Multi-Agent Collaborative Programming Practice: A Complete Process Review of Sound Effect Mode Button Refactoring

Around 9 PM on November 13, 2025, I completed what seemed like a simple UI refactoring task—unifying the visual styles of voice mode and music mode buttons in the sound effect functionality. The special aspect of this task was that it adopted a “main agent + multiple sub-agents collaborative” programming mode.

This article will completely review the entire collaborative process, deeply analyze the pros and cons of this emerging programming model, and its applicability to different complexity tasks.

I. Task Panorama: What Actually Happened in This Collaborative Programming?

1.1 Task Background and Requirements

Core Requirement: Unify the visual styles of voice mode and music mode buttons in the sound effect functionality interface, solving the current design’s inconsistent style problem.

What seemed like a simple UI adjustment actually involved coordination of multiple code layers:

Layout file style definitions
State management logic in Activity
State control in ViewModel
BLE communication state synchronization
Color resource management strategy

1.2 Complete Collaborative Process

The entire task execution followed a standard “main agent decision/implementation, subagent responsible for reading code & running builds” closed loop:

sequenceDiagram
    participant U as User
    participant M as Main Agent
    participant E as requirements-code-explorer
    participant B as build-tester

    U->>M: Propose sound effect button unification requirement
    M->>E: Analyze sound effect functionality code structure
    E->>M: Return structured analysis report
    M->>M: Make technical decisions based on analysis
    M->>M: Implement code modifications
    M->>B: Build and verify modifications
    B->>M: Return build and Lint reports
    M->>U: Summarize complete modification report

Step 1: requirements-code-explorer sub-agent deep analysis of code

Upon receiving instructions, the sub-agent systematically scanned the entire sound effect functionality module:

Layout Layer: Located activity_sound_effect.xml, identified buttons using different styles (Elevated vs Outlined)
Logic Layer: Analyzed SoundEffectActivity.kt, clarified button state management logic
State Layer: Checked SoundEffectsViewModel.kt, understood state flow mechanism
Communication Layer: Scanned SoundEffectsRepository.java, organized BLE communication protocol
Resource Layer: Extracted strings.xml text and colors.xml primary color configuration

Finally output a structured report containing key information such as file responsibilities, call chains, constant definitions, interaction flows, etc.

Step 2: Main agent makes technical decisions based on analysis

Based on the “internal project parameters” provided by the sub-agent, the main agent quickly locked onto core issues:

UI Level: Two button XML styles were inconsistent causing visual differences
Logic Level: Activity + ViewModel joint control state management had optimization space
Architecture Level: Lacked unified state style management mechanism

The technical solution became clear:

Unify button style definitions in XML
Introduce selector resource files to manage different state colors
Simplify manual color setting logic in Activity

Step 3: Main agent precisely locates and implements modifications

Benefiting from the precise location (file path + line number range) provided by the sub-agent, the main agent’s modification actions were very efficient:

<!-- Before modification -->
<Button android:id="@+id/btn_voice_mode" style="@style/Widget.Material3.Button.ElevatedButton" />
<Button android:id="@+id/btn_music_mode" style="@style/Widget.Material3.Button.OutlinedButton" />

<!-- After modification -->
<Button android:id="@+id/btn_voice_mode" style="@style/Widget.Material3.Button" />
<Button android:id="@+id/btn_music_mode" style="@style/Widget.Material3.Button" />

Step 4: build-tester sub-agent responsible for build verification

The main agent passed the modification scope and key path information to build-tester:

Build Scope: Focus on checking feature_sound_effect module
Key Paths: UI rendering, state switching, BLE communication
Verification Dimensions: Build success, resource packaging correctness, Lint rule compliance

Step 5: build-tester returns professional build conclusions

The build report contained information in three dimensions:

Build Status: ✅ Compile successful, resource packaging normal
Lint Analysis: Identified several potential issues
- LiveData null safety risks
- String.format missing Locale settings
- Some Views missing contentDescription
Fix Suggestions: Provided specific solutions for each problem

Step 6: Main agent summarizes and reports to user

Finally formed a complete “technical change report”:

This UI modification compiled successfully, resources correct
Detailed list of Lint issues and fix suggestions
Explained implementation continued project’s MVVM + BLE communication pattern, maintaining architectural consistency

II. Core Advantages of Collaborative Programming Mode

2.1 Clear Responsibility Division, Reduced Cognitive Load

This mode decomposes originally complex programming tasks into specialized sub-tasks:

requirements-code-explorer: Focus on code exploration and context organization
- Core capabilities: Large-scale code scanning, pattern recognition, information compression
- Output: Structured “internal project parameters” documentation
Main Agent: Focus on requirement understanding, solution design, code implementation
- Core capabilities: Business understanding, architectural decisions, code writing
- Cognitive load: Simplified from “code reading + understanding + implementation” to “understanding + implementation”
build-tester: Focus on build and quality verification
- Core capabilities: Build process execution, error analysis, quality checks
- Output: Standardized build and quality reports

Cognitive Advantage: The main agent is no longer overwhelmed by large-scale code reading, can think more from business and architectural perspectives.

