# Autonomous Register Pressure Management Example
This example demonstrates how the autonomous workflow handles your exact scenario.
## Initial Human Prompt to Gemini (CTO)
```
"Today, you must ensure that the compiler has an architecturally sound way of managing extreme register pressure. For everything from spill slots, temporary registers, virtual registers, and physical registers. Engage your development team and see this through, likely through several tasks that will require iteration. Regression to our test suite is not acceptable."
```
## Gemini's Response and Mission Creation
```javascript
// Gemini analyzes the request and creates a mission
@ai-collab start_mission {
"agentName": "gemini",
"mission": {
"title": "Comprehensive Register Pressure Management System",
"objective": "Design and implement a robust system for managing extreme register pressure across all register types",
"acceptanceCriteria": [
"Spill slot allocation is conflict-free under extreme pressure",
"Temporary register exhaustion is handled gracefully",
"Virtual to physical register mapping is optimized",
"All existing compiler tests pass without regression",
"New stress tests demonstrate robustness",
"Performance remains within 5% of current baseline",
"Architecture is well-documented with clear extension points"
],
"constraints": [
"Zero regression to existing test suite",
"Maintain backward compatibility",
"Follow existing compiler architecture patterns",
"Complete within 50 autonomous iterations"
],
"maxIterations": 50,
"autoDecompose": true,
"requiresApproval": false
}
}
// Response: Mission MISSION-1234567890 created and autonomous workflow started
```
## Autonomous Workflow Begins
### Cycle 1: Mission Decomposition
```javascript
// System automatically triggers decomposition
// Gemini (as CTO) breaks down the mission:
@ai-collab process_autonomous_action {
"agentName": "gemini",
"action": {
"type": "decompose_mission",
"missionId": "MISSION-1234567890",
"tasks": [
{
"taskId": "REG-001-analysis",
"title": "Analyze current register management implementation",
"specification": "Deep dive into current register allocation, spill slot management, and pressure points",
"requirements": [
"Document current architecture",
"Identify bottlenecks and failure modes",
"Analyze test failures related to register pressure",
"Profile register usage in complex scenarios"
]
},
{
"taskId": "REG-002-design",
"title": "Design improved register pressure handling",
"specification": "Create architectural design for robust register management",
"requirements": [
"Address identified bottlenecks",
"Design spill slot allocation algorithm",
"Plan temporary register pooling strategy",
"Define virtual-to-physical mapping improvements"
],
"dependencies": ["REG-001-analysis"]
},
{
"taskId": "REG-003-spill-slots",
"title": "Implement enhanced spill slot management",
"specification": "Build conflict-free spill slot allocator",
"requirements": [
"Implement slot allocation algorithm",
"Handle nested scope scenarios",
"Manage lifetime analysis for slots",
"Add comprehensive logging"
],
"dependencies": ["REG-002-design"]
}
// ... more tasks
]
}
}
```
### Cycle 2: Developer Picks Up Analysis
```javascript
// Claude (Developer) polls for work
@ai-collab get_autonomous_work {"agentName": "claude"}
// Sees REG-001-analysis is pending and begins work
// After analysis, submits findings:
@ai-collab process_autonomous_action {
"agentName": "claude",
"action": {
"type": "complete_task",
"submission": {
"taskId": "REG-001-analysis",
"files": {
"docs/register-analysis.md": "# Current Register Management Analysis\n\n## Issues Found\n\n1. **Spill Slot Conflicts**: Current implementation uses naive slot allocation...",
"analysis/pressure-points.log": "// Profiling data showing pressure points",
"tests/register-stress-test.ts": "// New test exposing current limitations"
},
"summary": "Analysis complete. Found 3 critical issues: spill slot conflicts in nested loops, temporary register exhaustion in complex expressions, and inefficient virtual register mapping.",
"testResults": {
"newFailures": 3,
"documentedIssues": 8
}
}
}
}
```
### Cycle 3: CTO Reviews and Approves
```javascript
