AI Agents, Meet Test Driven Development

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AI Summary

Summary of Video Transcript

Introduction

• Anita from Vum discusses the benefits of test-driven development in deploying reliable AI solutions.
• She highlights the success of Cursor AI, an AI-powered IDE with rapid growth due to better models, increased AI adoption, and coding being an obvious target for AI disruption.

AI Model Evolution

• Despite improvements in AI models, issues like hallucinations, overfitting, and the need for structured outputs persist.
• New training methods like real reinforcement learning have emerged, exemplified by the DeepSeq R1 model.
• New benchmarks are introduced to measure the performance of reasoning models, such as the “Humanity’s Last Exam.”

Building Reliable AI Products

• Successful AI teams use a structured approach: experimenting, evaluating at scale, and continuous improvement post-deployment.
• Experimentation involves trying different prompting techniques and involving domain experts.
• Evaluation at scale requires creating a dataset to test models and workflows, balancing quality, cost, latency, and privacy.
• Deployment in production involves monitoring, logging, handling API reliability, version control, and decoupling deployments from app updates.
• Continuous improvement includes creating a feedback loop, building a caching layer, and potentially fine-tuning a custom model.

Agentic Workflows

• Agentic workflows are discussed, with different levels of agentic behavior from simple tool use to fully creative workflows.
• Levels range from L0 (basic model calls) to L4 (fully creative and inventive AI workflows).
• L1 is common in production, focusing on orchestration, while L2 is expected to see innovation with planning and reasoning agents.
• L3 and L4 are limited by current models but represent areas of potential innovation.

Practical Demonstration

• Anita demonstrates building an SEO agent that automates keyword research, content analysis, and creation.
• The agent operates between L1 and L2, using a memory component and an iterative process with an editor to refine content.
• The agent saves time by generating a useful first draft based on analyzed context.

Bellum Workflows and Workflow SDK

• Bellum workflows bridge the gap between product and engineering teams, speeding up AI development while adhering to a test-driven approach.
• The newly introduced workflow SDK is customizable, self-documenting, and keeps UI and code in sync.
• It is open source, free, and available on GitHub.

Conclusion

• Anita concludes the presentation, inviting viewers to connect on LinkedIn or reach out via email or Twitter for further discussion on AI.