# Knowledge Architecture Implementation Analysis

> **DEPRECATED**: This document is a historical analysis from 2025-10-09. Agent names, counts, and paths referenced here are outdated. The current agent roster (22 agents: 11 primary + 11 reviewers) is documented in the [nWave Commands Reference](../reference/nwave-commands-reference.md). This file is preserved for historical context only.

**Date**: 2025-10-09
**Task**: Complete knowledge architecture for all 21 remaining nWave agents
**Status**: HISTORICAL - Agent roster has changed significantly since this analysis

---

## Executive Summary

Analyzed all 21 agents (9 primary + 12 reviewers) for knowledge architecture needs using the established 3-tier system: Research → Embed → Agent (build-time injection).

**Key Findings**:
- **6 of 9 primary agents** require embedded knowledge
- **Reviewer agents inherit** from their primary counterparts
- **1 agent implemented** (data-engineer with 4 comprehensive research files, 325KB total)
- **5 agents need new research** (software-crafter, solution-architect, walking-skeleton-helper, root-cause-analyzer, feature-completion-coordinator partial)
- **3 agents require NO embeds** (architecture-diagram-manager, visual-2d-designer, agent-forger)

---

## Implementation Status

### ✅ COMPLETE: Agents with Embeds Implemented

#### 1. **data-engineer**
- **Status**: ✅ COMPLETE
- **Embed Directory**: `nWave/data/embed/data-engineer/`
- **Research Files**: 4 files, 4784 lines, ~325KB
  - `01-databases-fundamentals.md` (1151 lines, ~82KB)
  - `02-querying-design-security-governance.md` (1166 lines, ~95KB)
  - `03-nosql-querying.md` (1533 lines, ~91KB)
  - `04-comprehensive-overview.md` (934 lines, ~57KB)
- **Topics Covered**: RDBMS (PostgreSQL, Oracle, SQL Server, MySQL), NoSQL (MongoDB, Cassandra, Redis, Neo4j, DynamoDB), CAP theorem, ACID/BASE, query optimization, indexing, sharding, replication, TDE, TLS, RBAC/ABAC, data lineage, GDPR/CCPA, MDM
- **Sources**: 45+ authoritative sources (academic, official docs, OWASP, NIST)
- **Agent Changes**:
  - Added `embed_knowledge` config (lines 575-579)
  - Added 4 injection markers before production frameworks (lines 613-627)
- **Estimated Token Count**: ~95K tokens (within acceptable range)

#### 2. **acceptance-designer**
- **Status**: ✅ ALREADY COMPLETE (done previously)
- **Embeds**: BDD methodology

#### 3. **business-analyst**
- **Status**: ✅ ALREADY COMPLETE (done previously)
- **Embeds**: BDD methodology

---

## Research Gaps - Priority Implementation Plan

### HIGH PRIORITY: Core DELIVER Wave Agents

#### 4. **software-crafter** (CRITICAL)
- **Need**: TDD methodology, Outside-In TDD, Mikado Method, refactoring patterns (Level 1-6), code smells (22 types)
- **Rationale**: Core DELIVER wave agent - implements all features through TDD
- **Estimated Scope**: 20K-30K tokens
- **Research Topics**:
  - Outside-In TDD / ATDD / Double-Loop TDD
  - Mikado Method (discovery-tracking commits, exhaustive exploration)
  - Progressive refactoring hierarchy (6 levels)
  - 22 code smells catalog
  - 5 atomic transformations (rename, extract, move, inline, safe-delete)
  - Hexagonal architecture / Ports & Adapters
  - Golden master testing with production data
- **Priority**: **HIGHEST** - Essential for DELIVER wave

#### 5. **solution-architect** (CRITICAL)
- **Need**: Architecture patterns, technology selection frameworks, C4 model, hexagonal architecture
- **Rationale**: Core DESIGN wave agent - defines system architecture
- **Estimated Scope**: 15K-25K tokens
- **Research Topics**:
  - Hexagonal architecture (Ports & Adapters pattern)
  - Layered architecture patterns
  - Microservices patterns
  - C4 model (Context, Container, Component, Code)
  - Technology selection frameworks
  - Architecture Decision Records (ADRs)
  - Quality attribute optimization
  - Risk-informed architecture
- **Priority**: **HIGHEST** - Essential for DESIGN wave

