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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AI adoption looks very different when mistakes can create legal, financial, and reputational risk.

Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.

The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.

Practical Takeaways

• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.

• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.

• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.

• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.

• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.

Timestamped Highlights

00:47, Acrisure’s shift from insurance brokerage toward fintech and financial tools.

01:44, Why regulated industries face a trust gap with AI and need explainable decisions.

04:41, How data teams are evolving from dashboards to conversational data enablement.

08:28, The build versus buy question and where internal AI tools may still make sense.

10:52, Why AI experimentation can get expensive before companies know what works.

16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.

18:14, Why data governance and data quality need to be treated as day one requirements.

One Line That Stuck

“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”

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Leonid Belkind, co founder and CTO at Torq, joins The Tech Trek to talk about what changes when an engineering organization does more than experiment with AI tools. Torq builds agentic security operations, and Leonid shares how his team is using AI across engineering, product, hiring, customer success, and go to market work.

This conversation gets past the shallow version of “AI makes coding faster.” Leonid makes a clear distinction between coding and software engineering, and explains why the best teams are using AI to shift cognitive load, not remove judgment.

Practical takeaways

• AI does not erase software engineering. It changes where engineering judgment shows up.

• Strong engineers still produce better AI generated work because they know what to ask, what to test, and what tradeoffs matter.

• Hiring processes need to reflect how engineers actually work now, including how they use AI to build, explain, and defend technical decisions.

• Productivity should not only be measured by speed. Leonid talks about throughput, maturity of delivery, and whether teams can produce more without lowering quality.

• AI adoption becomes more powerful when it moves beyond engineering into product, customer success, revenue operations, and talent.

Key moments

00:32

What Torq means by agentic security operations and why different tasks need different AI approaches.

01:49

Why building AI native products with AI native methods creates a useful feedback loop for engineering teams.

05:28

How AI shifts cognitive load so engineers can spend more attention on user experience, architecture, and product value.

10:34

The difference between software engineering and coding, and why that distinction matters more now.

15:13

How Torq has changed technical interviews to evaluate AI assisted engineering instead of pretending AI does not exist.

21:51

How one R&D group measured meaningful delivery gains after adopting AI more deeply.

24:25

Why AI adoption is moving into product, customer success, revenue operations, and talent teams.

One Line That Stuck

“Software engineering as a discipline is not going away. It just changes a phase a bit.”

Practical moves to steal

For hiring, Leonid suggests giving candidates more complex take home work because AI is now part of the real engineering workflow. The evaluation then shifts to the candidate’s ability to explain the architecture, defend decisions, describe how AI was used, and show how they tested and constrained the output.

That is a much better signal than asking someone to work as if the tools do not exist.

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AI coding tools are not just changing how software gets written. They are changing how teams work, how engineers are evaluated, and where bottlenecks show up.

Scott Breitenother, CEO and cofounder of Kilo, joins The Tech Trek to talk about what engineering looks like when developers are managing multiple agents, work continues overnight, and the real constraint is no longer typing code, but judgment, ownership, and process design.

Scott shares how Kilo uses Kilo to build its own product, why AI only creates speed when companies rethink their workflows, and how teams can build trust in agent generated code without creating a new layer of busywork.

Practical Takeaways

• AI does not automatically make teams faster. If approvals, meetings, and handoffs stay the same, the bottlenecks simply move.

• Engineers using coding agents still own the outcome. AI can assist with the work, but accountability for quality does not disappear.

• The strongest teams will find a middle ground between blindly accepting AI output and reviewing every line as if nothing changed.

• Agentic engineering may feel novel now, but Scott believes it will eventually just be called engineering.

• Always on agents are already useful for monitoring, triage, and preparing recommended fixes, even if full autonomy is still selective.

Episode Highlights

00:38 Scott explains what Kilo is building across AI coding, open source infrastructure, and always on agents.

01:16 How Kilo uses its own tools internally, and why developers are shifting from working with one agent to managing many at once.

05:34 Why companies often fail to see AI speed gains when they layer new tools onto old processes.

08:51 The trust curve with coding agents, from early experimentation to accountability, review, and better judgment.

12:39 Why Scott sees agentic coding as a transition phase, not a permanent category.

15:32 Two habits he thinks matter most right now, staying curious and trying a wide range of models and tools.

18:03 What always on agents can already do today, and how that could expand over the next year.

