The Tech Trek

Voice AI is moving from simple call routing into work that used to require trained human agents. The harder question is what happens when those conversations involve lending, collections, servicing, compliance, and real customer risk.

In this episode of The Tech Trek, Amir Bormand speaks with Joshua March, founder and CEO of Veritus, about building AI voice agents for regulated financial services. Joshua shares why consumer lending is a demanding test case for voice AI, what makes regulated conversations different, and why the next version of the contact center may be built with much smaller teams overseeing AI systems.

Practical takeaways

AI voice agents only matter if they can actually resolve the issue. Joshua argues that users have been trained to distrust automated phone systems because most IVRs block progress instead of helping.

Regulated communication is not just about what the agent says. It also includes who can be contacted, when they can be contacted, call frequency, TCPA rules, QA, and post call compliance.

Complex voice agents require more than a prompt. Joshua talks about context engineering, state management, specialized background agents, compliance monitoring, KYC workflows, latency, and turn detection.

AI changes startup execution. Small teams with experienced people can build and ship much more than before, but that also raises the pressure to move faster.

Venture backed AI companies face a bigger bar. Joshua makes the case that higher seed valuations and larger funds increase the need for very large outcomes.

Timestamped highlights
00:00, Why Veritus is focused on AI communications for financial services and voice agents in consumer lending
02:10, Joshua’s path from Facebook apps to social customer service, messaging, bots, and now voice AI
05:05, Why AI voice agents may replace a large share of traditional call center work
07:00, Why customers have learned to fight IVRs and what changes when AI can actually solve the problem
10:00, The compliance layers around regulated voice conversations in lending, servicing, origination, and collections
14:00, Why production voice agents need context engineering, state machines, background agents, observability, and monitoring
20:30, How AI has changed startup hiring, management, productivity, and the role of experienced individual contributors

One Line That Stuck

“This isn’t a crappy IVR that’s just trying to get in my way, this is an intelligent system that can actually take actions and actually resolve my issue.”

Practical lens for technical teams

If you are building AI into customer operations, the hard part is not only getting the model to speak well. The harder work is making sure it knows what it can do, when it can act, what rules apply, how it is monitored, and when humans need to step in.

That matters even more in regulated industries, where the conversation itself is only one part of the system.

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Joanne Chen, VP of Data and AI at SimplePractice, joins The Tech Trek to talk about what it takes to build AI data products in a regulated, sensitive domain where privacy, consistency, monitoring, and customer trust have to be designed from the start.

This conversation gets into why AI product development feels different from traditional software, how teams should think about quality control, and why not every valuable AI solution needs to be GenAI.

Practical Takeaways

• AI products need defense in depth, especially in healthcare, where privacy, confidentiality, and security cannot depend on one layer of protection.
• The core product questions still matter. What customer pain does this solve, who benefits, and what does it take to ship responsibly?
• AI changes the development life cycle because outputs are not always deterministic and quality can degrade after launch.
• Teams need monitoring, validation, and a plan for edge cases before putting AI features in front of customers.
• AI literacy is becoming part of every role involved in building, marketing, supporting, and operating software products.

Timestamped Highlights

00:00 Joanne Chen on AI data products, deterministic outputs, and safely shipping AI features
01:20 What SimplePractice does for mental health practitioners and group practices
02:30 Why healthcare AI needs multiple layers of risk protection
05:00 What makes an AI data product different from a traditional data product
08:15 Why stakeholder expectations around AI have widened so much
10:40 How AI changes the work across engineering, CS, marketing, and support
13:10 Where AI can help reduce tedious administrative work in healthcare
16:45 Why leaders need to keep their hands dirty with new AI tools

One Line That Stuck

“Keeping hands dirty is important.”

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Kevin Haggard, Vice President of Engineering at Barracuda, joins The Tech Trek to talk about how AI is showing up across cybersecurity products, engineering workflows, team adoption, and software delivery culture. He shares how Barracuda is approaching AI with guardrails, why adoption varies across teams, and what happened when the company ran protected AI dev days for the engineering organization.

What to take from this episode
* AI adoption inside engineering teams will not be even. Some teams are already orchestrating agents from requirements to deployment, while others are still figuring out where AI fits into their day to day work.
* Guardrails matter more in security sensitive environments. Barracuda uses an AI gateway and control plane so teams can experiment without leaking data or letting agents take uncontrolled actions.
* Protected time changes behavior. Barracuda’s AI dev days gave teams three days with no meetings so they could work with the tools inside real projects instead of treating AI as a side experiment.
* The coding bottleneck may move. AI can create more code faster, but QA, testing, release safety, problem definition, and rollback mechanisms become even more visible.
* The engineer’s role is shifting from operator to orchestrator. Kevin argues that system design, review, context, and crisp instruction will become more valuable as agents take on more execution.

