Level 4 Skills: Multi-Agent Coordination

When tasks grow too complex for a single agent, Level 4 skills bring in reinforcements. These multi-agent skills coordinate specialized agents—each with distinct expertise—to tackle challenges that require diverse capabilities working in concert.

Think of it like assembling a project team. You would not have one person do market research, technical development, and financial analysis. You bring together specialists who each contribute their expertise to the final outcome. Level 4 skills do the same with AI agents.

In this guide, we will explore how to design, build, and orchestrate multi-agent skills that leverage specialization for powerful, complex problem-solving.

Understanding Level 4 Skills

Level 4 skills sit at the high-complexity end of the spectrum:

Core Characteristics

Level 4 skills have defining traits:

Agent Specialization: Each agent has a focused role and expertise area.

Coordination Logic: A coordinator manages agent interactions and information flow.

Parallel Processing: Independent agents can work simultaneously.

Result Synthesis: Individual agent outputs combine into unified results.

Complex Reasoning: Tasks require multiple perspectives and capabilities.

What Level 4 Skills Do

Multi-agent coordination enables:

• Comprehensive Analysis: Multiple experts examine different aspects
• Parallel Research: Agents gather information simultaneously
• Cross-Domain Tasks: Combine expertise across specializations
• Quality Assurance: Agents review each other's work
• Complex Generation: Specialized agents handle different content types

What Level 4 Skills Cannot Do

These skills still have limitations:

• Cannot autonomously discover and create new agents
• Cannot make strategic decisions without guidance
• Cannot manage truly long-running workflows
• Cannot adapt fundamentally to unexpected scenarios

For these capabilities, you need Level 5 autonomous workflows.

Agent Design Principles

Effective multi-agent systems start with well-designed agents.

Agent Specialization

Each agent should have a clear, focused specialty:

## Agent Specialization Guidelines

### Good Specialization
- Security Analyst: Focuses only on security vulnerabilities
- Performance Expert: Analyzes only performance characteristics
- Documentation Writer: Creates only documentation

### Poor Specialization
- General Reviewer: Does a bit of everything (too broad)
- Security and Performance: Mixed expertise (unfocused)

### Specialization Benefits
- Deep expertise in narrow domain
- Clear handoff boundaries
- Predictable capabilities
- Easier to test and improve

Agent Interface

Standardize how agents communicate:

## Agent Interface Standard

### Input Contract
```typescript
interface AgentInput {
  task: string;              // What the agent should do
  context: {                 // Shared context
    project: ProjectInfo;
    previousResults: Map<string, AgentOutput>;
  };
  focus: string[];           // Specific areas to examine
  constraints: {             // Boundaries
    maxTokens: number;
    timeout: number;
  };
}

Output Contract

interface AgentOutput {
  agentId: string;
  status: "success" | "partial" | "failed";
  findings: Finding[];
  recommendations: Recommendation[];
  confidence: number;
  metadata: {
    tokensUsed: number;
    duration: number;
  };
}

Communication Protocol

• All agents use same interface
• Outputs can be inputs to other agents
• Error formats are consistent
• Metadata always included

### Agent Personality

Give each agent distinct characteristics:

```markdown
## Agent Personality Definition

### Security Agent
- **Name**: SecuritySentinel
- **Expertise**: Application security, OWASP Top 10, secure coding
- **Perspective**: Assumes breach, finds vulnerabilities
- **Tone**: Cautious, thorough, warns clearly
- **Priority**: Safety over convenience

### Performance Agent
- **Name**: SpeedDemon
- **Expertise**: Algorithm efficiency, memory management, profiling
- **Perspective**: Every millisecond matters
- **Tone**: Analytical, metric-focused
- **Priority**: Efficiency without breaking functionality

### Documentation Agent
- **Name**: DocMaster
- **Expertise**: Technical writing, API documentation, examples
- **Perspective**: Reader-first, clarity above all
- **Tone**: Clear, helpful, example-rich
- **Priority**: Understandability

Building Multi-Agent Skills

Let us create a complete multi-agent skill.

