The Tech Trek

What actually makes a company AI native? Is it using AI across every department, building more agents, or changing how the organization works?

Eswar Vandenapu, Cofounder and CTO of NovaWorks AI, shares what his team is learning as it moves from basic AI adoption toward a shared context model that shapes how decisions are made.

The discussion explores why giving everyone access to AI tools is only the beginning. Eswar explains how NovaWorks is using shared context to improve collaboration, why an agent that understands its environment can be more useful than dozens of specialized agents, and how the boundaries between product and engineering are changing.

We also discuss the skills engineering teams need as AI handles more coding work, including systems thinking, domain expertise, and the ability to ask better questions.

What You'll Learn

Why AI adoption alone does not make a company AI native.
How shared context changes collaboration and decision making.
Why agent quality matters more than the number of agents.
How AI is changing engineering roles, hiring, and technical skills.

Episode Highlights

00:40 How NovaWorks is rethinking HR and workforce management
03:37 The journey from AI awareness to shared organizational context
07:54 Why building more agents is not necessarily better
11:07 How AI is changing the relationship between product and engineering
14:41 Why systems thinking and domain knowledge matter more in engineering
18:57 Learning to question AI rather than simply give it instructions

One Line That Stuck

"Quantity of the agents doesn't matter."

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A few seconds of audio can now be enough to clone a voice. But the bigger challenge is knowing who, or what, is on the other end of an interaction. That question matters when someone calls a bank, joins a job interview, or sends an AI agent to act on their behalf.

Vijay Balasubramaniyan, CEO and Co Founder of Pindrop, joins The Tech Trek to discuss how identity and security are changing as deepfakes and AI agents become easier to create. He shares why Pindrop's apparent overnight success took 15 years, how fraudsters are automating account attacks, and why companies need to think beyond a single identity check.

The conversation also gets into hiring. Vijay describes fake applicants, different people appearing across interview rounds, and the case for verifying identity throughout the hiring process and after onboarding. His larger point is that stronger, ongoing identity signals could also make security less intrusive for legitimate users.

Key takeaways
- Deepfake detection needs to keep pace with tools that can clone voices from seconds of audio.
- Companies must distinguish legitimate AI agents from agents attempting fraud or unauthorized access.
- Recruiting teams are increasingly part of an organization's security defenses.
- Continuous identity can protect interviews, accounts, and employee access without adding constant friction.

Episode highlights
- 01:38 Why Pindrop's success took years to build
- 03:44 Voice cloning goes from 20 hours to three seconds
- 06:44 How fraudsters use AI agents to make calls at scale
- 11:51 Who takes responsibility when an AI agent makes a bad decision?
- 16:28 Fake candidates and the risks hiding in hiring funnels
- 21:34 How continuous identity could make security feel invisible

A moment worth pulling out: “They are now part of security. They're the front end.” Vijay on recruiting teams.

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The goal is not to do less. It is to give agents the flexibility to handle more of the work customers actually need.

Henry Hopcroft, Chief Product Officer at Gradial, joins The Tech Trek to discuss how building AI products is changing traditional approaches to product development, engineering, and customer delivery.

Henry explains why his team has moved away from building individual agent features toward a smaller set of core capabilities. He also shares how Gradial tests agents against difficult enterprise environments, uses forward deployed engineers to learn directly from customers, and approaches product planning when AI capabilities are improving so quickly.

What You'll Learn

Why adding more features can limit an AI agent's flexibility.
How real customer failures shape Gradial's agent evaluations.
Why forward deployed engineers play a central role in product development.
How coding agents are bringing product and engineering roles closer together.

Episode Highlights

03:59 Why Gradial started deleting agent features.
07:31 Why traditional software testing falls short for enterprise AI agents.
08:35 Building a nightmare marketing stack to test agents.
12:03 How forward deployed engineers improve the core product.
15:56 Why great engineers increasingly need product judgment.
21:55 Why long term roadmaps are becoming harder for AI companies.

A Moment Worth Pulling Out

"Features can be something of an anti-pattern, I think, for agentic software."

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Jayeeta Putatunda, AI Engineering Lead at Turing, joins The Tech Trek to explore what happens after AI generates the first draft. She explains how excessive code can create maintenance challenges, why growing pull request volumes put pressure on code reviews, and how teams can build AI review agents around their own business rules.

The conversation also explores how AI is changing product development. Teams can test ideas, create prototypes, and gather customer feedback faster. But taking those ideas into production still requires testing, security, and engineering discipline.

Jayeeta also discusses LLM observability, the growing importance of domain expertise, and why sharing lessons across engineering teams matters as AI tools evolve.

Key Takeaways

More AI generated code can create additional review and maintenance work.
Code review agents need business context, not just generic rules.
Faster prototypes still require careful testing before production.
Domain expertise and human judgment remain important in AI systems.

Episode Highlights

00:34 Why AI generates more code and documentation than teams may need.
03:46 How growing PR volumes create new review challenges.
06:44 Measuring productivity beyond the number of features delivered.
12:10 Managing stakeholder expectations when prototypes become easier to build.
14:34 Why LLM observability matters in regulated industries.
18:44 The role of domain experts in building more specialized AI models.

One Line That Stuck

"Who's reviewing those 10 PRs?"

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AI teams can build solutions faster than many organizations can make decisions. That gap is changing how engineers work with the business and what companies need from technical leaders.

Ash Pembroke, Managing Director of AI Strategy at Accordion, joins The Tech Trek to discuss the growing role of forward deployed engineers. She explains why technical depth alone is not enough and how successful teams combine engineering, product thinking, and business knowledge.

The conversation covers leading lean engineering teams, finding the right mix of skills, working alongside business stakeholders, and navigating the tension between rapid AI development and slower organizational adoption.

Key Takeaways

Strong forward deployed engineers combine technical expertise with curiosity and business understanding.
Lean teams need complementary skills rather than one engineer who can do everything.
AI agents can extend existing SaaS systems, but replacing those systems requires careful decisions.
Faster development does not automatically mean faster adoption or better business decisions.

Episode Highlights

01:30 What makes a strong forward deployed engineer?
03:18 Leading small engineering teams with overlapping responsibilities
08:25 How AI agents are changing the role of traditional SaaS
16:50 Moving from software engineering or product into forward deployed roles
25:13 Why business decisions can become the bottleneck to AI adoption
33:48 Giving business teams the ability to automate their own workflows safely

A Moment Worth Pulling Out

"Nobody knows the work better than the person doing the work."

Ash makes the case for giving employees the tools, access, and guardrails to improve their own workflows.

Follow The Tech Trek for more conversations on AI, engineering, and technical leadership.