CI规则推送和自动合并
run_cmd(["git","checkout","main"], cwd=task.repo_path)
run_cmd(["git","merge","--no-ff", f"agent/{task.id}"], cwd=task.repo_path)
result["status"] = "APPLIED"
else:
# 通过CI机器人创建PR而非直接合并
result["status"] = "REVIEW"
result["note"] = "Opened PR or created issue for human-on-exception."
return result
# ---------- 编排器 ----------
async def run_queue(tasks: List[Task], concurrency: int = 4) -> List[Dict[str, Any]]:
sem = asyncio.Semaphore(concurrency)
results = []
async def run_one(t: Task):
async with sem:
try:
res = await worker(t)
except Exception as e:
res = {"task": t.id, "status":"FAILED", "reason": repr(e)}
results.append(res)
await asyncio.gather(*(run_one(t) for t in tasks))
return results
# 使用示例：
# asyncio.run(run_queue([Task(id="1", kind="refactor", repo_path="/path/to/repo", target="foo.py", spec={"goal":"extract fn"})]))
如何实现”无需对话，只需代码”
- 规划器生成结构化编辑
- 验证器确保正确性
- 策略引擎自动应用安全差异，其余路由至审查
- 您无需与聊天机器人”对话”。只需提交任务（YAML/JSON或CLI），系统即会执行
您可能需要添加的升级功能
- 多模型路由器，含回退机制和自一致性投票
- JSON模式强制，配合重新提示直至模式有效的工具
- 领域验证器：数据库迁移试运行、API合约检查（OpenAPI）、负载测试
- 工件存储：用于计划、差异、日志；Grafana仪表板用于SLI/SLO监控
- 密钥与数据治理：范围化令牌、数据脱敏、仓库白名单
何时能真正实现”完全无人值守”
- 任务范围明确且可重复（例如文档同步、样板测试桩、常规重构）
- 具备良好的预言测试或强不变量
- 影子/金丝雀测试已成功运行一段时间且实现零接触
- 影响范围有限（差异小、可逆、沙箱化）
对于其他情况，请保持异常时人工介入。这样您仍能获得80-95%的生产力提升，同时避免静默失败的风险。
如果需要，我可以根据您的具体技术栈（Python/Java Maven、Ruff/Mypy/PyTest，或Maven/Gradle + SpotBugs/Checkstyle/JUnit）调整此框架，并连接到您的路由器配置。