Summary
In this episode, I spoke with Jonathan Ellis about how AI agents are changing the economics of software development and forcing teams to rethink where human time is best spent. We explored his argument that, now that code generation is dramatically faster, traditional human-centered code review is becoming a poor use of attention. Instead, Jonathan makes the case for shifting review earlier in the lifecycle toward specifications, RFCs, and architecture decisions, while investing more heavily in automated validation, static analysis, fuzzing, and other quality gates that can help verify whether software does what it is supposed to do regardless of how the code was written.
Jonathan also shared lessons from building Brokk AI’s tooling, including Bifrost for fast, agent-friendly static analysis and Mjolnir for coordinating multiple coding agents across harnesses and subscriptions. We discussed the emerging need for better agent-oriented collaboration systems, the risks of blindly outsourcing judgment to AI, and the challenge of training newer engineers in a world where agents can do much of the implementation work. Overall, this conversation examines what remains uniquely valuable for humans as agents take over more of the mechanics of software delivery: setting goals, making product and design decisions, building better feedback loops, and deciding what is worth building in the first place.
Announcements
Interview
Contact Info
Parting Question
Closing Announcements
Links
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
In this episode, I spoke with Jonathan Ellis about how AI agents are changing the economics of software development and forcing teams to rethink where human time is best spent. We explored his argument that, now that code generation is dramatically faster, traditional human-centered code review is becoming a poor use of attention. Instead, Jonathan makes the case for shifting review earlier in the lifecycle toward specifications, RFCs, and architecture decisions, while investing more heavily in automated validation, static analysis, fuzzing, and other quality gates that can help verify whether software does what it is supposed to do regardless of how the code was written.
Jonathan also shared lessons from building Brokk AI’s tooling, including Bifrost for fast, agent-friendly static analysis and Mjolnir for coordinating multiple coding agents across harnesses and subscriptions. We discussed the emerging need for better agent-oriented collaboration systems, the risks of blindly outsourcing judgment to AI, and the challenge of training newer engineers in a world where agents can do much of the implementation work. Overall, this conversation examines what remains uniquely valuable for humans as agents take over more of the mechanics of software delivery: setting goals, making product and design decisions, building better feedback loops, and deciding what is worth building in the first place.
Announcements
- Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
- Your host is Tobias Macey and today I'm interviewing Jonathan Ellis about how to make the best use of human time when using agents to build software
Interview
- Introduction
- How did you get involved in machine learning?
- Agentic coding has become much more viable this year, which dramatically shifts the bottlenecks in software delivery. What are the ways that you are seeing that impact engineering teams?
- As an industry we have built up numerous expectations and "best practices" around the software lifecycle, with many assumptions about the need for human review before sending code into production. How are you seeing those expectations impact teams who have shifted to agentic coding?
- Advanced teams have been working toward removing the human bottleneck in the validation and deployment path for several years now. What are the lessons that we can all learn from them?
- What are the new tools or practices that we need to build or adopt to gain the confidence that we need to deploy AI-written code to end-users?
- What are the highest leverage points of the software lifecycle that we can focus our limited human attention and review processes?
- What are you building that helps us work with agents to produce better software and build confidence in it?
- What lessons can we learn from the changes happening in software engineering that we can apply to the design and development of other agentic workflows?
- What are the most interesting, innovative, or unexpected ways that you have seen Bifrost/Mjolnir/etc. used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on tools for agentic software engineering?
- When is the Brokk suite the wrong choice?
- What do you have planned for the future of Bifrost/Mjolnir/etc.?
Contact Info
Parting Question
- From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers.
Links
- Bifrost
- Mjolnir
- Stop Doing Code Review Blog Post
- SlopCop
- IntelliJ
- ADR == Architecture Decision Record
- RFC == Request for Comments
- Witan Context - Agent-native memory, tasks, and projects (built by Tobias)
- Coverity
- Fuzz Testing
- Apache Cassandra
- Agent Skills
- OpenAI Article - Using Exec Plans To Drive Agents For Longer Tasks
- Vectorize
- SWE-Bench
- Deep-SWE
- Cursor Bench
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
