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AI coding agents can produce software faster, but they do not replace the judgment needed to understand the system.
Shaun Patterson, CTO at Titan, joins The Tech Trek to discuss how agentic coding is changing problem solving, development workflows, project management, and technical hiring.
Shaun explains why engineers still need a strong mental model of the systems they are building. AI can generate code, reproduce bugs, research implementation options, and automate repeated debugging work. But it can also keep working on the wrong problem long after a human debugger would have found the answer.
The conversation also gets into a bigger shift in software delivery. If agents can work across much larger pieces of a project, engineering teams may move from managing work at the story level to working at the epic level.
Key Takeaways
• AI speeds up implementation, but engineering judgment still matters.
• Repeated debugging work can become reusable agent skills.
• Faster implementation lowers the cost of testing different technical approaches.
• Hiring increasingly needs to measure how engineers work with AI.
Highlights
02:08 Why AI can abstract work, but not engineering wisdom
06:04 Turning repeated debugging sessions into reusable agent skills
09:47 Why faster development may change traditional project management
12:42 Moving engineering work from stories to epics
16:19 Where agentic coding still creates problems
19:29 How Titan evaluates engineers who use AI
One Line That Stuck
“It abstracts your thinking, but it doesn’t abstract your wisdom.”
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