error-debugging-multi-agent-review
Tests & Qualité"Use when working with error debugging multi agent review"
Documentation
Multi-Agent Code Review Orchestration Tool
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Instructions
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:
Tool Arguments and Configuration
Input Parameters
$ARGUMENTS: Target code/project for reviewAgent Types
Multi-Agent Coordination Strategy
1. Agent Selection and Routing Logic
```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
```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
```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
```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
```python
def resolve_conflicts(agent_insights):
conflict_resolver = ConflictResolutionEngine()
return conflict_resolver.process(agent_insights)
```
6. Performance Optimization
```python
def optimize_review_process(review_context):
return ReviewOptimizer.allocate_resources(review_context)
```
7. Quality Validation Framework
```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
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
Best Practices and Considerations
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
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