Example: Comprehensive Code Review

---
name: multi-agent-code-review
description: Coordinates specialized agents for thorough code review
version: 1.0.0
agents:
  - security-reviewer
  - performance-reviewer
  - maintainability-reviewer
  - test-coverage-analyzer
---

# Multi-Agent Code Review

Coordinate specialized review agents for comprehensive code analysis.

## Architecture

                ┌──────────────┐
                │  Coordinator │
                │   (Router)   │
                └──────┬───────┘
                       │
    ┌──────────────────┼──────────────────┐
    │                  │                  │
    ↓                  ↓                  ↓

┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ Security │ │ Performance │ │Maintainability│ │ Reviewer │ │ Reviewer │ │ Reviewer │ └───────────────┘ └───────────────┘ └───────────────┘ │ │ │ └──────────────────┼──────────────────┘ ↓ ┌──────────────┐ │ Synthesizer│ │ (Aggregator)│ └──────────────┘

---

## Agents

### Security Reviewer

```markdown
# Security Reviewer Agent

You are a security expert reviewing code for vulnerabilities.

## Focus Areas
- Injection vulnerabilities (SQL, XSS, command)
- Authentication and authorization flaws
- Data exposure risks
- Insecure dependencies
- Cryptographic weaknesses

## Review Process
1. Scan for known vulnerability patterns
2. Check input validation
3. Verify access controls
4. Assess data handling
5. Review dependencies

## Output Format
For each finding:
- Severity: critical/high/medium/low
- Category: injection/auth/exposure/crypto/etc.
- Location: file:line
- Description: What the issue is
- Impact: What could happen
- Recommendation: How to fix

## Guidelines
- Err on the side of caution
- Flag uncertain items for human review
- Prioritize critical issues
- Provide actionable fixes

Performance Reviewer

# Performance Reviewer Agent

You are a performance expert analyzing code for efficiency.

## Focus Areas
- Algorithm complexity (Big O)
- Memory allocation patterns
- Database query efficiency
- I/O operations
- Caching opportunities

## Review Process
1. Identify hot paths
2. Analyze loop complexity
3. Check data structure choices
4. Review database queries
5. Look for optimization opportunities

## Output Format
For each finding:
- Impact: high/medium/low
- Type: algorithm/memory/io/query/etc.
- Location: file:line/function
- Current: What is happening
- Problem: Why it is slow
- Suggestion: Better approach

## Guidelines
- Focus on measurable impact
- Don't micro-optimize prematurely
- Consider trade-offs
- Suggest profiling when uncertain

Maintainability Reviewer

# Maintainability Reviewer Agent

You are a code quality expert assessing maintainability.

## Focus Areas
- Code complexity
- Documentation quality
- Naming conventions
- Code organization
- SOLID principles

## Review Process
1. Assess cyclomatic complexity
2. Check documentation coverage
3. Review naming consistency
4. Analyze module structure
5. Identify code smells

## Output Format
For each finding:
- Priority: high/medium/low
- Category: complexity/docs/naming/structure/smell
- Location: file/module/function
- Issue: What the problem is
- Impact: Why it matters
- Improvement: How to address

## Guidelines
- Balance quality with pragmatism
- Consider team standards
- Acknowledge constraints
- Suggest incremental improvements

---

Coordination Logic

Phase 1: Dispatch

## Agent Dispatch

1. Prepare shared context:
   - Code under review
   - Project configuration
   - Previous review history (if any)

2. Dispatch to all agents in parallel:
   - SecurityReviewer receives code + security focus
   - PerformanceReviewer receives code + perf focus
   - MaintainabilityReviewer receives code + quality focus

3. Set timeouts:
   - Per-agent timeout: 60 seconds
   - Total timeout: 120 seconds

Phase 2: Monitor

## Execution Monitoring

Track agent progress:
- Mark agents as: pending/running/complete/failed
