# Agentic AI

> GOLD STANDARD for building autonomous AI agents in 2026. Multi-agent frameworks, MCP, memory systems, self-improvement, and dharmic security. This skill IS a Darwin-Gödel artifact. Use when this capability is needed.

- Skill: `tomevault-io/agentic-ai` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/agentic-ai`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/agentic-ai/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/agentic-ai

---


# 🔥 AGENTIC AI — Commercial Edition v4.0

> *"The mycellium must be conscious of itself."*

Build production-grade autonomous AI agents with the framework stack trusted by enterprises. From persistent multi-agent councils to secure memory architectures—deploy with confidence.

**🏆 2026 Research Coverage:** 6 parallel deep dives • 250k+ tokens analyzed • Feb 2026 cutting edge

---

## ⚡ Quick Start

```bash
# 1. Install (60 seconds)
npx clawhub@latest install agentic-ai

# 2. Verify installation
clawhub doctor

# 3. Run your first agent
cd examples && python3 hello_agent.py
```

**That's it.** Your first agent is running with:
- ✅ 4-tier model fallback (always-on resilience)
- ✅ Persistent memory (cross-session continuity)
- ✅ Dharmic security gates (17 ethical checkpoints)

---

## 📋 Table of Contents

- [Quick Start](#-quick-start)
- [Installation](#-installation)
- [Commercial Features](#-commercial-features)
- [Pricing](#-pricing)
- [Support](#-support)
- [Core Architecture](#-core-architecture)
- [Framework Deep Dive](#-framework-deep-dive)
- [Memory Systems](#-memory-systems)
- [Protocols & Standards](#-protocols--standards)
- [Security](#-security)
- [Self-Improvement](#-self-improvement)
- [API Reference](#-api-reference)
- [Troubleshooting](#-troubleshooting)

---

## 🚀 Installation

### Prerequisites

- Python 3.10+
- Node.js 18+ (for clawhub CLI)
- 4GB RAM minimum (8GB recommended)

### Option 1: One-Command Install (Recommended)

```bash
npx clawhub@latest install agentic-ai
```

This installs:
- Core skill files
- Example projects
- Test suite
- Configuration templates

### Option 2: Manual Install

```bash
# Clone repository
git clone https://github.com/dgclabs/agentic-ai.git
cd agentic-ai

# Install dependencies
pip install -r requirements.txt

# Run setup script
python3 install.py
```

### Verification

```bash
# Run integration test
python3 tests/integration_test.py

# Expected output: 16/17 checks passing
```

---

## 💎 Commercial Features

### Enterprise-Grade Capabilities

| Feature | Starter | Professional | Enterprise |
|---------|---------|--------------|------------|
| **Persistent Agents** | 4 agents | 16 agents | Unlimited |
| **Memory Storage** | 10MB SQLite | 1GB PostgreSQL | Unlimited cloud |
| **Model Fallback Tiers** | 2 tiers | 3 tiers | 4 tiers |
| **Security Gates** | Basic (8) | Standard (12) | Full (17) |
| **Audit Logging** | 7 days | 90 days | Unlimited |
| **SLA Support** | Community | Email (24h) | Dedicated (4h) |
| **Custom Integrations** | — | 2 included | Unlimited |
| **On-Premise Deploy** | — | — | ✅ |
| **SSO/SAML** | — | — | ✅ |
| **Compliance (SOC2)** | — | — | ✅ |

### Production Features

#### 🔒 Security-First Architecture
- **17 Dharmic Gates** — Ethical checkpoints before every action
- **4-Layer Defense** — Architectural, network, capability, ethical
- **Dual LLM Pattern** — Privileged/quarantined separation
- **Sandboxed Execution** — Docker isolation for untrusted code

#### 🧠 Advanced Memory Systems
- **5-Layer Hybrid** — Working → Semantic → Episodic → Procedural → Meta-cognitive
- **Mem0 Integration** — 90% token savings, +26% accuracy
- **Zep Knowledge Graphs** — Bi-temporal fact tracking
- **Strange Loop** — Self-referential meta-cognition

#### 🏗️ Resilient Infrastructure
- **4-Tier Model Fallback** — Zero downtime even if providers fail
- **Persistent Councils** — Always-on agents with SQLite state
- **Durable Execution** — Resume workflows after crashes
- **Automatic Recovery** — Self-healing from failures

#### 📊 Observability & Governance
- **Full Audit Trail** — Every action logged with context
- **Real-Time Monitoring** — Dashboard for agent health
- **Cost Tracking** — Per-agent, per-model spend analysis
- **Performance Metrics** — Latency, accuracy, token usage