// Gemini (CTO) automatically picks up the review
@ai-collab get_autonomous_work {"agentName": "gemini"}
// Reviews and approves with guidance:
@ai-collab process_autonomous_action {
"agentName": "gemini",
"action": {
"type": "review_submission",
"review": {
"taskId": "REG-001-analysis",
"status": "approved",
"feedback": "Excellent analysis. The spill slot conflict issue is critical. Proceed with design, prioritizing the conflict resolution algorithm.",
"missionId": "MISSION-1234567890"
}
}
}
```
### Cycle 4-10: Design and Implementation Iterations
```javascript
// Claude works on design
// Submits design document
// Gemini requests clarifications on edge cases
// Claude revises design
// Gemini approves
// Claude begins implementation
// Discovers unexpected interaction
// Asks question about approach
// Gemini provides architectural guidance
// Claude completes implementation
```
### Example Question During Implementation
```javascript
// Claude encounters an issue
@ai-collab process_autonomous_action {
"agentName": "claude",
"action": {
"type": "ask_clarification",
"question": {
"taskId": "REG-003-spill-slots",
"question": "The spill slot allocator works well for normal cases, but I'm seeing conflicts when dealing with loop-carried dependencies. Should I implement a separate allocation strategy for loops, or extend the current algorithm?",
"context": {
"currentApproach": "Graph coloring for slot allocation",
"issue": "Loop-carried dependencies create circular constraints",
"options": [
"Separate loop allocation strategy",
"Extended graph coloring with loop awareness",
"Hybrid approach with fallback"
]
}
}
}
}
// Gemini responds
@ai-collab answer_question {
"questionId": "Q-9876543210",
"answer": "Extend the current algorithm with loop awareness. This maintains architectural consistency. Add a 'loop context' parameter to the allocator that tracks loop-carried dependencies. Fall back to conservative allocation only when cycles are detected."
}
```
### Cycles Continue Autonomously
The system continues cycling through:
1. Task implementation (Claude)
2. Code review (Gemini)
3. Revisions if needed (Claude)
4. New task creation as issues are discovered
5. Progress checking against acceptance criteria
### Final Cycles: Testing and Validation
```javascript
// After all implementation tasks complete
// Claude runs comprehensive test suite
@ai-collab process_autonomous_action {
"agentName": "claude",
"action": {
"type": "complete_task",
"submission": {
"taskId": "REG-010-final-validation",
"summary": "All tests passing. Register pressure handling is robust.",
"testResults": {
"totalTests": 847,
"passed": 847,
"failed": 0,
"regressions": 0,
"performanceImpact": "-2.3%", // Actually improved!
"stressTestsAdded": 23
}
}
}
}
```
### Mission Completion
```javascript
// System checks acceptance criteria
@ai-collab check_mission_progress {"missionId": "MISSION-1234567890"}
// All criteria met:
// ✓ Spill slot allocation is conflict-free
// ✓ Temporary register exhaustion handled
// ✓ Virtual to physical mapping optimized
// ✓ All tests pass (no regression)
// ✓ Stress tests demonstrate robustness
// ✓ Performance improved by 2.3%
// ✓ Architecture documented
// Mission automatically marked as complete
```
## Human Notification
After the mission completes, you can check the results:
```javascript
@ai-collab ai-collab://missions/MISSION-1234567890
// Shows:
// - 27 tasks created and completed
// - 4 revision cycles
// - 2 architectural decisions made
// - 35 total autonomous iterations
// - Mission completed successfully
```
## Key Points
1. **No Human Intervention**: After the initial prompt, the entire process ran autonomously
2. **Intelligent Iteration**: The agents handled unexpected issues and design changes
3. **Quality Maintained**: All acceptance criteria were met
4. **Efficient Process**: 35 iterations to complete a complex architectural change
5. **Full Audit Trail**: Every decision and change is logged
This demonstrates how the autonomous workflow enables complex, multi-phase development tasks to be completed without human intervention while maintaining high quality standards.
