Podcast
43 articles tagged The AI Native Dev - from Copilot today to AI Native Software Development tomorrow.
An executive analysis of the strategic shift toward open source AI models, highlighting the risks of 'open washing,' the competitive advantage of Chinese open-first policies, and the necessity of national infrastructure for global competitiveness.
An analysis of WebMCP and client-side AI models as critical shifts in web development. Learn how exposing JavaScript functions to agents reduces latency and cost, while local inference enables privacy-focused, offline-capable applications.
Chad Fowler outlines Phoenix Architecture, a framework for treating code as disposable build artifacts. This analysis explores how spec-driven development, immutable infrastructure principles, and AI-generated code can transform software durability and operational efficiency.
Snyk reveals that 13.4% of published AI agent skills contain critical security vulnerabilities. This analysis explores the emerging threat landscape of prompt injections, obfuscated code, and supply chain risks in LLM ecosystems, offering actionable strategies for developers and security teams to mitigate these risks.
An executive analysis of agentic software development, highlighting the critical role of engineering maturity, context management, and containerization. Learn why high-maturity teams outperform low-maturity ones and how to mitigate hallucination risks in enterprise AI adoption.
An executive analysis of agent memory systems, distinguishing between context and memory management. Covers the strategic shift from file-based experimentation to robust database infrastructure, the role of skills as procedural memory, and the future of continuous learning loops in enterprise AI.
Cisco engineers detail the strategic implementation of CodeGuard, a security skill framework for AI coding agents. The analysis covers context optimization, evaluation methodologies, and the shift from model-centric to workflow-centric development strategies in enterprise environments.
Agentic development represents a fundamental paradigm shift driven by non-determinism and intent-based workflows. This analysis explores how context management replaces traditional code-centric practices, introducing a Context Development Lifecycle (CDLC) that integrates with the SDLC. Learn how to mitigate LLM biases, manage costs, and establish continuous evaluation frameworks for scalable AI-driven software engineering.
An executive analysis of how AI agents are transforming observability platforms. This brief covers the strategic importance of OpenTelemetry standardization, the shift from dashboard-centric to agent-first user experiences, and the operational risks of becoming a mere data repository. It provides actionable insights for SaaS leaders navigating the integration of LLMs into DevOps workflows.
Agent skills are emerging as a standard unit of reusable context for AI agents. This analysis explores the shift from ad-hoc markdown files to managed software assets, emphasizing the critical need for automated evaluations, package management, and lifecycle governance to ensure enterprise-grade reliability and performance.