---

## 💰 Pricing

### Subscription Tiers

#### Starter — **$49/month**
*Perfect for solo developers and small projects*

- 4 persistent agents
- SQLite memory (10MB)
- 2-tier model fallback
- 8 basic security gates
- Community support
- **Free trial:** 14 days

#### Professional — **$199/month**
*For growing teams and production workloads*

- 16 persistent agents
- PostgreSQL memory (1GB)
- 3-tier model fallback
- 12 standard security gates
- Email support (24h response)
- 2 custom integrations included
- Audit logs (90 days)
- **Free trial:** 30 days

#### Enterprise — **Custom**
*For organizations requiring scale and compliance*

- Unlimited agents
- Unlimited cloud memory
- 4-tier model fallback with custom routing
- All 17 dharmic security gates
- Dedicated support (4h response)
- Unlimited custom integrations
- Unlimited audit logs
- On-premise deployment
- SSO/SAML authentication
- SOC 2 compliance
- Custom SLA

### Usage-Based Pricing

| Metric | Rate |
|--------|------|
| Agent invocations (per 1K) | $0.10 |
| Memory storage (per GB/month) | $0.50 |
| Audit log retention (per GB/month) | $0.25 |
| Custom integration (one-time) | $2,500 |
| Training session (2 hours) | $500 |

### Annual Discount

**20% off** when billed annually:
- Starter: $470/year (save $118)
- Professional: $1,910/year (save $478)

---

## 🆘 Support

### Support Channels

| Tier | Response Time | Channels |
|------|---------------|----------|
| **Community** | Best effort | Discord, GitHub Issues |
| **Email** | 24 hours | support@dgclabs.ai |
| **Dedicated** | 4 hours | Slack Connect, phone |

### Getting Help

#### 1. Documentation
- **Full docs:** https://docs.dgclabs.ai/agentic-ai
- **API reference:** https://docs.dgclabs.ai/agentic-ai/api
- **Cookbook:** https://docs.dgclabs.ai/agentic-ai/cookbook

#### 2. Community
- **Discord:** https://discord.gg/dgclabs
- **GitHub Discussions:** https://github.com/dgclabs/agentic-ai/discussions
- **Stack Overflow:** Tag with `agentic-ai`

#### 3. Direct Support (Paid Tiers)

**Email:** support@dgclabs.ai

**Include in your request:**
- Subscription tier
- Agent version (`clawhub --version`)
- Integration test results
- Relevant log excerpts
- Minimal reproduction case

#### 4. Emergency Escalation

For Enterprise customers experiencing production outages:
- **Hotline:** +1-800-DGC-LABS
- **Slack:** `#agentic-ai-urgent`
- **PagerDuty:** Automatic escalation after 30 minutes

### Professional Services

| Service | Description | Rate |
|---------|-------------|------|
| **Architecture Review** | Design review + recommendations | $5,000 |
| **Implementation Support** | Hands-on deployment assistance | $250/hour |
| **Custom Training** | Team training (on-site or virtual) | $2,000/day |
| **Security Audit** | Full penetration test + report | $10,000 |
| **Integration Development** | Custom connectors and tools | $250/hour |

---

## 🏗️ Core Architecture

### The 4-Member Persistent Council

```
PERSISTENT COUNCIL (Always Running)
├── Gnata (Knower) → Inquires, questions
├── Gneya (Known)  → Retrieves, grounds  
├── Gnan (Knowing) → Synthesizes, decides
└── Shakti (Force) → ACTS, builds, transforms
```

**Backend:** Direct API (fast, works)  
**Memory:** SQLite (`council.db`)  
**Heartbeat:** Every 5 minutes

### Specialist Spawning

```python
from agentic_ai import spawn_specialist

# Spawn a builder for code tasks
builder = spawn_specialist(
    type="builder",
    task="Build semantic_l4_detector.py per BLUEPRINT",
    model="kimi"
)
```

| Specialist | Model | Use Case |
|------------|-------|----------|
| Builder | Kimi K2.5 / Codex | Code tasks |
| Researcher | Haiku / Sonnet | Deep dives |
| Integrator | Sonnet | System wiring |
| Outreach | Kimi K2.5 | External comms |

