Agents, IDEs, and the Blast Radius: Practical AI for Software Engineers

Summary
In this episode of the AI Engineering Podcast Will Vincent, Python developer advocate at JetBrains (PyCharm), talks about how AI utilities are revolutionizing software engineering beyond basic code completion. He discusses the shift from "vibe coding" to "vibe engineering," where engineers collaborate with AI agents through clear guidelines, iterative specs, and tight guardrails. Will shares practical techniques for getting real value from these tools, including loading the whole codebase for context, creating agent specifications, constraining blast radius, and favoring step-by-step plans over one-shot generations. The conversation covers code review gaps, deployment context, and why continuity across tools matters, as well as JetBrains' evolving approach to integrated AI, including support for external and local models. Will emphasizes the importance of human oversight, particularly for architectural choices and production changes, and encourages experimentation and playfulness while acknowledging the ethics, security, and reliability tradeoffs that come with modern LLMs.

Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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• Your host is Tobias Macey and today I'm interviewing Will Vincent about selecting and using AI software engineering utilities and making them work for your teamInterview

• Introduction
• How did you get involved in machine learning?
• Software engineering is a discipline that is relatively young in relative terms, but does have several decades of history. As someone working for a developer tools company, what is your broad opinion on the impact of AI on software engineering as an occupation?
• There are many permutations of AI development tools. What are the broad categories that you see?
• What are the major areas of overlap?
• What are the styles of coding agents that you are seeing the broadest adoption for?
• What are your thoughts on the role of editors/IDEs in an AI-driven development workflow?
• Many of the code generation utilities are executed on a developer's computer in a single-player mode. What are some strategies that you have seen or experimented with to extract and share techniques/best practices/prompt templates at the team level?
• While there are many AI-powered services that hook into various stages of the software development and delivery lifecycle, what are the areas where you are seeing gaps in the user experience?
• What are the most interesting, innovative, or unexpected ways that you have seen AI used in the context of software engineering workflows?
• What are the most interesting, unexpected...