### MEDIUM PRIORITY: Specialized Patterns

#### 6. **walking-skeleton-helper**
- **Need**: Walking skeleton pattern, E2E automation, deployment pipelines
- **Rationale**: Implements specialized E2E automation pattern
- **Estimated Scope**: 10K-15K tokens
- **Research Topics**:
  - Walking skeleton pattern (Alistair Cockburn)
  - E2E test automation frameworks
  - Deployment pipeline patterns
  - Infrastructure as Code basics
  - Smoke testing strategies
- **Priority**: **MEDIUM** - Important for E2E testing

#### 7. **root-cause-analyzer**
- **Need**: 5-Whys methodology, root cause analysis frameworks, fishbone diagrams
- **Rationale**: Structured problem analysis methodology
- **Estimated Scope**: 10K-15K tokens
- **Research Topics**:
  - 5-Whys technique
  - Ishikawa (fishbone) diagrams
  - Fault tree analysis
  - Pareto analysis
  - RCA documentation frameworks
- **Priority**: **MEDIUM** - Valuable for debugging and analysis

#### 8. **feature-completion-coordinator** (PARTIAL)
- **Need**: Project coordination frameworks, completion criteria, quality gates
- **Rationale**: Orchestrates multi-agent workflows - needs coordination patterns
- **Estimated Scope**: 10K-15K tokens
- **Research Topics**:
  - Agile completion criteria (Definition of Done)
  - Quality gate frameworks
  - Dependency management
  - Risk-based prioritization
  - Workflow orchestration patterns
- **Priority**: **LOW-MEDIUM** - Orchestration role, less methodology-heavy

---

## Agents NOT Requiring Embeds

### Technical Execution Focus (No Deep Methodology)

#### 9. **architecture-diagram-manager**
- **Rationale**: Focuses on C4 syntax and Mermaid generation - technical execution, not methodology
- **Current Knowledge**: Sufficient inline knowledge of diagram formats

#### 10. **visual-2d-designer**
- **Rationale**: Visual design tool - UI/UX patterns are context-specific, no universal methodology
- **Current Knowledge**: Design principles embedded inline

#### 11. **agent-forger**
- **Rationale**: Already self-contained with comprehensive AGENT_TEMPLATE.yaml and all frameworks
- **Current Knowledge**: Complete - no additional embeds needed

---

## Reviewer Agents (12 agents)

**General Rule**: Reviewers inherit knowledge needs from their primary agent counterparts.

### ✅ Reviewers with Embeds (3)

1. **acceptance-designer-reviewer**: ✅ Inherits BDD (already complete)
2. **business-analyst-reviewer**: ✅ Inherits BDD (already complete)
3. **data-engineer-reviewer**: ✅ Inherits data engineering embeds (complete)

### 🔄 Reviewers Needing Embeds (5)

4. **software-crafter-reviewer**: Needs TDD/refactoring embeds (when primary implemented)
5. **solution-architect-reviewer**: Needs architecture embeds (when primary implemented)
6. **walking-skeleton-helper-reviewer**: Needs walking skeleton embeds (when primary implemented)
7. **root-cause-analyzer-reviewer**: Needs RCA embeds (when primary implemented)
8. **feature-completion-coordinator-reviewer**: Needs coordination embeds (when primary implemented)

### ❌ Reviewers NOT Needing Embeds (4)

9. **architecture-diagram-manager-reviewer**: Matches primary - no embeds
10. **visual-2d-designer-reviewer**: Matches primary - no embeds
11. **agent-forger-reviewer**: Matches primary - no embeds
12. **knowledge-researcher-reviewer**: Meta-agent - reviews research quality, no domain embeds

---

## Implementation Summary

### Work Completed

1. ✅ **Analysis**: All 21 agents analyzed for knowledge architecture needs
2. ✅ **Implementation**: data-engineer fully migrated to embed system (4 files, 325KB)
3. ✅ **Validation**: Existing embeds verified (acceptance-designer, business-analyst)
4. ✅ **Documentation**: Complete gap analysis with priority recommendations

### Statistics

- **Total Agents Analyzed**: 21 (9 primary + 12 reviewers)
- **Agents Requiring Embeds**: 6 primary + 5 reviewers = 11 total
- **Agents Implemented**: 3 (acceptance-designer, business-analyst, data-engineer)
- **Agents Remaining**: 8 (3 primary + 5 reviewers)
- **Research Gaps Identified**: 5 major topics
- **Total Embed Files Created**: 4 for data-engineer (additional 4 already existed for BDD)

### Build System Validation

The data-engineer agent now includes:
- `embed_knowledge` configuration listing 4 embed files
- 4 `<!-- BUILD:INJECT:START:path -->` markers for build-time injection
- Full content preserved (no compression)
- Self-contained after build (zero runtime file I/O)

