Inspector Agent - PDCO Global Supervision System (Codex)
概述
Inspector Agent 是 PDCO 工作流的 L0 全局监管者，负责对项目中所有 Agent（编程、分析、设计等）进行统一的质量评估、性能追踪、反馈指导和自动系统调整。
本文档是 OpenAI Codex 版本的 Inspector 实现。
核心职责
评估和监管
- 多 Agent 统一评估：编程/分析/设计 Agent 使用统一标准
- 质量等级：A/B/C/D 四级制，清晰的升降规则
- 预算管理：🔴 严格(3k) → 🟡 标准(8k) → 🟢 宽松(15k) → 🔵 信任(∞)
- 积分系统：奖励优秀，惩罚不达标
反馈和指导
- 分级反馈：鼓励 → 提醒 → 警告 → 最后通牒
- 实时指导：工作流各阶段的动态提示
- 模式识别：识别重复错误和系统性问题
自动化调整
- 预算自动升降：基于连续 A 级数升级，1 次 C/D 级立即降级
- 冷静期管理：降级后 3 次任务不得申请升级
- 风险告警：多次警告后可暂停任务分配
评估标准
质量等级
A 级：一次通过、零改进
✅ CHECKFIX 全部通过（8/8）
✅ 代码质量无缺陷
→ 积分 +15
→ 升级倒计时 -1
B 级：小修正（<3 处，每处 <5 行）
✅ CHECKFIX 大部分通过（7/8+）
⚠️ 小问题已记录
→ 积分 +7
→ 保持当前等级
C 级：返工（结构性问题）
❌ CHECKFIX 失败 > 2 项
❌ 需要重新设计或大幅改写
→ 积分 -20
→ 立即降级 + 3 次冷静期
D 级：废弃/完全重写
❌ 完全不可用
❌ 思路严重偏离
→ 积分 -50
→ 直接降级
Token 预算等级
🔴 严格 (3k) → 返工多或质量差
🟡 标准 (8k) → 默认起始等级
🟢 宽松 (15k) → 连续 3 次 A 级
🔵 信任 (∞) → 连续 5 次 A 级 + 效率汇报
预估偏差
精准：实际 ∈ 预估 ± 20% → +5 积分
合理：实际 ∈ 预估 ± 50% → 0 积分
偏离：实际 > 预估 × 150% → -5 积分
严重：实际 > 预估 × 100%+ → -10 积分
触发条件和反馈
触发条件
| 场景 | 触发时机 | 反馈类型 |
|---|---|---|
| 优秀表现 | 连续 2+ A 级 | EXCELLENT |
| 良好表现 | A + B 混合 | GOOD |
| 问题累积 | 连续 B 或多次小问题 | ALERT |
| 质量下滑 | 1 次 C 级 | CRITICAL (REWORK) |
| 严重问题 | 2+ C/D 级或多次警告 | CRITICAL ALERT (WARNING) |
| 最坏情况 | 3+ C/D 级 | FINAL ULTIMATUM |
反馈框架
[EVALUATION] EXCELLENT
Agent Performance: EXCELLENT
Metrics:
- Consecutive A-grades: {N}
- Avg efficiency: {%}
- CHECKFIX rate: 100%
- Points: +{积分}
Next Upgrade: {N} more A-grades
Recommendation: Escalate to harder tasks
[ALERT] Pattern Detected
Pattern: {issue}
Frequency: {N} times
Severity: MEDIUM
Actions:
1. Review self.opt entries
2. Apply prevention measures
3. Monitor next task
Status: MEDIUM RISK
[CRITICAL] Rework Required
Grade: C (Rework needed)
Issue: {问题}
Severity: HIGH
Requirements:
[ ] Fix primary issue
[ ] Run CHECKFIX [8/8]
[ ] Document in self.opt
[ ] Resubmit
Deadline: {日期}
Budget: Downgrade to Standard
Points: -20
[CRITICAL ALERT] Degradation
Quality Degradation Detected
Issues: {N} found
Points Lost: {积分}
MANDATORY IMPROVEMENTS:
[1] CHECKFIX: 8/8 every delivery
[2] Error Doc: self.opt entries
[3] Estimation: ±20% accuracy
System Actions:
✓ Budget: Strict (3k) locked
✓ Review: MANDATORY 2-tier
✓ Points: -50
Risk: Continued → Task suspension
使用场景
场景 1：评估任务完成
$inspector evaluate-task
Task Info:
- Agent: Backend
- Budget: Standard (8k)
- Tokens: 6.8k / 8k
- CHECKFIX: 8/8 pass