2.2 High Context Utilization Efficiency, Optimized Token Consumption

Context dilemma in traditional single Agent mode:

# Traditional method - context explosion
context = "Please read the following 20 files, help me understand the sound effect functionality implementation..."
files = read_entire_module_files("sound_effect")  # Massive token consumption

Context optimization in collaborative mode:

# Collaborative method - precise context
analysis = code_explorer.explore("sound_effect module", focus="button styles")
# analysis = {
#     "key_files": ["activity.xml", "SoundEffectActivity.kt", "ViewModel.kt"],
#     "button_styles": {"voice": "Elevated", "music": "Outlined"},
#     "state_management": "Activity + ViewModel pattern"
# }
context = f"Based on analysis report: {analysis}, design unified button style solution"

Effect: Use one model as a “compression/retrieval layer”, concentrating precious context bandwidth on deep reasoning and implementation.

2.3 Complete Quality Assurance System, Independent Build Review

The existence of build-tester sub-agent brings two significant benefits:

1. Prevent “changed but doesn’t compile” basic errors

Main agent doesn’t need to remember specific Gradle/Maven build commands
Has a dedicated role responsible for “execute build + analyze error logs”
Reduces build failures caused by environment differences or command errors

2. Proactively discover potential code quality issues

Lint issues discovered this time:
- LiveData null safety (2 locations)
- String.format missing Locale settings (1 location)
- View missing contentDescription (3 locations)
- Code complexity too high warning (1 location)

This mechanism makes the entire development process closer to CI pipeline: Code modification → automatic build → static analysis → quality report → human review

2.4 Cost-Benefit Optimization, Rational Model Resource Allocation

In collaborative mode, tasks of different complexities are assigned to models with different capabilities:

I/O-intensive tasks (code scanning, file location, pattern matching)
- Suitable for using economical models
- Core requirements: accuracy, coverage, structured output
Reasoning-intensive tasks (requirement understanding, solution design, code implementation)
- Suitable for using high-performance models
- Core requirements: creativity, judgment, code quality

Cost Structure Optimization: Concentrate expensive computing resources on the most valuable links, achieving “using good steel on the blade’s edge.”

2.5 Strong Traceability and Explainability

Each sub-agent’s output adopts a structured format, forming a complete evidence chain:

{
  "task_id": "sound_effect_button_refactor_20251113",
  "analysis_agent": {
    "input": "Analyze sound effect functionality button style implementation",
    "output": {
      "files_analyzed": 12,
      "key_findings": ["inconsistent button styles", "dispersed state management"],
      "suggested_approach": "unify styles, introduce selector"
    }
  },
  "build_agent": {
    "input": "Verify build impact of button style modifications",
    "output": {
      "build_status": "SUCCESS",
      "lint_issues": 5,
      "resource_changes": 2
    }
  }
}

Value Manifestation:

Code Review Friendly: Provides complete decision process and basis
Easy Problem Traceback: Can clearly trace “why it was modified this way at that time”
Effective Knowledge Accumulation: Facilitates team accumulation of best practices and lessons learned

III. Challenges and Risks of Collaborative Programming Mode

3.1 Increased Process Overhead, Decreased Cost-Effectiveness for Small Tasks

Actual Time Cost Comparison:

Single Agent Mode (Simple UI modification):
- Requirement understanding: 30 seconds
- Code location: 2 minutes
- Code modification: 1 minute
- Local verification: 30 seconds
Total: 4 minutes

Multi-Agent Collaborative Mode (Same task):
- Requirement understanding: 30 seconds
- Subtask construction: 1 minute
- Code Explorer analysis: 2 minutes
- Main Agent solution design: 2 minutes
- Code implementation: 1 minute
- Build Tester verification: 2 minutes
- Result summary: 1 minute
Total: 9.5 minutes

Conclusion: For simple, independent small tasks, the time cost of collaborative mode is 2-3 times that of single Agent mode.

