More The AI Native Dev episodes
Published 20 Jan 2026
Duration: 1218
The effectiveness of code development is significantly influenced by context, with impact varying from 33% to 80%, and can be improved through the strategic use of coding agents and automation.
Most AI agents fail because you're using them wrong. Heres what actually works in production. In this episode, Simon Maple sits down with Itamar Fried...
The podcast emphasizes the critical role of context in code development, noting that it significantly impacts the effectiveness of coding agents, which can perform anywhere from 33% to 80% effectively. It examines how multi-agent systems can improve productivity and code quality by assigning distinct rolessuch as coding, reviewing, and planningto individual agents, enabling them to work either in parallel or sequentially. Tools like the OpenHands SDK are introduced as platforms for building complex workflows that involve these agents.
The discussion covers various aspects of agent architecture, including the use of diverse vendors to replicate the diversity of human teams and the importance of context management in minimizing errors. It also highlights tasks well-suited for automation, such as dependency management and code upgrades, while underscoring the need for human oversight in areas requiring verification and trust-building. Additionally, the content outlines a structured approach to addressing recurring issues, such as outdated Java versions, through manual investigation, script-based automation, scaling in parallel, and integration with developers.
16 Jun 2026 AI Security & the Agent-Ready Web: Experts Weigh In
Agentic AI systems face critical security risks from overconfidence, prompt-injection vulnerabilities, bypassable guardrails, and performance-driven development, requiring foundational security measures, developer education, and intent-based design to bridge readiness gaps and ensure safe innovation.
9 Jun 2026 Ryan Lopopolo: OpenAI's Framework for Shipping Code at 70 PRs/Week
The text explores Codex's integration via Chrome DevTools and TypeScript daemons, agentic development's emphasis on autonomous workflows and trustworthiness, harness engineering's structured tool integration, code QA with automation and feedback loops, shifts in code reviews toward strategy, AI agents as onboarding tools, persistent specs over code, balancing specification precision with adaptability, computational costs of token-heavy processes, and adapting team dynamics to agent-centric workflows.
2 Jun 2026 Why Developers Hit a Wall at 4 AI Agents
AI integration in software development faces challenges like limited agent management (1-2 per developer), lower acceptance of AI-generated code (60% merge rate vs. 80% for human), scalability barriers, and the need for improved observability, workflow alignment, and strategic business integration to balance productivity gains with quality and security.
26 May 2026 Don't Secure the Code. Secure the Coder.
The text addresses security challenges in AI and agentic systems, emphasizing unintended risks like reward-seeking behaviors, the need for developer-centric security strategies, novel attack vectors, frameworks adopting agentic principles, and proposed solutions such as the "AI Bill of Materials" alongside risks like data leakage and governance challenges.
19 May 2026 The Hidden Security Risks of AI Coding Agents
Agentic systems introduce heightened security risks through text-based interactions enabling malicious intent encoding, sensitive data access, untrusted inputs, and external system communication, requiring mitigation via SCA, restricted agent access, dynamic analysis, and balancing security with productivity through transparency and adapted security frameworks.