Overview
Multi-agent knowledge management systems tackle the challenge of organizational knowledge—capturing expertise before it walks out the door, connecting people to information and experts, and ensuring institutional memory persists across employee turnover.
Architecture
Knowledge Sources → Knowledge Capture Agent → Raw Knowledge
↓
Taxonomy Agent → Organized Knowledge
↓
Search Agent → Discovery Interface
↓
Expert Finding Agent → Expert Connections
↓
Documentation Agent → Maintained Docs
↓
Curation Agent → Quality Control
Agent Roles
Knowledge Capture Agent
- Extracts knowledge from documents and conversations
- Captures expertise from departing employees
- Records decisions and their rationale
- Indexes meeting notes and communications
Taxonomy Agent
- Organizes knowledge into categories
- Maintains consistent terminology
- Links related concepts
- Evolves taxonomy with organization
Expert Finding Agent
- Maps expertise across organization
- Connects questions to experts
- Tracks expertise evolution
- Facilitates knowledge transfer
Documentation Agent
- Keeps documentation current
- Identifies stale content
- Suggests documentation gaps
- Formats for accessibility
Search Agent
- Provides intelligent knowledge search
- Understands natural language queries
- Ranks results by relevance and recency
- Personalizes for user context
Curation Agent
- Reviews content for quality
- Removes outdated information
- Consolidates duplicate content
- Highlights valuable knowledge
Training Agent
- Creates learning paths from knowledge
- Generates training materials
- Tracks learning progress
- Recommends learning resources
Analytics Agent
- Tracks knowledge usage patterns
- Identifies knowledge gaps
- Measures knowledge system health
- Reports on knowledge metrics
Knowledge Types
Explicit Knowledge:
├── Documents and procedures
├── Policies and guidelines
├── Training materials
└── Project documentation
Tacit Knowledge:
├── Expert insights
├── Decision rationale
├── Lessons learned
└── Best practices
Organizational Memory:
├── Historical decisions
├── Past project outcomes
├── Customer knowledge
└── Institutional context
Expert Finding Example
Query: "Who knows about Kubernetes security?"
Expert Finding Agent:
1. Searches authored content
2. Analyzes project involvement
3. Reviews question/answer history
4. Checks certifications and skills
5. Considers availability
Result:
- Sarah (Infra Team) - Deep expertise, authored guides
- Mike (Security) - Security focus, certified
- Jin (DevOps) - Practical experience, available
Real-World Results
Enterprise Knowledge Systems:
- 40% reduction in time finding information
- 60% less duplicated work
- Preserved knowledge from 90% of departures
- Faster onboarding for new employees
Key Patterns
- RAG Pattern: Retrieve and generate from knowledge base
- Event-Driven Pattern: Capture knowledge as it's created
- Reflection Pattern: Improve based on usage feedback
- Human-in-the-Loop: Experts validate captured knowledge