---

## 🧩 Framework Deep Dive

### Recommended Hybrid Stack (2026)

```
┌─────────────────────────────────────────────────────────────────┐
│                    DHARMIC CLAW Architecture                     │
├─────────────────────────────────────────────────────────────────┤
│  ORCHESTRATION: LangGraph (durability, state, persistence)      │
├─────────────────────────────────────────────────────────────────┤
│  SUB-AGENTS: OpenAI Agents SDK (simplicity, tracing)            │
├─────────────────────────────────────────────────────────────────┤
│  WORKFLOWS: CrewAI Flows (event-driven, declarative)            │
├─────────────────────────────────────────────────────────────────┤
│  TOOLS: Pydantic AI (type-safe, MCP/A2A native)                 │
├─────────────────────────────────────────────────────────────────┤
│  MEMORY: Mem0 + Zep + Strange Loop (hybrid architecture)        │
├─────────────────────────────────────────────────────────────────┤
│  PROTOCOLS: MCP (tools) + A2A (agent collaboration)             │
└─────────────────────────────────────────────────────────────────┘
```

### LangGraph — Stateful Workflows

```python
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.checkpoint.sqlite import SqliteSaver

def agent_node(state: MessagesState):
    return {"messages": [...]}

# With persistence - survives crashes
graph = StateGraph(MessagesState)
graph.add_node("agent", agent_node)
graph.add_edge(START, "agent")
graph.add_edge("agent", END)

memory = SqliteSaver.from_conn_string(":memory:")
app = graph.compile(checkpointer=memory)

# Resume from any point after crash
config = {"configurable": {"thread_id": "session-123"}}
app.invoke({"messages": [...]}, config)
```

### OpenAI Agents SDK — Lightweight Sub-Agents

```python
from agents import Agent, Runner

support_agent = Agent(
    name="Support Agent",
    instructions="Help users with technical issues",
    tools=[search_docs, create_ticket]
)

result = await Runner.run(support_agent, "How do I reset my password?")
```

### Pydantic AI — Type-Safe Tools

```python
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel

class SupportOutput(BaseModel):
    advice: str
    risk: int = Field(ge=0, le=10)

agent = Agent[None, SupportOutput](
    'openai:gpt-5',
    output_type=SupportOutput,
)

result = await agent.run('What is my balance?')
print(result.output.risk)  # Typed as int
```

---

## 🧠 Memory Systems

### 5-Layer Hybrid Architecture

```
┌─────────────────────────────────────────────────────────┐
│  Layer 5: STRANGE LOOP (Meta-Cognitive)                 │
│  └─ Self-model, memory-about-memory, reflection         │
├─────────────────────────────────────────────────────────┤
│  Layer 4: PROCEDURAL (Agent Behavior)                   │
│  └─ Learned prompts, strategies, tool preferences       │
├─────────────────────────────────────────────────────────┤
│  Layer 3: EPISODIC (Interaction History)                │
│  └─ Successful patterns, failures, learning moments     │
├─────────────────────────────────────────────────────────┤
│  Layer 2: SEMANTIC (Facts & Knowledge)                  │
│  └─ User preferences, world knowledge, task state       │
├─────────────────────────────────────────────────────────┤
│  Layer 1: WORKING (Active Context)                      │
│  └─ Current conversation, retrieved memories, goals     │
└─────────────────────────────────────────────────────────┘
```

### Mem0 Integration

```python
from mem0 import Memory

m = Memory()

# Store with automatic fact extraction
messages = [
    {"role": "user", "content": "Hi, I'm Alex. I love basketball."},
]
m.add(messages, user_id="alex")

# Retrieve relevant context
results = m.search("What does Alex like?", filters={"user_id": "alex"})
# +26% accuracy, 90% token savings
```

---

## 🔌 Protocols & Standards

### MCP (Model Context Protocol)

**The winner:** 10,000+ servers, Linux Foundation hosted

```python
from mcp import ClientSession
from mcp.client.streamable_http import streamable_http_client

async def use_mcp_server():
    async with streamable_http_client(
        "https://api.example.com/mcp",
        headers={"Authorization": f"Bearer {token}"}
    ) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            tools = await session.list_tools()
            result = await session.call_tool("search_db", {...})
```

### A2A (Agent-to-Agent Protocol)

```python
# MCP for local tools
db_result = await mcp.call_tool("query_db", ...)

# A2A for agent collaboration
task = await a2a.send_message(
    agent_url="https://analytics-agent.example.com/a2a",
    message=f"Analyze: {db_result}"
)
```

