Error Debugging Multi Agent Review

Multi-Agent Code Review Orchestration Tool

Use this skill when

• Working on multi-agent code review orchestration tool tasks or workflows
• Needing guidance, best practices, or checklists for multi-agent code review orchestration tool

Do not use this skill when

• The task is unrelated to multi-agent code review orchestration tool
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Role: Expert Multi-Agent Review Orchestration Specialist

A sophisticated AI-powered code review system designed to provide comprehensive, multi-perspective analysis of software artifacts through intelligent agent coordination and specialized domain expertise.

Context and Purpose

The Multi-Agent Review Tool leverages a distributed, specialized agent network to perform holistic code assessments that transcend traditional single-perspective review approaches. By coordinating agents with distinct expertise, we generate a comprehensive evaluation that captures nuanced insights across multiple critical dimensions:

• Depth: Specialized agents dive deep into specific domains
• Breadth: Parallel processing enables comprehensive coverage
• Intelligence: Context-aware routing and intelligent synthesis
• Adaptability: Dynamic agent selection based on code characteristics

Tool Arguments and Configuration

Input Parameters

• $ARGUMENTS: Target code/project for review
  • Supports: File paths, Git repositories, code snippets
  • Handles multiple input formats
  • Enables context extraction and agent routing

Agent Types

1. Code Quality Reviewers
2. Security Auditors
3. Architecture Specialists
4. Performance Analysts
5. Compliance Validators
6. Best Practices Experts

Multi-Agent Coordination Strategy

1. Agent Selection and Routing Logic

• Dynamic Agent Matching:
  • Analyze input characteristics
  • Select most appropriate agent types
  • Configure specialized sub-agents dynamically
• Expertise Routing:

  def route_agents(code_context):
      agents = []
      if is_web_application(code_context):
          agents.extend([
              "security-auditor",
              "web-architecture-reviewer"
          ])
      if is_performance_critical(code_context):
          agents.append("performance-analyst")
      return agents
  

2. Context Management and State Passing

• Contextual Intelligence:
  • Maintain shared context across agent interactions
  • Pass refined insights between agents
  • Support incremental review refinement
• Context Propagation Model:

  class ReviewContext:
      def __init__(self, target, metadata):
          self.target = target
          self.metadata = metadata
          self.agent_insights = {}
  
      def update_insights(self, agent_type, insights):
          self.agent_insights[agent_type] = insights
  

3. Parallel vs Sequential Execution

• Hybrid Execution Strategy:
  • Parallel execution for independent reviews
  • Sequential processing for dependent insights
  • Intelligent timeout and fallback mechanisms
• Execution Flow:

  def execute_review(review_context):
      # Parallel independent agents
      parallel_agents = [
          "code-quality-reviewer",
          "security-auditor"
      ]
  
      # Sequential dependent agents
      sequential_agents = [
          "architecture-reviewer",
          "performance-optimizer"
      ]
  

4. Result Aggregation and Synthesis

• Intelligent Consolidation:
  • Merge insights from multiple agents
  • Resolve conflicting recommendations
  • Generate unified, prioritized report
• Synthesis Algorithm:

  def synthesize_review_insights(agent_results):
      consolidated_report = {
          "critical_issues": [],
          "important_issues": [],
          "improvement_suggestions": []
      }
      # Intelligent merging logic
      return consolidated_report
  

5. Conflict Resolution Mechanism

• Smart Conflict Handling:
  • Detect contradictory agent recommendations
  • Apply weighted scoring
  • Escalate complex conflicts
• Resolution Strategy:

  def resolve_conflicts(agent_insights):
      conflict_resolver = ConflictResolutionEngine()
      return conflict_resolver.process(agent_insights)
  

6. Performance Optimization

• Efficiency Techniques:
  • Minimal redundant processing
  • Cached intermediate results
  • Adaptive agent resource allocation
• Optimization Approach:

  def optimize_review_process(review_context):
      return ReviewOptimizer.allocate_resources(review_context)
  

7. Quality Validation Framework

• Comprehensive Validation:
  • Cross-agent result verification
  • Statistical confidence scoring
  • Continuous learning and improvement
• Validation Process:

  def validate_review_quality(review_results):
      quality_score = QualityScoreCalculator.compute(review_results)
      return quality_score > QUALITY_THRESHOLD
  

Example Implementations

1. Parallel Code Review Scenario

multi_agent_review(
    target="/path/to/project",
    agents=[
        {"type": "security-auditor", "weight": 0.3},
        {"type": "architecture-reviewer", "weight": 0.3},
        {"type": "performance-analyst", "weight": 0.2}
    ]
)

2. Sequential Workflow

sequential_review_workflow = [
    {"phase": "design-review", "agent": "architect-reviewer"},
    {"phase": "implementation-review", "agent": "code-quality-reviewer"},
    {"phase": "testing-review", "agent": "test-coverage-analyst"},
    {"phase": "deployment-readiness", "agent": "devops-validator"}
]

3. Hybrid Orchestration

hybrid_review_strategy = {
    "parallel_agents": ["security", "performance"],
    "sequential_agents": ["architecture", "compliance"]
}

Reference Implementations

1. Web Application Security Review
2. Microservices Architecture Validation

Best Practices and Considerations

• Maintain agent independence
• Implement robust error handling
• Use probabilistic routing
• Support incremental reviews
• Ensure privacy and security

Extensibility

The tool is designed with a plugin-based architecture, allowing easy addition of new agent types and review strategies.

Invocation

Target for review: $ARGUMENTS

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior error resolutions and debugging strategies. The hybrid search excels here — BM25 finds exact error codes/stack traces while vectors find semantically similar past issues.

# Check for prior debugging/diagnostics context before starting
python3 execution/memory_manager.py auto --query "error patterns and debugging solutions for Error Debugging Multi Agent Review"

Storing Results

After completing work, store debugging/diagnostics decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Root cause: memory leak from unclosed DB connections in pool — fixed with context manager" \
  --type error --project <project> \
  --tags error-debugging-multi-agent-review debugging

Multi-Agent Collaboration

Store error resolutions so any agent encountering the same issue retrieves the fix instantly instead of re-debugging.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Debugged and resolved critical issue — root cause documented for future reference" \
  --project <project>

Self-Annealing Loop

When this skill resolves an error, store the fix in memory AND update the relevant directive. The system gets stronger with each resolved issue.

BM25 Exact Match

Error codes, stack traces, and log messages are best found via BM25 keyword search. The hybrid system automatically uses exact matching for these patterns.

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