Implementing AI-Powered Development with Cursor, MCP, and Advanced Models
Agentic AI represents a paradigm shift from traditional automation to intelligent, autonomous systems capable of reasoning, adapting, and collaborating.
vs 38% for Claude 3 Opus
coding problem success ratevs 20% manual coding
senior engineer interactionsClear, detailed instructions yield better results than concise but ambiguous prompts
Use ### headers and formatting to structure your instructions effectively
Provide examples alongside instructions for better AI understanding
Guide complex reasoning with step-by-step thinking processes
Write code for user authentication
Create a secure user authentication system using JWT tokens. Include:
### Requirements
- Login/logout endpoints
- Password hashing with bcrypt
- Token expiration (24 hours)
- Input validation
### Output Format
- Express.js middleware functions
- Error handling with proper HTTP status codes
Model Context Protocol provides a standardized way to connect AI systems with external data sources and tools, enabling seamless integration across different platforms.
AI Applications
Cursor, VS CodeStandardized Communication
MCP StandardData Sources & Tools
APIs, DatabasesExecute functions and interact with external systems
Share reusable prompt templates across applications
Access and manipulate data from various sources
Test-Driven Development serves as fitness functions in evolutionary algorithms, guiding AI code generation toward optimal solutions.
Quantitative evaluation of candidate solutions in genetic algorithms
Test cases as evolutionary guardrails for AI-generated code
Architectural constraints are precisely validated through automated testing
Test cases serve as clear behavioral documentation for AI interpretation
Failed tests provide targeted feedback for AI code iteration
Natural language interpretation and creative problem exploration
Plan decomposition with safety guardrails and validation
Deterministic execution with optimized performance
Move beyond traditional automation to systems that can reason, adapt, and collaborate autonomously.
Invest time in learning effective prompting techniques - they're the foundation of successful AI collaboration.
Use Model Context Protocol to create seamless integrations between AI systems and your existing tools.
Combine LLM creativity with classical AI precision for robust, reliable systems.
Thank you for your attention. I'm ready to discuss any questions about agentic AI implementation, best practices, or the upcoming demonstration.