More The AI Native Dev episodes
Published 17 Feb 2026
Duration: 1877
Agentic development uses AI agents to automate software tasks, shifting the focus to speed and intent-driven specifications while relying on context management to achieve accurate results.
Most teams think agentic dev is about writing better prompts. It's not. Guy Podjarny and Simon Maple explain why managing context, not crafting prompt...
Agentic development represents a shift in software engineering, emphasizing speed and the use of AI agents to automate tasks that are typically done by humans. This approach relies heavily on managing context, which is essential for aligning agents, maintaining institutional knowledge, and ensuring the accuracy of their outputs. Unlike traditional software development, agentic development is non-deterministic, requiring new practices around evaluation, observation, and iterative refinement of context to ensure effectiveness.
A major challenge in agentic development is the tendency of AI agents to re-implement existing code rather than reuse it, along with their potential reliance on outdated knowledge. Managing agent systems in a cost-effective manner is also a key concern. The development process involves continuous cycles of analysis, generation, evaluation, and iteration, with context being updated as systems evolve. Successful implementation depends on effectively communicating context to agents and integrating it into the broader software development lifecycle, much like onboarding and aligning human teams.
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.