One Line That Stuck

“Bringing in AI does not remove accountability from whoever creates the PR.”

Pro Tips

• Start small with AI assisted workflows, then expand into single agents, multiple agents, and automated review as trust grows.

• Match review depth to risk. A mission critical system deserves more scrutiny than a simple cosmetic change.

• Use automated review to guide human reviewers toward the areas that deserve the most attention.

• Keep experimenting. A tool that fails on Monday may be materially better by Wednesday.

Stay Connected

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Tax is one of the hardest places to earn trust with AI. The work is complex, the stakes are personal, and being mostly right is not good enough.

In this episode of The Tech Trek, David Kang, founder and CEO of Keeper, explains how his team is applying AI to tax workflows without pretending humans disappear from the process. He breaks down why tax is such a strong fit for language models, where AI can reduce manual review, how Keeper decides when a case needs human escalation, and why the best products may feel less like autonomous agents and more like systems that make experts sharper.

Key Takeaways

• AI is most valuable when it removes repetitive work while preserving human judgment where risk is highest.

• High trust products need clear escalation logic, especially when edge cases drive most of the anxiety.

• Tax is a strong fit for AI because much of the work involves language, rules, validation, and workflow routing.

• The smartest AI adoption often starts with bounded operational tasks before moving into more domain specific decisions.

• Consumer trust in AI can change quickly, but messaging still matters when the product sits inside sensitive workflows.

Highlights

00:34 Where Keeper fits for people who have outgrown DIY tax software but do not need a traditional personal accountant.

02:27 Why tax may be one of the more practical use cases for AI, even in a high stakes environment.

07:15 The accounting talent shortage, what automation may replace, and how roles could shift.

10:55 How Keeper uses AI before professional review to flag possible issues and optimization opportunities.

13:51 Why the company moved from keeping AI in the background to talking about it more directly.

17:58 How Keeper separates the routine parts of a tax return from the parts that need expert attention.

21:05 The path from simple customer support automation to more advanced tax focused AI workflows.

One Line That Stuck

“Across tens of thousands of returns and clients, you can kind of get to the point where you err on the side of safety.”

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What happens after you build a public company, spend nearly three decades at the helm, and then find yourself starting over?

Rob Locascio, CEO and founder of Uare.ai, joins The Tech Trek to talk about that exact journey. Rob previously founded LivePerson, helped create web chat for customer service, took the company public, and later scaled it into a major conversational AI business. Now he is back in founder mode, building a new company around individual AI, personal knowledge, and human control over data.

This conversation gets into what it takes to return to zero, why strong ideas need more than belief, how Uare.ai evolved from a personal loss into a broader AI platform, and why Rob sees the current AI moment as bigger and more complex than the dot com era.

Practical Takeaways

• Ideas are not the asset. The ability to turn them into something people understand, join, and buy is what matters.

• Starting over after success requires shedding the habits of scale and getting back into a true startup mindset.

• The first version of a company may only be an entry point. The deeper opportunity often reveals itself through real users.

• Rob believes the future of AI should include individual systems built from a person’s own knowledge, voice, and data, not only large aggregated models.

• The current AI wave has stronger infrastructure than the dot com era, but also more pressure from incumbents and government involvement.

Timestamped Highlights

00:33 Rob explains Uare.ai and its approach to building AI around individual human knowledge.

01:17 The LivePerson story, from inventing web chat to building a large conversational AI company.

03:13 What it felt like to leave the company he spent 28 years building and become a founder again.

06:04 The personal and family tradeoffs of starting another company later in life.

09:06 Why Rob compares building a company to writing a song, and what it means to manifest an idea.

15:52 How the original idea for Uare.ai came from wanting to preserve his father’s voice and memory.

24:00 Rob compares the dot com boom with the current AI cycle, including where he sees real differences.

One Line That Stuck

“They may be able to take your company, but they can’t take your ideas and they can’t take you.”

Practical Founder Advice

• Find the smallest real entry point for the idea and get moving.

• Do not let criticism kill something before the market has a chance to respond.

• Pay close attention to who shows up early. The wrong people can distort a young company quickly.

• Expect the company to evolve. Staying loyal to the original insight does not mean staying frozen in the original product.

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Snigdha Kumar, CEO and co founder at Bricco, joins The Tech Trek to talk about a part of fintech most people never see, state by state licensing.

