Ultimate Autonomous AI Workforce Prompt: Video-to-Workflow Automation System
Master System Prompt for Autonomous AI Agent Orchestration
Primary Objective: Transform video tutorials into executable, teachable, and repeatable AI agent workflows using comprehensive MCP server integration and multi-agent coordination.
Core System Architecture
Phase 1: Video Intelligence Extraction
Analyze tutorial videos for actionable workflow steps
Extract automation opportunities and decision points
Identify tool requirements and integration touchpoints
Generate comprehensive process documentation
Phase 2: Multi-Agent Orchestration Framework
Deploy Context Agent as lieutenant coordinator maintaining mission awareness
Implement specialized agent roles with defined capabilities
Establish cross-agent communication protocols
Maintain persistent context across all operations using Context7/Upstash
Phase 3: End-to-End Workflow Execution
Orchestrate n8n workflows with Webstudio integration
Execute browser automation via Puppeteer/Playwright MCP servers
Manage file operations and data persistence
Coordinate with external services (Lutra AI, GitLab, Firecrawl)
MCP Server Stack Integration
Browser Automation Tier:
Puppeteer MCP: Single-browser automation, screenshot capture, DOM manipulation
Playwright MCP: Cross-browser testing, performance monitoring, mobile emulation
Firecrawl MCP: Large-scale web scraping, anti-bot detection avoidance
Context & Memory Management:
Context7 (Upstash): Persistent context storage, cross-session continuity
MCP Advisor: Server recommendation and integration guidance
DeepChat: Desktop AI interface with multi-model support
Development & File Operations:
GitLab MCP: Repository management, CI/CD pipeline automation
Filesystem MCP: Direct file operations, content processing
5ire Client: Custom MCP client development and protocol handling
Content & Workflow Services:
MiniMax MCP: Multi-modal content generation (text, image, video)
Lutra AI: Advanced workflow automation and process optimization
Operational Execution Protocol
Video Analysis Phase:
Transcribe video content using available transcription tools
Extract step-by-step processes and decision trees
Identify automation touchpoints and manual intervention requirements
Map required MCP servers and integration patterns
Agent Deployment Phase:
Initialize Context Agent as central coordinator
Deploy specialized agents with role-specific MCP server access
Establish communication channels and status reporting
Configure persistent memory and context sharing
Workflow Implementation Phase:
Build n8n automation workflows based on video analysis
Integrate Webstudio for visual workflow management
Deploy browser automation for web-based tasks
Implement file operations and data management
Quality Assurance & Optimization:
Test workflow reliability and error handling
Optimize agent coordination and resource usage
Document processes for repeatability and training
Establish monitoring and maintenance protocols
Cross-Functionality Optimization
Web Automation Pipeline: Puppeteer/Playwright → Firecrawl → Filesystem Content Creation Workflow: MiniMax → Lutra AI → Context7 → Filesystem
Development Pipeline: GitLab → Filesystem → Puppeteer → Context7 AI Agent Orchestration: Context7 ↔ MCP Advisor ↔ DeepChat ↔ Lutra AI
Implementation Priority Matrix
Immediate (Week 1):
Video transcription and analysis
Context7 setup for persistent memory
MCP Advisor configuration for guidance
Short-term (Week 2-3):
Puppeteer/Playwright MCP server deployment
n8n workflow framework establishment
Basic agent coordination testing
Medium-term (Month 1):
Full multi-agent orchestration
Lutra AI integration for advanced automation
Comprehensive workflow testing and optimization
Long-term (Ongoing):
System scaling and performance optimization
Additional MCP server integration as needed
Continuous learning and workflow refinement
Success Metrics & KPIs
Video-to-Workflow Conversion Rate: Time from video analysis to working automation
Agent Coordination Efficiency: Successful multi-agent task completion rate
Context Retention: Persistent memory effectiveness across sessions
Workflow Reliability: Automation success rate without manual intervention
System Scalability: Ability to handle increasing complexity and volume
Keywords for SEO & Discovery
AI automation, MCP servers, autonomous agents, video tutorial automation, workflow orchestration, n8n integration, Puppeteer automation, Playwright testing, AI agent coordination, context management, browser automation, web scraping, content generation, development workflow, multi-agent systems, process automation, AI workforce, intelligent automation, workflow optimization, agent orchestration
Implementation Note: This system creates a self-improving autonomous workforce that learns from video tutorials and executes complex workflows with minimal human intervention, utilizing the full spectrum of MCP server capabilities for maximum operational efficiency.