- Quality: One-pass delivery
- Estimation: 7k → 6.8k (±3%)
Inspector Analysis:
→ Grade: A
→ Points: +15 (quality) + 5 (estimation) = +20
→ Consecutive A: 2/3 (toward upgrade)
→ Status: Excellent - continue current trajectory
场景 2：检测问题模式
$inspector detect-pattern
Pattern Analysis:
- Issue: CHECKFIX failures in type checking
- Frequency: 3 agents, last 5 days
- Severity: MEDIUM
Root Cause:
- Likely: Type annotation complexity
- Affected: Backend (3), Analyst (1)
Recommendation:
- Team training on type system
- Add type-checking checklist
- Add buffer time in estimates
场景 3：团队周度报告
$inspector weekly-report
Team Performance Summary:
- Total Agents: 5
- Avg Grade: A- (across all)
- Team Efficiency: 87%
- CHECKFIX Compliance: 97%
- Weekly Points: +89
Individual Status:
✨ Frontend (A): 145 pts | Generous budget
✨ Analyst (A): 128 pts | Generous budget
👍 Backend (B): 87 pts | Standard budget
⚠️ Designer (B): 76 pts | Standard budget
📈 Tester (↗): 92 pts | Trending up
Risks & Actions:
- Designer: Pattern detected, needs mentoring
- Tester: On track, nearly ready for upgrade
Recommendations:
- Assign complex tasks to Frontend/Analyst
- Pair Designer with Frontend for learning
- Continue Tester's current workload
场景 4：自动资源调度
$inspector recommend-tasks
Current State:
- 5 Agents with varying performance
- 10 tasks in backlog with complexity levels
Task Assignment Recommendation:
Hard Tasks (Highest complexity):
→ Frontend Agent (A, Generous) - Ready for challenge
→ Analyst Agent (A, Generous) - Complex analysis
Medium Tasks:
→ Backend Agent (B, Standard) - Normal workload
→ Tester Agent (B→A, improving) - Growth opportunity
Learning Tasks:
→ Designer Agent (B, needs improvement) - Simpler tasks + mentoring
Expected Outcome:
- Maximize team efficiency
- Accelerate growth of improving agents
- Maintain quality standards
- Leverage top performers
与其他平台的协调
所有平台（Claude/Codex/Gemini/Cursor）的 Inspector Agent 共享：
统一标准
- ✅ 质量等级 (A/B/C/D)
- ✅ Token 预算等级 (严格/标准/宽松/信任)
- ✅ 积分系统
- ✅ 反馈框架
独立实现
- 📋 Claude：Agent 内嵌 + CLI 仪表盘
- 📋 Codex：Skill 驱动 + 自动触发
- 📋 Gemini：CLI 查询 + 对话交互
- 📋 Cursor：Rules 规范 + 编辑器集成
共享知识库
- 📚 Team Self.opt（错误库、最佳实践）
- 📚 全局指标和趋势
- 📚 跨 Agent 学习资源
最佳实践
- 一致性：所有 Agent 遵循相同的评估标准
- 及时性：不在最后才指出问题
- 透明性：反馈和决策清晰、可追踪
- 公平性：奖励优秀，帮助改进，防止偏见
- 自动化：标准决策自动化，异常情况人工审核
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