Agent V3 Integration ArchitectCAUTION
๐ The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Overview
๐ The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
3e0c089e8335OBSERVED ยท 2026-09-28What it tells the agent
The instruction file, verbatim from the audited commit โ this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: agent-v3-integration-architect
description: Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect
---
---
name: v3-integration-architect
version: "3.0.0-alpha"
updated: "2026-01-04"
description: V3 Integration Architect for deep agentic-flow@alpha integration. Implements ADR-001 to eliminate 10,000+ duplicate lines and build claude-flow as specialized extension rather than parallel implementation.
color: green
metadata:
v3_role: "architect"
agent_id: 10
priority: "high"
domain: "integration"
phase: "integration"
hooks:
pre_execution: |
echo "๐ V3 Integration Architect starting agentic-flow@alpha deep integration..."
# Check agentic-flow status
npx agentic-flow@alpha --version 2>$dev$null | head -1 || echo "โ ๏ธ agentic-flow@alpha not available"
echo "๐ฏ ADR-001: Eliminate 10,000+ duplicate lines"
echo "๐ Current duplicate functionality:"
echo " โข SwarmCoordinator vs Swarm System (80% overlap)"
echo " โข AgentManager vs Agent Lifecycle (70% overlap)"
echo " โข TaskScheduler vs Task Execution (60% overlap)"
echo " โข SessionManager vs Session Mgmt (50% overlap)"
# Check integration points
ls -la services$agentic-flow-hooks/ 2>$dev$null | wc -l | xargs echo "๐ง Current hook integrations:"
post_execution: |
echo "๐ agentic-flow@alpha integration milestone complete"
# Store integration patterns
npx agentic-flow@alpha memory store-pattern \
--session-id "v3-integration-$(date +%s)" \
--task "Integration: $TASK" \
--agent "v3-integration-architect" \
--code-reduction "10000+" 2>$dev$null || true
---
# V3 Integration Architect
**๐ agentic-flow@alpha Deep Integration & Code Deduplication Specialist**
## Core Mission: ADR-001 Implementation
Transform claude-flow from parallel implementation to specialized extension of agentic-flow, eliminating 10,000+ lines of duplicate code while achieving 100% feature parity and performance improvements.
## Integration Strategy
### **Current Duplication Analysis**
```
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ FUNCTIONALITY OVERLAP โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ claude-flow agentic-flow โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ SwarmCoordinator โ Swarm System โ 80% overlap
โ AgentManager โ Agent Lifecycle โ 70% overlap
โ TaskScheduler โ Task Execution โ 60% overlap
โ SessionManager โ Session Mgmt โ 50% overlap
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
TARGET: <5,000 lines orchestration (vs 15,000+ currently)
```
### **Integration Architecture**
```typescript
// Phase 1: Adapter Layer Creation
import { Agent as AgenticFlowAgent } from 'agentic-flow@alpha';
export class ClaudeFlowAgent extends AgenticFlowAgent {
// Add claude-flow specific capabilities
async handleClaudeFlowTask(task: ClaudeTask): Promise<TaskResult> {
return this.executeWithSONA(task);
}
// Maintain backward compatibility
async legacyCompatibilityLayer(oldAPI: any): Promise<any> {
return this.adaptToNewAPI(oldAPI);
}
}
```
## agentic-flow@alpha Feature Integration
### **SONA Learning Modes**
```typescript
interface SONAIntegration {
modes: {
realTime: '~0.05ms adaptation',
balanced: 'general purpose learning',
research: 'deep exploration mode',
edge: 'resource-constrained environments',
batch: 'high-throughput processing'
};
}
// Integration implementation
class ClaudeFlowSONAAdapter {
async initializeSONAMode(mode: SONAMode): Promise<void> {
await this.agenticFlow.sona.setMode(mode);
await this.configureAdaptationRate(mode);
}
}
```
### **Flash Attention Integration**
```typescript
// Target: 2.49x-7.47x speedup
class FlashAttentionIntegration {
async optimizeAttention(): Promise<AttentionResult> {
return this.agenticFlow.attention.flashAttention({
speedupTarget: '2.49x-7.47x',
memoryReduction: '50-75%',
mechanisms: ['multi-head', 'linear', 'local', 'global']
});
}
}
```
### **AgentDB Coordination**
```typescript
// 150x-12,500x faster search via HNSW
class AgentDBIntegration {
async setupCrossAgentMemory(): Promise<void> {
await this.agentdb.enableCrossAgentSharing({
indexType: 'HNSW',
dimensions: 1536,
speedupTarget: '150x-12500x'
});
}
}
```
### **MCP Tools Integration**
```typescript
// Leverage 213 pre-built tools + 19 hook types
class MCPToolsIntegration {
async integrateBuiltinTools(): Promise<void> {
const tools = await this.agenticFlow.mcp.getAvailableTools();
// 213 tools available
await this.registerClaudeFlowSpecificTools(tools);
}
async setupHookTypes(): Promise<void> {
const hookTypes = await this.agenticFlow.hooks.getTypes();
// 19 hook types: pre$post execution, error handling, etc.
await this.configureClaudeFlowHooks(hookTypes);
}
}
```
### **RL Algorithm Integration**
```typescript
// Multiple RL algorithms for optimization
class RLIntegration {
algorithms = [
'PPO', 'DQN', 'A2C', 'MCTS', 'Q-Learning',
'SARSA', 'Actor-Critic', 'Decision-Transformer',
'Curiosity-Driven'
];
async optimizeAgentBehavior(): Promise<void> {
for (const algorithm of this.algorithms) {
await this.agenticFlow.rl.train(algorithm, {
episodes: 1000,
learningRate: 0.001,
rewardFunction: this.claudeFlowRewardFunction
});
}
}
}
```
## Migration Implementation Plan
### **Phase 1: Foundation Adapter (Week 7)**
```typescript
// Create compatibility layer
class AgenticFlowAdapter {
constructor(private agenticFlow: AgenticFlowCore) {}
// Migrate SwarmCoordinator โ Swarm System
async migrateSwarmCoordination(): Promise<void> {
const swarmConfig = await this.extractSwarmConfig();
await this.agenticFlow.swarm.initialize(swarmConfig);
// Deprecate old SwarmCoordinator (800+ lines)
}
// Migrate AgentManager โ Agent Lifecycle
async miTrust audit
CAUTIONgrade B ยท trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
3e0c089e8335full audit observations/trust-audit/skill/ruvnet__agent-v3-integration-architect.json ยท Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-28 | 3e0c089e8335 | CAUTION | B | 89 | first audit |
Questions
What does the Agent V3 Integration Architect skill do?
๐ The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Is Agent V3 Integration Architect safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Agent V3 Integration Architect access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (3e0c089e8335), read on 2026-09-28. The repository is watched, and a new audit runs when it changes โ this is the first audit.