🎯 The Vision: From Solo to Swarm

Instead of working with one AI agent at a time, CSVLOD-AI enables you to orchestrate specialized agent teams that work together seamlessly while you maintain complete control.

❌ Before: Single Agent Workflow

  • One conversation at a time
  • Context switching between tasks
  • Manual coordination required
  • Limited specialization

✅ After: Agent Swarm Coordination

  • Multiple specialists working simultaneously
  • Automatic task decomposition
  • Intelligent dependency management
  • Each agent optimized for their role

⚙️ How Agent Swarm Coordination Works

1

Task Decomposition

Describe a high-level goal like "Implement user authentication" and the swarm coordinator automatically breaks it down into specialized subtasks.

2

Agent Assignment

Specialized agents are assigned based on their expertise: Backend Agent handles APIs, Frontend Agent builds UI, Security Agent reviews implementation.

3

Coordinated Execution

Agents work in parallel where possible, respecting dependencies, while sharing context through CSVLOD structure to maintain consistency.

4

Intelligent Integration

The Coordinator Agent ensures all work integrates properly, resolves conflicts, and maintains the overall vision of your project.

🤖 Agent Specializations

🎯

Coordinator

Primary orchestrator and decision maker

  • Task decomposition and distribution
  • Agent coordination and conflict resolution
  • Quality assurance and final integration
  • Human interaction and communication
Context Focus: Considerations, Visions, Landscapes
⚙️

Backend Specialist

Backend development and architecture

  • API design and implementation
  • Database schema and optimization
  • Server architecture and deployment
  • Performance optimization
Context Focus: Standards, Designs, Considerations
🎨

Frontend Specialist

Frontend development and user experience

  • UI/UX implementation
  • Client-side architecture
  • Performance optimization
  • Accessibility compliance
Context Focus: Visions, Outlines, Designs
🔒

Security Auditor

Security assessment and compliance

  • Security vulnerability assessment
  • Code security review
  • Compliance validation
  • Security best practices enforcement
Context Focus: Considerations, Standards
📚

Documentation Agent

Documentation generation and maintenance

  • Code documentation generation
  • User guide creation and updates
  • API documentation maintenance
  • Knowledge base management
Context Focus: Landscapes, Outlines

⚙️ Configuration

Enhanced AGENT-MANIFEST.yaml

The swarm coordination system extends your existing AGENT-MANIFEST.yaml with new swarm configuration:

version: "2.0"
name: "Your Project Repository"
description: "Enhanced with agent swarm coordination capabilities"

# SWARM COORDINATION CONFIGURATION
swarm_config:
  enabled: true
  coordination_mode: "hierarchical"  # hierarchical, peer-to-peer, or hybrid
  max_concurrent_agents: 5
  context_sync_mode: "real-time"    # real-time, batch, or manual
  conflict_resolution: "coordinator_decides"  # coordinator_decides, consensus, or escalate

# AGENT SPECIALIZATIONS
agent_specializations:
  coordinator:
    role: "Primary orchestrator and decision maker"
    responsibilities:
      - "Task decomposition and distribution"
      - "Agent coordination and conflict resolution"
      - "Quality assurance and final integration"
    context_focus: ["considerations", "visions", "landscapes"]
    priority: 1
    
  backend_specialist:
    role: "Backend development and architecture"
    responsibilities:
      - "API design and implementation"
      - "Database schema and optimization"
    context_focus: ["standards", "designs", "considerations"]
    priority: 2
    
  # ... additional agent definitions

🛠️ MCP Server Tools

swarm_init

Initialize agent swarm coordination for a CSVLOD project

Parameters: projectPath

swarm_decompose

Decompose a high-level task into agent-specific subtasks

Parameters: projectPath, taskDescription, requiredContext

swarm_status

Get current status of agent swarm coordination

Parameters: projectPath

swarm_assign

Assign a task to an available specialized agent

Parameters: projectPath, taskId

swarm_complete

Mark a task as completed and update swarm state

Parameters: projectPath, taskId, result

swarm_tasks

Get details about swarm tasks (specific task or filtered view)

Parameters: projectPath, taskId?, agentType?

🏛️ Sovereignty Principles

🔒

Complete Control

You decide which agents to activate, how they coordinate, and when to intervene. No external dependencies.

🏠

Local-First

All coordination happens locally through your MCP server. No cloud dependencies or data sharing.

📊

Transparent Operation

Every task, assignment, and decision is logged and inspectable. No black box behaviors.

🔧

Fully Customizable

Modify agent specializations, coordination rules, and conflict resolution to match your workflow.

🚀 Getting Started

Quick Start with Agent Swarm Coordination

1

Update your AGENT-MANIFEST.yaml

Add swarm_config and agent_specializations sections to enable coordination

2

Initialize Swarm Coordination

Use the MCP server tool swarm_init to activate coordination for your project

3

Decompose Your First Task

Use swarm_decompose with a high-level task description and watch it break down into specialist subtasks

4

Monitor and Coordinate

Use swarm_status and swarm_tasks to monitor progress and coordinate your AI team