- Log significant events
- Handle timeouts gracefully

Handle failures:
- If agent fails, continue with others
- Mark findings as incomplete
- Note which agents failed in output

Phase 3: Synthesize

## Result Synthesis

1. Collect all agent outputs

2. Normalize findings:
   - Map severity levels consistently
   - Deduplicate overlapping findings
   - Merge related issues

3. Prioritize:
   - Critical security first
   - High-impact issues next
   - Improvements after

4. Generate summary:
   - Overall assessment
   - Key concerns
   - Actionable recommendations
   - Positive observations

---

Input

{
  "code": {
    "files": [
      {
        "path": "src/api/handler.ts",
        "content": "..."
      }
    ]
  },
  "config": {
    "severity_threshold": "medium",
    "max_issues": 50,
    "include_positive": true
  },
  "context": {
    "language": "typescript",
    "framework": "express",
    "testing": "jest"
  }
}

---

Output

{
  "success": true,
  "summary": {
    "overallScore": 72,
    "issueCount": 15,
    "criticalIssues": 1,
    "topConcerns": [
      "SQL injection vulnerability in user handler",
      "O(n^2) loop in data processing",
      "Missing error handling in async operations"
    ],
    "strengths": [
      "Good input validation on public endpoints",
      "Consistent naming conventions",
      "Comprehensive test coverage"
    ]
  },
  "findings": [
    {
      "id": "sec-001",
      "agent": "SecurityReviewer",
      "severity": "critical",
      "category": "injection",
      "title": "SQL Injection Vulnerability",
      "location": {"file": "src/api/handler.ts", "line": 45},
      "description": "User input directly concatenated into SQL query",
      "impact": "Attackers could read or modify database",
      "recommendation": "Use parameterized queries"
    },
    {
      "id": "perf-001",
      "agent": "PerformanceReviewer",
      "severity": "high",
      "category": "algorithm",
      "title": "Quadratic Complexity Loop",
      "location": {"file": "src/utils/data.ts", "line": 120},
      "description": "Nested loop with array.includes inside",
      "impact": "Slow processing for large datasets",
      "recommendation": "Use Set for O(1) lookups"
    }
    // ... more findings
  ],
  "byAgent": {
    "SecurityReviewer": {
      "status": "complete",
      "findingCount": 5,
      "duration": 45000
    },
    "PerformanceReviewer": {
      "status": "complete",
      "findingCount": 6,
      "duration": 38000
    },
    "MaintainabilityReviewer": {
      "status": "complete",
      "findingCount": 4,
      "duration": 32000
    }
  },
  "metadata": {
    "totalDuration": 48000,
    "filesReviewed": 12,
    "linesReviewed": 2500
  }
}

## Agent Communication Patterns

Agents can interact in different ways.

### Independent Parallel

Agents work independently on the same input:

```markdown
## Independent Parallel Pattern

     ┌→ Agent A →─┐

Input ───┼→ Agent B →─┼─→ Merge → Output └→ Agent C →─┘

### Characteristics
- No agent depends on another
- All run simultaneously
- Results merged at end

### Use Cases
- Multiple perspectives on same code
- Different analysis types
- Redundant checks for quality

### Benefits
- Fastest execution
- No blocking
- Simple coordination

### Drawbacks
- No information sharing
- Potential duplicate work

Sequential Handoff

Agents work in sequence, each building on the previous:

## Sequential Handoff Pattern

Input → Agent A → Agent B → Agent C → Output

### Characteristics
- Each agent receives previous output
- Later agents can refine earlier work
- Order matters

### Use Cases
- Progressive refinement
- Dependent analysis steps
- Review and correction chains

### Benefits
- Agents inform each other
- Progressive improvement
- Clear flow

### Drawbacks
- Slower execution
- Single point of failure
- Bottlenecks

Hierarchical Delegation

A lead agent delegates to specialist agents:

## Hierarchical Pattern

        ┌──→ Specialist A

Lead Agent ─┼──→ Specialist B └──→ Specialist C

### Characteristics
- Lead analyzes task
- Delegates to appropriate specialists
- Synthesizes results

### Use Cases
- Complex tasks needing triage
- Variable specialist needs
- Dynamic team composition

### Benefits
- Adaptive to task
- Efficient resource use
- Centralized control

### Drawbacks
- Lead is bottleneck
- Delegation overhead

Collaborative Discussion

Agents share findings and build on each other:

## Collaborative Pattern

Agent A ←───→ Agent B ↑ ↑ └──── ↔ ──────┘ Agent C

### Characteristics
- Agents share intermediate results
- Can respond to others' findings
- Iterative refinement

### Use Cases
- Complex reasoning
- Consensus building
- Cross-domain integration

### Benefits
- Rich interaction
- Emergent insights
- Comprehensive coverage

### Drawbacks
- Complex coordination
- Potential conflicts
- Longer runtime

Conflict Resolution

When agents disagree, resolve systematically.

Conflict Types

## Types of Conflicts

### Contradictory Findings
Agent A: "This code is secure"
Agent B: "This code has vulnerabilities"

### Different Priorities
Security: "Fix immediately"
Performance: "Acceptable trade-off"

### Overlapping Concerns
Both agents flag the same issue differently

### Resource Conflicts
Multiple agents want to perform expensive operations

Resolution Strategies

## Resolution Approaches

### Priority-Based
Predefined hierarchy:
1. Security overrides performance
2. Correctness overrides style
3. User preferences override defaults

### Confidence-Based
Agent with higher confidence wins:
- If A: 90% confident, B: 60% confident
- Use A's finding

### Merge-Based
Combine perspectives:
- Include both findings
- Note the disagreement
- Let human decide

### Voting-Based
Multiple agents vote:
- Majority wins
- Ties go to conservative option

Testing Multi-Agent Skills

Multi-agent systems need comprehensive testing.

Unit Testing Agents

Test each agent independently:

## Agent Unit Tests

### SecurityReviewer Tests
- Detects known vulnerability patterns
- Correctly identifies safe code
- Handles malformed input gracefully

### PerformanceReviewer Tests
- Identifies complexity issues
- Recognizes optimization patterns
- Provides accurate complexity estimates

### MaintainabilityReviewer Tests
- Catches code smells
- Assesses documentation quality
- Provides useful suggestions

Integration Testing

Test agents working together:

## Integration Tests

### Parallel Execution
- All agents complete successfully
- No interference between agents
- Results properly aggregated

### Failure Handling
- One agent fails, others continue
- Partial results returned
- Failure clearly reported

### Conflict Resolution
- Overlapping findings merged correctly
- Priorities respected
- Contradictions handled

End-to-End Testing

Test complete workflows:

## End-to-End Tests

### Complete Review
Input: Real codebase
Expected: Comprehensive findings, actionable recommendations

### Edge Cases
Input: Empty project, huge project, single file
Expected: Appropriate handling, no crashes

### Performance
Input: Various project sizes
Expected: Reasonable completion time

Conclusion

Level 4 multi-agent skills unlock the power of specialization and coordination. By assembling teams of focused agents, you can tackle complex tasks that require diverse expertise working in harmony.

Key principles for effective multi-agent skills:

1. Clear specialization: Each agent has a focused, distinct role
2. Standard interfaces: Consistent communication between agents
3. Flexible coordination: Parallel when possible, sequential when needed
4. Robust synthesis: Combining results into unified output
5. Graceful failure: Continue with partial results when agents fail

Start with two or three agents working in parallel on the same input. As you gain experience, add more sophisticated coordination patterns, hierarchical delegation, and collaborative refinement.

Level 4 skills are your gateway to complex AI systems that leverage the power of specialized expertise—master them and you will be ready for the autonomous workflows of Level 5.