Product - Self-Evolving AI Operating System

AMPM-AIOPS: Self-Evolving AI Operating System

Not just one AI, but an AI Organization that collaborates.

AMPM-AIOPS is the world’s first self-evolving AI Operating System that enables developers and organizations to build, manage, and evolve autonomous AI agent ecosystems.

Core Architecture

User (Natural Language Commands)
  ↓
AMPM Command Center (Management Interface)
  ↓
AMPM Boss Agent (Orchestration)
  ↓
┌─────────────────────────────┐
│    Agent Layer              │
├─────────────────────────────┤
│  Qiyuan    Jueze    Verify  │
│ (Research) (Execute)(Audit) │
└─────────────────────────────┘
  ↓
┌─────────────────────────────┐
│  Tools / Memory / Runtime   │
└─────────────────────────────┘

Agent Level Permission System

Each agent has clear permissions to ensure security and control:

Default Configuration:

• Qiyuan (Research): Level 2
• Jueze (Execute): Level 4
• Verify Agent: Level 1
• Boss Agent: Level 5

Five-Layer Product Line

Layer 1: Open Source

• Core agent runtime
• Basic tools and integrations
• Community-driven development

Layer 2: Cloud SaaS

• Managed hosting
• Auto-scaling infrastructure
• Monitoring and analytics

Layer 3: Enterprise

• Private deployment
• Custom integrations
• SLA guarantees

Layer 4: Marketplace

• Pre-built agents
• Tool integrations
• Workflow templates

Layer 5: Agent Store

• Community agents
• Premium agents
• Revenue sharing

Key Features

1. Natural Language Command System

Users don’t need to learn programming. Just say:

“Help me improve website SEO, target 50% traffic increase in 3 months.”

System automatically handles:

1. Analyze goal (understand intent)
2. Assign tasks (distribute to Qiyuan/Jueze)
3. Execute work (actual operations)
4. Verify results (confirm completion)
5. Report progress (to user)

2. Multi-Agent Collaboration

AMPM Boss (Orchestrator)

• Task decomposition (break large goals into small tasks)
• Work assignment (assign to suitable agents)
• Final reporting (integrate results for user)

Qiyuan (Research)

• Analyze problems
• Plan solutions
• Find best approaches
• Write documentation

Jueze (Execute)

• Modify code
• Deploy services
• Test functionality
• Fix errors

Verify Agent (Supervisor)

• Check completion
• Run tests
• Prevent errors
• Report anomalies

3. Memory System

• Session Memory: Current task context
• Long-term Memory: Past experiences
• Knowledge Graph: Structured information

Agents learn from past successes and failures, automatically adjust strategies, and optimize workflows.

Use Cases

Case 1: Website Optimization

User Command:

“Improve website SEO and user experience”

System Execution:

1. Qiyuan researches SEO best practices
2. Jueze implements optimizations
3. Verify Agent tests changes
4. Boss Agent reports progress weekly

Case 2: Content Generation

User Command:

“Generate 10 blog posts about AI trends”

System Execution:

1. Qiyuan researches trending topics
2. Jueze writes posts
3. Verify Agent checks quality
4. Boss Agent schedules publication

Case 3: Data Analysis

User Command:

“Analyze user behavior and suggest improvements”

System Execution:

1. Qiyuan collects and analyzes data
2. Jueze implements tracking
3. Verify Agent validates results
4. Boss Agent presents insights

Getting Started

Installation

git clone https://github.com/ampm-aiops/ampm-os.git
cd ampm-os
pip install -r requirements.txt

Quick Start

from ampm import BossAgent

boss = BossAgent()
boss.receive_goal("Improve website SEO")
boss.execute()

Roadmap

Phase 1: MVP (0-3 months) ✅ Current

• Natural language command system
• Boss Agent orchestration
• Task management
• Agent status reporting

Phase 2: Self-Learning (3-6 months)

• Memory system
• Task history learning
• Agent performance evaluation
• Dynamic permission control

Phase 3: Platform (6-12 months)

• Web dashboard
• Agent marketplace
• Multi-user support
• API system

Phase 4: AI Organization (12-24 months)

• Auto agent creation
• Adaptive learning
• Agent store ecosystem
• Enterprise deployment

Success Metrics

Phase 1 Completion

• Natural language command input
• Boss Agent task decomposition
• Qiyuan/Jueze task reception
• Progress tracking
• Result verification

12-Month Goals

• 5000 pages indexed
• 5000-20000 daily visitors
• 5000 GitHub stars

Contact

• Website: https://ampm-aiops.com
• GitHub: https://github.com/ampm-aiops
• Documentation: https://ampm-aiops.com/docs

AMPM-AIOPS: Not just one AI, but an AI Organization that collaborates.