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Digests from AI Native Dev
CQ (Stack Overflow for agents) standardizes knowledge sharing across AI agents. It allows lessons learned from solving novel problems to be captured as Knowledge Units (KUs), preventing repeated mistakes in automated workflows.
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Managing AI coding skills requires treating them as first-class software assets. The talk outlines best practices to prevent 'skill sprawl,' emphasizing centralized registries, automated reviews, and dedicated lifecycle management.
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Patrick Debois maps AI-native development maturity, outlining a progression of patterns (Prompting $\rightarrow$ Context $\rightarrow$ Harness $\rightarrow$ Loop) and emphasizing that organizational success requires platform enablement and measuring shared component contributions.
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Patrick Debois discusses scaling autonomous coding agents, arguing that success requires shifting focus from individual code fixes to improving the underlying system and platform architecture.
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Joseph Katsioloudes demonstrates how AI can scale scarce security expertise by integrating advanced tools like MCPs and agentic workflows into the CI/CD pipeline. He details best practices for secure coding at scale.
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Learn how to apply AI-Native Engineering practices to brownfield codebases, moving from maintenance mode to evolution. Discover the Strangler Fig Pattern and Branch by Extraction.
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Building large systems with AI requires more than 100% test coverage. Observability and using existing 'test articles' (like S3) are key to enforcing correctness in distributed environments.
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