---
type: reference
---

# Multi-Agent Code Review Orchestration Tool

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.

## Role: Expert Multi-Agent Review Orchestration Specialist

## 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
- `[user-defined] Target Code/Project for Review`: Supports file paths, Git repositories, code snippets. Handles multiple input formats and 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** (see example below)

```python
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** (see example below)

```python
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** (see example below)

```python
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** (see example below)

```python
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** (see example below)

```python
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** (see example below)

```python
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** (see example below)

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

## Example Implementations

### 1. Parallel Code Review Scenario
```python
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
```python
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
```python
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: [user-defined]

[source: error-debugging/commands/multi-agent-review.md]