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is the tendency of AI agents to re-implement existing code rather than reuse it, along with their potential reliance on outdated knowledge.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.
12 May 2026 "AI Doesn't Stand for Artificial Intelligence" Venkat Subramaniam's Take Will Change How You Think About It
AI in software development requires balancing its speed and automation with human accountability for safety and ethics, emphasizing rigorous quality practices, community-driven adoption, critical thinking, and oversight to ensure AI complements rather than replaces human judgment.
5 May 2026 The Creator of Spring Thinks You Can't Code Serious Software With AI
Integrating AI into enterprises via HTTP calls and existing infrastructure requires balancing language agnosticism, deterministic frameworks like GOAT, Java/Kotlin over Python for reliability, and prioritizing explainability, human oversight, and alignment with business logic over overreliance on AI for simple tasks.
28 Apr 2026 What OpenAI, Stripe & ElevenLabs Devs Do Differently Now | AI Native Dev
The text examines challenges in integrating AI into software workflows, highlights AI-native practices like Stripe's Minions automating code tasks, emphasizes balancing human oversight with automation, and explores future trends in agent-native engineering, specialized models, open-source tools, and ethical considerations in AI-driven development.
21 Apr 2026 Logan Kilpatrick on Who Ships AGI, DeepMind and the Problem With More Software
Software developers must adapt to AI's complexity by bridging tools with real-world applications, mastering context engineering, shifting toward high-level design and system architecture, and navigating fragmented ecosystems as AGI emerges as an integrated system rather than a standalone model.
14 Apr 2026 Everything 100 Episodes Revealed About AI Native Dev
AI in software development shifts from code-centric practices to context- and specification-driven approaches, with humans prioritizing decision-making and oversight while AI handles implementation, but challenges like non-human-readable code, alignment with team practices, and contextual accuracy remain critical.