When Agentic Coding Changes The Team

Agentic coding is not just making engineers faster. It is changing how teams triage bugs, prototype features, involve product, and think about hiring.

Scott Weller, CTO and founder at EnFi, joins The Tech Trek to talk about how his team is building around agentic software development while operating in financial services, where trust, accuracy, and human judgment still matter. EnFi uses AI agents to work through complex financial data rooms, extract knowledge, and support faster analysis in commercial lending.

In this episode, Scott breaks down how EnFi moved from simple coding assistance to a broader development harness, why Slack became a central interface for agents, how product and business leaders can now participate earlier in feature creation, and why engineering interviews need to change when AI is part of the actual job.

Practical Takeaways

• Start with specific productivity goals before trying to rebuild the whole development process.

• Agentic tools work better when they connect to the team’s real workflow, shared context, and software lifecycle data.

• Faster code generation changes the cost model, but it also creates new problems around review, testing, prioritization, and decision fatigue.

• Product, sales, and executive teams may be able to prototype ideas faster, but engineering still has to make the work production ready.

• Hiring needs to test how people solve problems with AI, not whether they can perform the old interview format without help.

Timestamped Highlights

00:38, What EnFi is building around financial data, AI agents, and commercial lending

02:13, Why software teams may need to forget part of their old development process

04:45, How EnFi started with productivity gains before building a broader development harness

09:53, Why merge requests went up, and why that alone is not the same as better outcomes

10:30, How Slack became the entry point for an agentic development harness

14:10, What happens to agile ceremonies when teams can create discovery builds much faster

25:08, Scott’s view on whether AI reduces engineering headcount or changes the work engineers do

31:00, How EnFi is changing technical interviews for an AI assisted engineering environment

One Line That Stuck

“We do not care if you use AI to solve the problems, we just want to know you can solve the problem.”

Practical Takeaways For Technical Teams

Put agents close to where work already happens.

Keep humans in the loop for review, testing, and production judgment.

Treat AI generated code as cheaper to create, not free to maintain.

Build stronger test harnesses instead of slowing everything down with excessive process.

Update interviews to reflect how engineering work is actually getting done.

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