For any financial company trying to launch in the United States, licensing can be slow, expensive, and operationally painful. Snigdha explains why that barrier limits experimentation, how Bricco is trying to automate the process, and why better compliance infrastructure could help more useful financial products reach the market.

Practical takeaways

• Financial innovation is not only a product problem. Licensing, compliance, reporting, audits, and exams can shape what gets built before a product ever reaches customers.

• Lowering the cost of licensing does not remove regulation. It makes the process more efficient while keeping important protections in place.

• The biggest barrier for fintech founders is often not knowing what path is available. Education and clearer process design can keep teams from avoiding licensing or choosing expensive workarounds.

• Better financial products still need better distribution and awareness. Easy access is not the same as helping people find the right product for their actual financial life.

• Responsible financial behavior may need better product design, better incentives, and a stronger cultural signal, not just more advice.

Timestamped highlights

00:43, Snigdha explains how Bricco is automating state by state regulatory compliance for financial licensing.

02:15, How her career has focused on reducing barriers to financial services across Asia, Africa, and the United States.

05:05, The reverse culture shock of finding major access gaps inside the US financial system.

06:08, Why licensing costs can run into the millions and shrink the number of fintech experiments.

09:58, Why reducing the barrier matters, but eliminating it completely would create real risk.

12:21, The difference between making financial products easy and making sure people are using the right product.

16:05, Why spending has a social identity, but saving and responsible investing often do not.

21:10, How Bricco uses education and content to help founders treat licensing as a strength instead of a blocker.

One Line That Stuck

“Think about licensing as a strength, think about it as a way to own your destiny.”

Practical Takeaways

For fintech founders and operators, the message is simple. Do not treat licensing as a late stage legal detail. It can affect product timelines, market access, capital needs, and the type of company you are able to build.

For technical and product leaders, this is a reminder that infrastructure is not always code. Sometimes the biggest product constraint is the operating system around the business.

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Adam Kirk, CTO and cofounder of Jump, joins The Tech Trek to talk about what it really takes to build AI native products for people who do not want to think like technologists.

Jump serves financial advisors, a market where ease of use, trust, workflow fit, and domain context matter as much as the model itself. Adam shares how his team validates product ideas, uses coding agents across engineering, and is rethinking how technical teams build, review, and hire in the AI era.

What You’ll Take Away

• AI native products still win or lose on adoption. If the user feels like they are programming, the product is already too complicated.

• The engineering bottleneck is moving. AI can generate code faster, but teams still need humans to review, validate, and understand the tradeoffs.

• Product teams can now get closer to the build. PMs using AI to prototype create sharper product definition, even when engineers still rebuild the final version properly.

• Technical debt is not disappearing. Code may be cheaper to write, but data models, migrations, architecture, and judgment still carry real risk.

• Engineering interviews are breaking. If engineers use AI every day, hiring teams need better ways to assess ownership, judgment, and technical taste.

Timestamped Highlights

00:38

Adam explains how Jump helps financial advisors turn client meetings into notes, CRM updates, and advisor specific workflows

02:20

Why less technical users force better product validation, and why a flexible interface can still feel like programming.

07:00

How Jump uses coding agents across the engineering team, and why code review matters more as AI generated code improves.

11:15

Why PMs vibe coding product ideas can help engineers understand what needs to be built.

14:08

Where AI is creating real productivity gains, and where human coordination still slows things down.

18:00

Why some technical debt may get easier to manage, but data modeling and migrations remain hard.

20:51

How AI is forcing engineering leaders to rethink coding interviews, referrals, and what great engineers should be measured on.

One Line That Stuck

“Generating code is really not the bottleneck anymore. It is validating the code, reviewing the code, and sharing the context around to the team.”

Practical Takeaways

• Test product ideas with real users before engineering builds too far.

• Treat AI prototypes as product definition, not production architecture.

• Use coding agents to speed up the work, but do not skip review.

• Assess engineers for judgment, ownership, and decision quality, not just raw syntax.

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Krishna Sai, CTO at SolarWinds, joins The Tech Trek to talk about one of the biggest shifts happening inside IT and engineering teams: AI is moving people from operators to orchestrators.

The conversation goes beyond faster code and automation. Krishna explains why AI is changing how teams think about systems, governance, validation, observability, and the skills technical leaders will need as work moves from manual execution to higher level oversight.

