# Hybrid Scheduler - Agent Committee Review Report

**Date**: 2026-02-06
**Plan Reviewed**: `/Users/les/Projects/mahavishnu/docs/HYBRID_SCHEDULER_IMPLEMENTATION_PLAN.md`

---

## 📋 Committee Status

| Agent | Status | Score | Summary |
|-------|--------|-------|---------|
| 🏗️ **Architect Reviewer** | ⏳ Running | TBD | In progress |
| 👨‍💻 **Code Reviewer** | ❌ Error | - | Hit context limit (retry needed) |
| 📚 **Documentation** | ⏳ Running | TBD | In progress |
| ⚡ **Performance Engineer** | ⏳ Running | TBD | In progress |
| 🐍 **Python Pro** | ✅ **Complete** | **7.5/10** | Good with recommendations |

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## ✅ Completed Review: Python Pro Agent

**Overall Score**: 7.5/10 (Good - With Recommendations)

### Detailed Ratings

| Category | Score | Status |
|----------|-------|--------|
| **Python Best Practices** | 7/10 | ⚠️ Good |
| **Async Patterns** | 6/10 | ⚠️ Fair |
| **Library Integration** | 7/10 | ⚠️ Good |
| **Dependencies** | 6/10 | ⚠️ Fair |
| **Overall** | 7.5/10 | ✅ Approved with fixes |

---

## 🚨 Critical Issues Identified

### 1. **Missing APScheduler Dependency** (BLOCKING)
- **Issue**: APScheduler not in `pyproject.toml`
- **Impact**: Cannot implement Phase 1
- **Fix**: Add `"apscheduler>=3.10.0,<3.11.0"` to dependencies

### 2. **No Async Context Manager**
- **Issue**: Resource leak risk without proper cleanup
- **Impact**: Schedulers may not shut down cleanly
- **Fix**: Implement `@asynccontextmanager` for lifecycle management

### 3. **Not Using TaskGroup**
- **Issue**: Missing Python 3.13's structured concurrency
- **Impact**: Slower startup, harder error handling
- **Fix**: Use `asyncio.TaskGroup()` for concurrent scheduler startup

### 4. **Incomplete Type Hints**
- **Issue**: `Callable` should be `Callable[..., Awaitable[Any]]`
- **Impact**: Type safety issues
- **Fix**: Add proper Protocol for scheduler interface

---

## ✅ Key Recommendations from Python Pro

### **Use Python 3.13 Features:**

```python
# ✅ TaskGroup for concurrent scheduler startup
async with asyncio.TaskGroup() as tg:
    tg.create_task(self._start_apscheduler())
    tg.create_task(self._start_oneiric_queues())
    tg.create_task(self._start_prefect_deployments())
```

```python
# ✅ Async context manager for lifecycle
@asynccontextmanager
async def lifecycle(self) -> AsyncIterator[None]:
    try:
        await self.start()
        yield
    finally:
        await self.stop()
```

```python
# ✅ Protocol-based typing for scheduler interface
class SchedulerBackend(Protocol):
    async def schedule_task(...) -> str: ...
```

---

## 📊 Committee Consensus (Partial)

Based on the completed review:

### ✅ **Strengths of Plan**
1. Clear three-tier separation of concerns
2. Comprehensive implementation roadmap
3. Well-defined success criteria
4. Good risk mitigation strategy

### ⚠️ **Areas Requiring Attention**
1. Add APScheduler dependency immediately
2. Implement async lifecycle management
3. Use Python 3.13 TaskGroup for concurrency
4. Improve type hints throughout
5. Address dependency version conflicts

---

## 🎯 Approval Status

**Current Status**: ⚠️ **CONDITIONAL APPROVAL**

The committee **conditionally approves** the plan with the following requirements:

### Must Fix Before Implementation:
1. ✅ Add APScheduler to `pyproject.toml`
2. ✅ Implement `@asynccontextmanager` lifecycle
3. ✅ Use `asyncio.TaskGroup()` for startup
4. ✅ Add Protocol-based type hints

### Should Fix During Implementation:
1. Improve error handling in scheduler methods
2. Add comprehensive integration tests
3. Implement proper shutdown procedures
4. Add observability/metrics hooks

### Nice to Have:
1. Scheduler health check endpoints
2. Dynamic job registration API
3. Unified job ID format across schedulers
4. Job priority queueing

---

## 📁 Generated Documentation

The Python Pro agent created:

1. **`docs/HYBRID_SCHEDULER_PYTHON_REVIEW.md`** (15,000+ words)
   - Complete code examples with all fixes
   - Implementation roadmap
   - Best practices guide

2. **`docs/HYBRID_SCHEDULER_REVIEW_SUMMARY.md`**
   - Executive summary
   - Quick reference guide
   - Priority recommendations

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## 🔄 Next Steps

1. **Await remaining agent reviews** (Architect, Documentation, Performance)
2. **Address Python Pro's critical issues** (can start now)
3. **Consolidate all committee feedback** into final report
4. **Present final plan** for user approval
5. **Begin implementation** once approved

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## 📝 Open Questions from Review

1. Should we persist APScheduler jobs to SQLite for durability?
2. Do we need a unified job ID format across all three schedulers?
3. Should we implement a "fallback chain" (Prefect → Oneiric → APScheduler)?
4. How do we handle scheduler conflicts for the same workflow?
5. What's the rollback strategy if a scheduler fails to start?

---

**Report Generated**: 2026-02-06 22:14 UTC
**Status**: Awaiting 3 more agent reviews...
