AI Agents, Meet Test Driven Development
AI Summary
Overview
- Presented by Anita, focusing on effective AI Solutions using test-driven development (TDD).
Key Points
- Adoption of TDD
- Companies utilizing TDD build more reliable systems for production.
- Evolution of AI capabilities in coding and production.
- Past and Present of AI in Development
- AI adoption has increased, with better coding models emerging.
- Cursor AI: Fastest-growing SaaS product, illustrating robust development practices.
- Challenges: Hallucinations and overfitting of models.
- New Training Methods
- Deep seek R1: Model trained without labeled data using reinforcement learning.
- Implementing Chain of Thought reasoning for complex problem solving.
- Test-Driven Development Process
- Experimentation: Test various prompting techniques (e.g., few-shot, Chain of Thought).
- Evaluation: Create thorough datasets to assess model performance under multiple conditions.
- Scaling: Develop flexible testing frameworks to accommodate changing AI behavior.
- Deployment: Monitor model behavior in real-time, implement fallback options for API reliability.
- Continuous Improvement
- Capture feedback and edge cases post-deployment for ongoing enhancements.
- Build caching mechanisms to optimize responses and reduce costs.
- Fine-tune models based on production data for improved efficiency.
- Agentic Workflows
- Overview of various levels of agentic behaviors (L0 to L4), ranging from simple API calls to fully autonomous decision-making agents.
- Emphasis on the importance of understanding context and planning within AI workflows.
- Development of complex tools and techniques to enhance productivity.
Conclusion
- Emphasizing the importance of TDD and agentic workflows in building robust AI applications.
- Presentation includes practical examples and a demo of an SEO agent workflow that exemplifies these principles.