Key Takeaways

• AI is raising the level of abstraction for IT and engineering teams. The work is shifting from operating systems manually to designing systems that can increasingly run, adapt, and respond on their own.

• AI does not automatically reduce workload. In many teams, it changes the type of work by moving effort from execution into validation, judgment, risk management, and governance.

• Code generation is only one part of the delivery system. Without testing, security review, observability, and strong engineering process, faster code can create more problems faster.

• The best AI outcomes depend on strong foundations. Clean data, connected systems, clear ownership, and resilient architecture matter more as AI becomes part of core workflows.

• Technical professionals will need stronger systems thinking, business context, adaptability, and domain understanding as AI changes the shape of day to day work.

Timestamped Highlights

00:00

Krishna Sai joins the show and sets the stage for a conversation about AI, IT responsibility, skill gaps, and the latest SolarWinds IT Trends Report.

02:14

Why IT is moving from operator to orchestrator, and what that means for teams that used to spend most of their time responding to tickets and manually managing systems.

04:54

Krishna explains why AI feels different from prior technology shifts. This is not just infrastructure change. It touches individual workflows, jobs, and decision making.

08:56

The messy middle of AI adoption. Teams are getting faster at some tasks, but the workload has not disappeared. It has moved into validation, review, and oversight.

14:46

How AI may force teams to rethink the software delivery cycle, sprint structure, feedback loops, and the speed at which customer issues can be resolved

24:27

Krishna shares how principles from distributed systems, including loose coupling and high cohesion, can help leaders build AI systems that can change without breaking everything around them.

Standout Moment

“AI is a multiplier. It does not magically fix all your problems. It multiplies your current state.”

Pro Tips

• Do not measure AI success only by how much faster a team can generate code or complete a task.

• Look at the full system around the work, including testing, review, security, observability, and ownership.

• Build AI workflows with enough flexibility to swap tools, models, and processes as the technology changes.

• Invest in systems thinking and domain knowledge. Those skills become more valuable as execution becomes easier to automate.

Call to Action

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Dan Wald, cofounder and chief AI officer at Sciemo, joins The Tech Trek for a sharp conversation about what AI can and cannot do inside real business workflows.

The big question: can AI move beyond quick answers and actually support the messy, context heavy work that still lives in Excel, data teams, and functional expertise?

Dan breaks down why consumer style AI has trained people to expect instant answers, why that creates risk inside companies, and why the next wave of AI products needs more than a chat box. It needs context, transparency, guardrails, and humans who understand the work well enough to challenge the output.

The conversation also gets into AI agents, coding, entry level talent, narrow workflow specific AI, and why replacing judgment is a much harder problem than replacing repetitive tasks.

Key takeaways

• AI tools are only useful when they understand the context behind the question, not just the wording of the prompt.

• Excel remains powerful because users can see the data, change assumptions, and understand the logic. AI products need to earn that same level of trust.

• The best AI workflows are not black boxes. They let users inspect assumptions, challenge outputs, and adjust the answer.

• Agents can speed up work, but they still need human judgment, especially when the task requires strategy, constraints, or domain expertise.

• AI may change entry level work, but companies still need people who can think critically, solve new problems, and understand why the output is right or wrong.

Timestamped highlights

00:40 Dan explains how Sciemo helps consumer brands unify messy data and apply AI to inventory, pricing, assortment, and promotion decisions.

02:30 Why the single prompt experience has changed what people expect from AI, and why that expectation can break down inside the workplace.

04:19 How purpose built AI differs from general AI, especially when the workflow requires context, guardrails, and a clear goal.

07:41 Why Excel is still hard to replace, and what AI systems need to learn from the control and transparency users already expect.

12:57 Dan compares AI agents to unlimited interns, useful for many tasks, but still limited without expert direction.

21:57 The slap chop analogy, and why faster tools do not automatically make someone better at the underlying craft.

31:15 Why predictions about technology and work are so hard to get right, even when productivity clearly improves.

A line that stuck

“Used properly, they’re great. Used poorly, it’s a very new technology. There will be more mistakes than there are winners.”

Practical points worth taking

• Do not treat a confident AI answer as a complete answer.

• Build AI around real workflows, not generic prompts.

• Keep humans close to the assumptions, especially when the decision has business impact.

• Use AI to move faster, but make sure someone still understands the logic behind the work.

Listen next

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