The Tech Trek

AI agents are moving beyond helping with individual tasks. The bigger question is how much of a company they can actually run.

Ben Cera, founder of Polsia, joins The Tech Trek to discuss what happens when AI handles engineering, support, marketing, research, and other parts of company execution.

Ben explains how Polsia uses specialized agents that can take direction from a founder or decide what to work on autonomously. He also shares how he uses similar systems inside his own company, which he says has more than 10,000 paying customers and is approaching a $10 million run rate without a traditional full time team.

The conversation gets into where humans still matter, why AI mistakes may be acceptable, and how faster execution changes the way founders test ideas.

Key Takeaways

• AI agents can move from completing tasks to coordinating entire business functions.
• Faster execution gives founders quicker feedback on what works and what does not.
• Humans still matter most for judgment, direction, and authentic storytelling.
• Autonomy requires accepting some mistakes instead of demanding perfect AI output.

Highlights

02:43 What changes when AI becomes part of how a founder operates
04:03 Turning customer support into a system that can also fix problems
06:19 Running a company without a traditional full time team
13:47 Why founder judgment still matters when AI gives the options
18:49 How specialized agents coordinate engineering, marketing, and outreach
22:10 What happens when autonomous AI makes the wrong decision

One Line That Stuck

“You have to trust your gut and you have to be willing to make mistakes.”

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AI coding agents can help engineering teams ship more code. But the bigger change may be what engineers spend their time doing.

Viren Baraiya, Co-Founder and CTO of Orkes, joins The Tech Trek to discuss how AI is changing workflow orchestration, engineering productivity, project delivery, and hiring. As agents take on more implementation work, engineers are spending more time on design, architecture, review, and verification.

Viren shares how his team measures the return on AI through product velocity, stability, and the ability to build things that previously required more time or outside resources. He also explains how Orkes manages model costs by using stronger models for difficult reasoning and smaller models for implementation.

Key Takeaways

• Coding agents increase output, but they also increase the need for verification.
• Engineers are shifting from pure implementation toward design, review, and orchestration.
• Repeated AI tasks can become reusable workflows that reduce ongoing token usage.
• Hiring should test how engineers actually work with agents, not just manual coding.

Highlights

01:21 Why agents are workflows and where orchestration fits into AI systems
05:19 How AI changed feature velocity, testing, and customer engineering at Orkes
07:44 Measuring AI ROI through velocity, stability, and new product capabilities
09:19 Why engineers increasingly look more like tech leads
12:56 Turning repeated AI requests into reusable workflows to reduce token usage
19:34 Why Orkes changed engineering interviews to include agentic coding

One Line That Stuck

“That has become a more important skill than actually writing the code now.”

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AI is doing more than helping engineers write code faster. It is starting to change who can participate in software development, how teams divide work, and where technical talent creates the most value.

Shaosu Liu, Co Founder and CTO at Loop, explains how his team is using AI agents across the software development lifecycle while also enabling highly technical people outside traditional software engineering roles to build customer specific workflows. The result is a different model for scaling technical work, one that matters for founders and technical leaders thinking about team design in the age of AI.

Loop is also using forward deployed engineers as core product engineers who can work directly with customers, understand difficult edge cases, and turn those requirements into product improvements.

Takeaways

• AI agents can now support much more than coding, including specifications, testing, deployment, validation, rollout, and production management.
• Giving technical people better AI tools can expand who is capable of contributing to software development.
• The final few percent of a customer workflow may consume most of the manual effort. AI makes that customization more practical.
• Forward deployed engineers need both technical ability and the judgment, communication skills, and confidence to work directly with customers.

Key Moments

04:37 How Loop uses AI across the software development lifecycle
07:05 Why people outside traditional engineering roles are writing significant amounts of code
09:03 Why automating the final few percent of a workflow can remove most of the remaining manual work
11:01 Why forward deployed engineers matter when customer requirements get complicated
16:37 Why forward deployed engineering is often a path to another role rather than a long term career
19:50 How Loop evaluates technical ability and customer facing skills when hiring

One Line That Stuck

“That last 5% automation ends up saving 100% of time.”

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AI can make teams faster, but it can also expose every weakness in the data underneath it.

Elizabeth Stanford, VP of Data at PandaDoc, joins The Tech Trek to talk about what it takes to prepare a growing company to actually execute on AI. That means more than giving engineers access to Claude or Cursor. It means getting the data foundation, team skills, stakeholder expectations, and ownership model right.

Elizabeth explains how PandaDoc is preparing its data organization for AI while keeping a small team from becoming the company’s quality control department. She also shares how AI is changing what she looks for when hiring data professionals, and why expertise, problem framing, and judgment may become more valuable as coding gets easier.

What you’ll take away

• AI readiness starts with reliable data, shared definitions, and systems that can provide consistent context.
• Giving stakeholders easier access to data creates a new problem when the data team becomes responsible for checking everyone else’s AI generated work.
• Technical execution is becoming easier, which puts more value on knowing what questions to ask and whether an answer is actually correct.
• Hiring standards are changing. Candidates need to show how they think with AI, not simply that they can use it.

Best Line

“It’s not whether you know today’s technology, it’s whether you can figure out tomorrow’s technology.”

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AI is not just changing software. It may also change the economics of the services businesses built around it.

Anirudh Sriram, CTO at Tessera Labs, joins The Tech Trek to explain how his company is using AI to take on enterprise transformation work traditionally handled by large systems integrators. Tessera focuses on migrations, ERP upgrades, code, data, and planning, but the bigger story is how a startup can compete by replacing large teams and long projects with automation, smaller teams, and a focus on outcomes.

The conversation also gets into a harder problem. AI can produce work much faster than people can verify it. In one example, Tessera completed code migration work in three days, but functional testing still required roughly two months. That gap between production and verification may become one of the biggest constraints on enterprise AI.

What Stood Out

• AI creates an opening for startups to compete in markets where incumbents have historically won through scale and headcount.
• Selling outcomes instead of large project teams can change both pricing and customer expectations.
• Enterprise migrations can be a wedge into a much larger opportunity because they require understanding a customer's systems, data, code, and business processes.
• Faster AI output does not remove the need for human review. In some cases, verification becomes the new bottleneck.

Key Moments

02:12 Where AI can take work out of the enterprise migration process
04:39 How transformation projects can stretch years beyond their original plan
08:27 Why AI may change the economics of services businesses
13:42 Why verifying AI output is becoming a major constraint
22:17 How Tessera approaches security, governance, and enterprise data
24:56 Using migration as the entry point into broader enterprise automation

One Line That Stuck

“We sell the outcome and not the process.”

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