3.2 Complex Collaboration Chain, Protocol Quality Becomes Key

Multi-agent collaboration places high requirements on interface protocols:

Main Agent → Code Explorer Protocol:

"Analyze sound effect functionality module, focusing on:
1. Button style definitions in UI layout files
2. Button state management logic (Activity + ViewModel)
3. Related color resources and constant definitions
4. State synchronization mechanism in BLE communication protocol
Please return structured report containing file paths, code snippets, improvement suggestions"

Code Explorer → Main Agent Return Format:

{
  "module_overview": "Sound effect functionality module is responsible for...",
  "key_files": [
    {"path": "activity_sound_effect.xml", "role": "UI layout", "lines": "77-91"},
    {"path": "SoundEffectActivity.kt", "role": "state management", "key_methods": ["updateButtonStates()"]}
  ],
  "findings": [
    {"type": "inconsistent styles", "severity": "medium", "description": "Two buttons use different styles"}
  ],
  "suggestions": ["unify button styles", "introduce selector resources"]
}

Risk Points: Any unclear description on either end can lead to information transmission distortion.

3.3 Decreased Direct Code Perception Capability of Main Agent

Risks of over-relying on sub-agents:

Potential Problem Scenarios:

Code Explorer misses key files or logic branches
Sub-agent has biased understanding of complex business logic
Only provides code fragments lacking complete context
Ignores important boundary conditions or exception handling

Consequences: Main agent may make technical decisions based on incomplete information, while lacking direct perception of complete code itself.

Mitigation Strategies:

Main agent retains “spot check authority” over key code
Sub-agent outputs include “confidence” assessments
Establish multi-round verification mechanisms

3.4 Increased Debugging Complexity, More Difficult Problem Location

Single Agent Mode Debugging Chain:

Result incorrect → Directly check Prompt → Adjust Prompt → Regenerate

Multi-Agent Collaborative Debugging Chain:

Result incorrect
    ↓
Main Agent misunderstanding? → Code Explorer analysis error? → Build Tester verification error?
    ↓                    ↓                      ↓
Check main Prompt         Check analysis Prompt          Check build Prompt
    ↓                    ↓                      ↓
Adjust main decision logic   Adjust analysis strategy            Adjust verification standards

Debugging Cost: Collaborative mode problem location requires checking more links, significantly increasing debugging complexity.

IV. Task Applicability Analysis

4.1 Very Suitable Scenarios

Complexity Characteristics:

Involves multi-layer architecture calls (UI → Business → Data → External)
Cross-multiple files/modules linkage modifications
Needs to understand complex business logic and historical evolution
High requirements for code quality and maintainability

Typical Scenario Examples:

✅ Payment process refactoring (involving UI, business logic, risk control, accounting)
✅ Permission system upgrade (involving frontend, backend, database, cache)
✅ Configuration center transformation (involving configuration management, deployment strategy, caching mechanisms)
✅ Sound effect functionality optimization (this case: UI + state management + BLE communication)

4.2 Scenarios Requiring Cautious Evaluation

Complexity Characteristics:

Single file local modifications
Independent functionality new development
Interaction scenarios with extremely high real-time requirements
Clear modification scope with controllable impact

Typical Scenario Examples:

⚠️ Modify string text
⚠️ Adjust button color values
⚠️ Add simple utility functions
⚠️ Independent script development

4.3 Hybrid Mode Strategy

Recommend implementing “automatic task complexity identification” in the main Agent:

def analyze_task_complexity(user_request):
    complexity_indicators = {
        "file_count": estimate_files_involved(user_request),
        "module_crossing": check_cross_module_impact(user_request),
        "logic_depth": analyze_logic_complexity(user_request),
        "risk_level": assess_change_risk(user_request)
    }

    if complexity_indicators["score"] > threshold:
        return "COLLABORATIVE_MODE"  # Enable multi-agent collaboration
    else:
        return "DIRECT_MODE"         # Direct processing mode

V. Engineering Practice Recommendations

5.1 Standardize Sub-Agent Interface Protocols

Code Explorer Standard Output Format:

{
  "task_id": "unique_identifier",
  "analysis_scope": "Module boundaries and scope",
  "key_files": [
    {
      "path": "file path",
      "role": "responsibility description",
      "importance": "high|medium|low",
      "key_sections": [{"line_range": "start-end", "description": "function description"}]
    }
  ],
  "findings": [
    {
      "type": "issue type",
      "severity": "critical|high|medium|low",
      "location": "file location",
      "description": "detailed description",
      "suggestion": "improvement suggestion"
    }
  ],
  "dependencies": ["external dependency list"],
  "risks": ["potential risk points"],
  "confidence": 0.95
}

