Epic: Custom Agent Management System for Discovery & Development

Overview
Enable dynamic creation, management, and lifecycle control of specialized agents within the memory/agent folder to support research-driven development workflows. This system allows orchestrators to build domain-specific agent teams with persistent knowledge stores that can be consulted throughout project lifecycles.
User Story
As a project orchestrator
I want to define and manage custom agents with specialized knowledge
So that I can leverage targeted expertise during discovery, research, and implementation phases while maintaining full control over my development resources.
Core Capabilities
1. Agent Definition & Creation

Custom Agent Framework: Standardized structure following memory/agents/readme specifications
Knowledge Integration: Direct import from 3rd party research and internal hive research
Auto-generation Support: Ability to supplement custom agents with auto-generated complementary agents
Template System: Reusable agent templates for common specializations (coding, domain expertise, architecture)

2. Specialized Agent Types

Domain Knowledge Agents: Subject matter experts for specific business domains
Architectural Agents: Technical experts for system design and patterns
Coding Agents: Language/framework-specific development specialists
Research Agents: Information gathering and analysis specialists

3. Agent Lifecycle Management

Deployment: Seamless integration into swarm development workflows
Consultation: On-demand expert advice during implementation
Persistence: Long-lived agents with accumulated project knowledge
Retirement: Clean removal of obsolete agents ("firing" capability)
Portability: Copy agents between projects via folder transfer

4. Knowledge & Memory System

Persistent Storage: Individual knowledge stores per agent
Memory Continuity: Agents retain context across sessions
Knowledge Evolution: Agents grow expertise over time
Selective Retention: Curated knowledge management

Technical Requirements
Agent Structure
Copymemory/agents/
├── {agent-name}/
│ ├── config.json # Agent configuration
│ ├── knowledge/ # Domain-specific knowledge base
│ ├── memory/ # Persistent memory store
│ ├── capabilities.json # Agent skills and limitations
│ └── lifecycle.json # Creation, updates, retirement log
Integration Points

Discovery Phase: Research-to-agent pipeline
Development Phase: Agent consultation APIs
Swarm Integration: Seamless agent participation in development workflows
Project Management: Agent roster and assignment tracking

Acceptance Criteria
Phase 1: Foundation

Agent folder structure and standards defined
Basic agent creation from research inputs
Agent configuration and capability definition
Simple consultation mechanism

Phase 2: Lifecycle Management

Agent deployment to swarm development
Persistent memory and knowledge storage
Agent retirement/removal capabilities
Cross-project agent portability

Phase 3: Advanced Features

Auto-generation of complementary agents
Agent performance and contribution tracking
Knowledge evolution and learning mechanisms
Team composition optimization

Success Metrics

Research-to-Implementation Speed: Reduced time from discovery to development start
Knowledge Retention: Persistent expertise across project phases
Development Quality: Improved implementation outcomes through specialized consultation
Resource Efficiency: Optimal agent team composition and lifecycle management

Dependencies

Existing memory/agent folder structure
Swarm development framework
Research input processing capabilities
Knowledge storage and retrieval systems

Risks & Mitigations

Knowledge Drift: Implement version control for agent knowledge
Agent Sprawl: Establish governance for agent creation and retirement
Integration Complexity: Phased rollout with clear interfaces
Performance Impact: Monitor agent consultation overhead

This epic transforms the development process from reactive coding to proactive, research-driven implementation with persistent, specialized expertise readily available throughout the project lifecycle.

Claude Flow notes - align the above feature description as best fit to the exisiting arhitecture.
Possible extension points- A2A Protocol with agent card descriptions for interfacing with external agents.

Start by reading the existing memory/agents/readme specifications and inspecting the memory/agent folder structure. Map the proposed config.json, knowledge, memory, capabilities.json, and lifecycle.json files to the existing architecture and swarm integration points. Done means the phased foundation, lifecycle, and advanced capabilities have concrete interfaces and acceptance criteria aligned with the current system.

Written by the indexing model from the issue text.