The Tech Trek

AI coding agents can produce software faster, but they do not replace the judgment needed to understand the system.

Shaun Patterson, CTO at Titan, joins The Tech Trek to discuss how agentic coding is changing problem solving, development workflows, project management, and technical hiring.

Shaun explains why engineers still need a strong mental model of the systems they are building. AI can generate code, reproduce bugs, research implementation options, and automate repeated debugging work. But it can also keep working on the wrong problem long after a human debugger would have found the answer.

The conversation also gets into a bigger shift in software delivery. If agents can work across much larger pieces of a project, engineering teams may move from managing work at the story level to working at the epic level.

Key Takeaways

• AI speeds up implementation, but engineering judgment still matters.
• Repeated debugging work can become reusable agent skills.
• Faster implementation lowers the cost of testing different technical approaches.
• Hiring increasingly needs to measure how engineers work with AI.

Highlights

02:08 Why AI can abstract work, but not engineering wisdom
06:04 Turning repeated debugging sessions into reusable agent skills
09:47 Why faster development may change traditional project management
12:42 Moving engineering work from stories to epics
16:19 Where agentic coding still creates problems
19:29 How Titan evaluates engineers who use AI

One Line That Stuck

“It abstracts your thinking, but it doesn’t abstract your wisdom.”

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AI agents create a different security problem from traditional software. They can operate at software speed and scale while behaving in ways that are much less predictable.

Ev Kontsevoy, CEO and cofounder of Teleport, joins The Tech Trek to discuss what happens when companies deploy agents into security systems designed around humans, applications, and relatively static organizational structures.

The conversation gets into authentication, impersonation, infrastructure identity, access control, and a harder question: what actually defines the identity of an AI agent when its model, memory, skills, and capabilities can change?

Ev also explains why the combination of speed, scale, and unpredictable behavior changes the risk of mistakes. Later, he explores the tension between agents being useful because they can do new things and security systems that often depend on predictable behavior.

Key takeaways

• Agent identity gets harder when memory, models, and capabilities can change.
• Traditional access controls often reflect static organizational structures.
• Agents combine software speed with behavior that can be difficult to predict.
• Useful agent behavior can conflict with security systems built around anomaly detection.

Highlights

00:41 What Teleport does and why infrastructure identity matters
08:37 Why companies may already be behind on agent security
13:47 Why an electronic account is not the same as identity
15:11 What actually defines the identity of an AI agent?
22:49 Why agent speed and unpredictability change the risk equation
29:04 The conflict between useful agent behavior and anomaly detection

One Line That Stuck

“Agents are just as unpredictable as humans, but they are way, way, way faster.”

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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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