Key moments

00:28, What Barracuda does and how AI pairs with people in cybersecurity
03:35, Why AI adoption varies across engineering teams inside a larger organization
05:06, The need for AI guardrails, gateways, and control planes
06:20, How Barracuda ran AI dev days across the organization
08:42, The light bulb moments from product, design, and engineering teams
13:07, Why AI velocity makes existing delivery bottlenecks harder to ignore
17:00, How engineers may move from writing code to orchestrating agents and reviewing systems

One line that stuck:

“Their role is going to elevate more.”

Practical moves from Kevin’s experience
* Bring trusted partners in for training, but follow that with hands on sessions.
* Give teams protected time to use AI inside actual work, not just demos.
* Share what the advanced teams are learning so adoption does not stay isolated.
* Keep people in the loop, especially when agents are generating code, tests, or workflow changes.
* Work backward from release bottlenecks, not just coding speed.

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#agenticai #ai #techleadership #engineeringleadership #engineering

AI adoption is no longer just a policy conversation. For many organizations, the bigger question is how to move faster without creating avoidable risk.
In this episode of The Tech Trek, Amir Bormand sits down with Aimee Cardwell, CIO and CISO in residence at Transcend, to talk about responsible AI deployment, the tension between speed and control, and how leaders should think about security, compliance, productivity, and customer experience as AI moves through the enterprise.
Aimee brings a rare view across the CIO, CISO, and board lens. The conversation gets into why blocking AI often backfires, how prompt redaction can help teams move faster safely, where companies should draw the line on risk, and why some teams may need to rethink old assumptions about tech debt, code ownership, and modernization.
Practical Takeaways
• Responsible AI depends on the lens. Security, compliance, business, board, and technology teams may all define it differently.
• Blocking employee AI usage can create worse outcomes. People may use shadow tools anyway, or teams may fall behind in productivity.
• Prompt redaction and enterprise agreements can give teams room to experiment while reducing exposure of sensitive data.
• Moving fast is not the same as releasing half finished customer experiences. Bad AI tools can train customers to distrust the entire interaction.
• AI may change how teams think about tech debt, refactoring, and whether some legacy systems should be rebuilt instead of patched forever.
Timestamped Highlights
00:00 Responsible AI deployment and why the definition changes by role
02:35 Aimee explains the CIO, CISO, and board perspectives on AI adoption
05:14 Why companies that block AI may create shadow usage and slower teams
06:52 Prompt redaction as a practical way to let employees experiment safely
10:40 How AI risk changes when the data exposure model is different from traditional insider theft
15:10 Why releasing poor AI customer experiences can damage trust
21:50 Using shared enterprise prompts to raise the quality of AI output across engineering teams
26:20 How AI could change the way teams approach security debt and code modernization
One Line That Stuck
“The conversation has flipped, and it is really how can I get the company to go faster.”
Pro Tips
• Start by identifying what truly makes your business defensible. Not every asset carries the same risk.
• Give employees safe paths to use AI instead of pretending they will not use it.
• Build shared prompts with engineering standards, approved tools, and company context so teams do not start from scratch every time.
• Ask whether old assumptions still hold. Some decisions made sense when changes were expensive, slow, or risky. AI may change that equation.
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#ai #agentic #techleadership #engineeringleadership

AI agents are easy to demo. They are much harder to trust, maintain, govern, and put into production.

In this episode of The Tech Trek, Amir Bormand talks with Lucas Thelosen, CEO and cofounder at Gravity, about the agent economy, AI analytics, and what changes when analysts move from doing every task themselves to managing AI systems that create more bandwidth.

Lucas shares why Gravity built Orion, an AI analyst, after years working in analytics, product, and data teams at companies like Looker and Google. The conversation gets into the messy middle of AI adoption, why so many agent projects struggle to make it into production, and how context may become one of the most valuable assets a company owns.

Practical Takeaways

• Agent prototypes are easy. Production agents require support, maintenance, accuracy checks, and clear ownership.
• Not every company should build every agent internally. If the capability is not core to what you sell, buying may be the faster path.
• Context matters because it lets humans critique AI output with business judgment, not just technical review.
• Analysts may shift toward data architecture, governed data models, and internal product management for analytics.
• AI does not remove human responsibility. It raises the bar for review, delegation, and decision making.

Timestamped Highlights

00:40, What Gravity is building with Orion, an AI analyst designed around the work analytics teams already know well.
02:28, Why mature companies still miss major insights, even when they already have data teams.
03:43, The agent economy reality check, easy prototypes, hard production, and the gap between demo and durable system.

06:28, Why companies still build agents internally, even when many projects never reach production.

09:48, The case for experimenting now instead of waiting for the AI stack to settle.

11:40, How AI shifts people from doing the work to managing the work.

18:50, What the future analyst role may look like as AI takes on more of the execution layer.

One Line That Stuck

"Where previously you were the person doing the work, now you're the manager."

Subscribe to The Tech Trek for more conversations on how technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.