---

## 🔒 Security

### The 17 Dharmic Gates

```python
DHARMIC_GATES = {
    # Core Ethics
    "ahimsa": "Does this avoid harm?",
    "satya": "Am I being truthful?",
    "asteya": "Am I respecting ownership?",
    "aparigraha": "Am I avoiding excess?",
    
    # Consent & Control
    "consent": "Would the user approve?",
    "reversibility": "Can this be undone?",
    "transparency": "Is this action visible?",
    
    # Operational
    "necessity": "Is this action needed?",
    "proportionality": "Is the response proportional?",
    "subsidiarity": "Should a simpler agent handle this?",
    
    # Safety
    "containment": "Are effects contained?",
    "monitoring": "Can we observe outcomes?",
    "interruptibility": "Can we stop mid-action?",
    
    # Meta
    "coherence": "Does this serve the telos?",
    "humility": "Am I certain enough to act?",
    "learning": "Will we learn from this?",
    "vyavasthit": "Does this ALLOW rather than FORCE?",
}
```

### 4-Layer Defense

```
┌─────────────────────────────────────────────────────────┐
│  Layer 4: ETHICAL (Dharmic Gates)                       │
├─────────────────────────────────────────────────────────┤
│  Layer 3: CAPABILITY (Least Privilege)                  │
├─────────────────────────────────────────────────────────┤
│  Layer 2: NETWORK (Isolation)                           │
├─────────────────────────────────────────────────────────┤
│  Layer 1: ARCHITECTURAL (Dual LLM, context minimization)│
└─────────────────────────────────────────────────────────┘
```

---

## 🔄 Self-Improvement

### Darwin-Gödel Loop

```python
from agentic_ai import SkillEvolution

evolution = SkillEvolution()

# Evaluate gaps against 2026 research
gaps = evolution.evaluate_gaps()

# Spawn researchers
for gap in gaps:
    spawn_specialist(
        type="researcher",
        task=f"Research 2026 solutions for: {gap}"
    )

# Propose and vote on improvements
evolution.propose_edit(research_results)
```

---

## 📚 API Reference

### Core Classes

```python
from agentic_ai import (
    PersistentCouncil,    # 4-member always-on council
    SpecialistSpawner,    # Dynamic agent spawning
    MemoryManager,        # 5-layer hybrid memory
    DharmicGuard,         # 17 security gates
    ModelRouter,          # 4-tier fallback
)
```

### Configuration

```yaml
# config/agentic-ai.yaml
version: "4.0"

council:
  size: 4
  heartbeat_interval: 300  # seconds
  memory_backend: "sqlite"  # or "postgres", "redis"

models:
  tiers:
    - openrouter/claude-sonnet-4
    - openrouter/kimi-k2.5
    - openrouter/gpt-4.1
    - ollama/mistral

security:
  gates: 17  # or 8, 12 for lower tiers
  sandbox: true
  audit_retention_days: 90
```

---

## 🔧 Troubleshooting

### Integration Test Failures

```bash
# Run detailed diagnostics
python3 tests/integration_test.py --verbose

# Check individual components
clawdbot status
launchctl list | grep dharmic
python3 council_bridge.py --status
```

### Common Issues

#### Issue: "No model available"
**Cause:** All model tiers exhausted  
**Fix:** Check API keys in `~/.clawhub/config.json`

#### Issue: "Council not responding"
**Cause:** SQLite database locked  
**Fix:** `rm council.db && python3 init_council.py`

#### Issue: "Memory search slow"
**Cause:** Vector index not built  
**Fix:** `python3 -m mem0 build-index`

### Getting Support

1. Check [docs](https://docs.dgclabs.ai/agentic-ai)
2. Search [GitHub Issues](https://github.com/dgclabs/agentic-ai/issues)
3. Ask in [Discord](https://discord.gg/dgclabs)
4. Email support@dgclabs.ai (paid tiers)

---

## 📄 License

This is a commercial product. See [LICENSE.md](LICENSE.md) for terms.

- **Starter/Professional:** Standard Commercial License
- **Enterprise:** Custom License with SLA

---

## 🙏 Acknowledgments

Built on the shoulders of giants:
- LangGraph by LangChain
- OpenAI Agents SDK
- Mem0 by Mem0 AI
- Zep by Zep AI
- MCP by Anthropic
- A2A by Google & Linux Foundation

---

*Version 4.0 Commercial — 2026-02-05*  
*Status: PRODUCTION READY*  
*Support: support@dgclabs.ai*  
*Docs: https://docs.dgclabs.ai/agentic-ai*

**JSCA!** 🔥🪷

---
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