Build Tester Standard Output Format:

{
  "task_id": "unique_identifier",
  "build_status": "SUCCESS|FAILURE|WARNING",
  "build_details": {
    "duration": "build time",
    "modules_built": ["list of built modules"],
    "resource_changes": ["list of resource changes"]
  },
  "lint_analysis": {
    "error_count": 0,
    "warning_count": 5,
    "issues": [
      {
        "type": "lint type",
        "severity": "error|warning|info",
        "file": "file location",
        "line": "line number",
        "message": "problem description",
        "suggestion": "fix suggestion"
      }
    ]
  },
  "recommendations": ["follow-up optimization suggestions"]
}

5.2 Establish Task Tracking and Audit Mechanism

Task Log Structure:

{
  "session_id": "collaborative_task_20251113_210000",
  "user_request": "user original requirement",
  "agents_involved": ["main", "code_explorer", "build_tester"],
  "timeline": [
    {
      "timestamp": "2025-11-13T21:00:00Z",
      "agent": "main",
      "action": "analyze_task",
      "input": "task analysis input",
      "output": "task complexity assessment"
    },
    {
      "timestamp": "2025-11-13T21:01:00Z",
      "agent": "code_explorer",
      "action": "explore_codebase",
      "input": "exploration scope and focus",
      "output": "code analysis report"
    }
  ],
  "final_result": {
    "changes_made": ["specific modification list"],
    "quality_metrics": {"build_success": true, "lint_issues": 5},
    "user_satisfaction": "satisfaction assessment"
  }
}

5.3 Improve Quality Assurance Mechanisms

Multi-level Quality Checks:

Syntax Level: Compilation checks, static code analysis
Logic Level: Unit test, integration test coverage
Architecture Level: Design pattern consistency, dependency relationship rationality
Business Level: Functional correctness, user experience completeness

VI. Future Outlook

6.1 Scalable Sub-Agent Ecosystem

Based on current successful practices, can gradually introduce more specialized sub-agents:

performance-analyzer: Performance analysis and optimization suggestions
security-scanner: Security vulnerability detection and fix suggestions
test-generator: Automatic generation of unit tests and integration tests
documentation-writer: Automatic generation of technical documentation and API descriptions
code-reviewer: Simulate senior engineer code review

6.2 Intelligent Task Scheduling Optimization

Develop intelligent task dispatcher, automatically select optimal Agent combinations based on task characteristics:

def optimize_agent_allocation(task):
    task_profile = analyze_task_characteristics(task)

    if task_profile.is_performance_critical:
        return ["main", "performance_analyzer", "build_tester"]
    elif task_profile.is_security_sensitive:
        return ["main", "security_scanner", "code_reviewer", "build_tester"]
    elif task_profile.is_complex_business_logic:
        return ["main", "code_explorer", "documentation_writer", "build_tester"]
    else:
        return ["main", "build_tester"]  # Lightweight mode

VII. Conclusion

This sound effect mode button refactoring task successfully demonstrated the complete value chain of multi-agent collaborative programming mode:

🔍 code_explorer → 🧠 main_agent → 🔨 build_tester → 📋 Complete Report

Each Agent plays to its professional strengths, forming an efficient collaboration pipeline:

Problem Exploration → Solution Design → Code Implementation → Quality Verification → Result Delivery

Core Value Summary:

Structured Thinking: Decomposes complex programming tasks into professional sub-tasks
Quality Assurance: Independent build and verification links reduce error probability
Scalability: Flexibly adjust Agent combinations based on task complexity
Traceability: Complete decision process recording facilitates knowledge accumulation

Applicable Principles:

✅ Complex Tasks: Cross-module, multi-level business logic changes
⚠️ Simple Tasks: Evaluate cost-effectiveness, avoid over-engineering
🔄 Hybrid Mode: Dynamically select processing strategy based on task characteristics

Multi-agent collaborative programming is not a silver bullet, but the advantages it demonstrates when handling complex, cross-domain programming tasks provide us with a completely new engineering approach. As technology matures and patterns improve, this collaboration method is expected to become one of the mainstream paradigms for future AI-assisted programming.

This article is based on the sound effect functionality refactoring practice on November 13, 2025. All code and architectural details come from real project experience. If you have related questions, welcome to discuss in the comments section.