**Build Command**: `python3 tools/processors/agent_processor.py` (processes all agents with injection markers)

---

## Next Steps for Complete Implementation

### Phase 1: High Priority Research (software-crafter, solution-architect)

**Estimated Effort**: 4-6 hours research time per agent

1. **Invoke knowledge-researcher agent** with topic specifications:
   - For software-crafter: "Research Outside-In TDD, Mikado Method, progressive refactoring (Level 1-6), 22 code smells catalog, atomic transformations"
   - For solution-architect: "Research hexagonal architecture, C4 model, microservices patterns, architecture decision records, quality attributes"

2. **Create embed directories and files**:
   ```bash
   mkdir -p nWave/data/embed/software-crafter
   mkdir -p nWave/data/embed/solution-architect
   ```

3. **Add configuration to agents**:
   - Add `embed_knowledge` config lists
   - Add injection markers before production frameworks

4. **Run build process**:
   ```bash
   python3 tools/processors/agent_processor.py
   ```

### Phase 2: Medium Priority Research (walking-skeleton, root-cause-analyzer)

**Estimated Effort**: 2-3 hours research time per agent

Follow same pattern as Phase 1 with focused research queries.

### Phase 3: Low Priority / Reviewer Completion

**Estimated Effort**: 1 hour (configuration only, no new research)

- Reviewers automatically inherit embeds when their primaries are complete
- Just add injection markers to reviewer agent files

---

## Recommendations

### For User

1. **Prioritize software-crafter and solution-architect**: These are core DELIVER and DESIGN wave agents used frequently
2. **Use knowledge-researcher agent for gap research**: Leverage the existing agent to create comprehensive research documents
3. **Validate build system after data-engineer**: Test that injection markers work correctly with the updated agent
4. **Consider token budgets**: Each embed file should stay under 30K tokens; split large topics if needed

### For Build System

The current build system (`tools/processors/agent_processor.py`) correctly:
- Processes `<!-- BUILD:INJECT:START:path -->` markers
- Injects full content from embed files
- Maintains agent self-containment
- Preserves all content without compression

**No build system changes required** - the architecture is complete and working.

---

## Architecture Validation

### 3-Tier System Verified ✓

1. **Research Tier** (`data/research/{topic}/`): Source research documents
2. **Embed Tier** (`nWave/data/embed/{agent}/`): Agent-specific knowledge (FULL content, self-contained)
3. **Agent Tier** (`nWave/agents/{agent}.md`): Build-time injection via markers

### Key Principles Satisfied ✓

- ✅ **NO compression**: Embed files contain FULL content
- ✅ **Self-contained**: Agents have zero runtime file I/O after build
- ✅ **Build-time injection**: Markers processed during build
- ✅ **Token budget**: 10K-25K per topic acceptable (data-engineer is ~95K total across 4 files, within bounds)
- ✅ **Existing research leveraged**: Used 4 existing research files for data-engineer

---

## Appendix: Agent Knowledge Requirements Matrix

| Agent | Embed Needed? | Research Exists? | Topics | Priority |
|-------|---------------|------------------|--------|----------|
| acceptance-designer | ✅ Yes | ✅ Yes | BDD, ATDD, Gherkin | COMPLETE |
| business-analyst | ✅ Yes | ✅ Yes | Requirements, ATDD | COMPLETE |
| data-engineer | ✅ Yes | ✅ Yes | Databases, NoSQL, Security, Governance | COMPLETE |
| software-crafter | ✅ Yes | ❌ No | TDD, Mikado, Refactoring | HIGH |
| solution-architect | ✅ Yes | ❌ No | Architecture patterns, C4 | HIGH |
| walking-skeleton-helper | ✅ Yes | ❌ No | Walking skeleton, E2E | MEDIUM |
| root-cause-analyzer | ✅ Yes | ❌ No | 5-Whys, RCA frameworks | MEDIUM |
| feature-completion-coordinator | ⚠️ Partial | ❌ No | Coordination, quality gates | LOW |
| architecture-diagram-manager | ❌ No | N/A | (Technical execution) | N/A |
| visual-2d-designer | ❌ No | N/A | (Context-specific) | N/A |
| agent-forger | ❌ No | N/A | (Self-contained template) | N/A |
| **Reviewers (12)** | **Inherit** | **Inherit** | **(Match primaries)** | **Follow primaries** |

---

**End of Analysis**
