The Tech Trek

What does it take to modernize healthcare infrastructure when uptime is not just an SLA, but a patient outcome?
In this episode, Amir talks with Jeff Sponaugle, CTO of Surescripts, about building and operating mission critical healthcare systems, navigating the move from on premises infrastructure to the cloud, and figuring out where AI can create real value without compromising reliability. It is a sharp conversation on engineering judgment, modernization, workforce evolution, and why technical leadership still needs real technical depth.
What stood out
Cloud migration in healthcare is not just a cost or architecture decision. It is a reliability decision with real downstream impact on patients.
The best reliability strategy is not pretending nothing will ever break. It is designing systems so the customer never feels the break.
In regulated industries, structure can be an advantage. Standardized data and consistent formats make AI more useful, especially in healthcare.
AI can already improve the patient and clinician experience in practical ways, from transcription to summarizing complex records and surfacing relevant context faster.
Technical leaders cannot afford to drift too far from the work. Jeff makes the case that strong CTOs stay close enough to the technology to understand the tradeoffs, guide teams well, and spot what matters next.
Timestamped Highlights
00:00
Jeff Sponaugle joins the show to unpack mission critical technology in healthcare, cloud migration, AI, and workforce upskilling.
01:57
Why Surescripts sits in a critical layer of healthcare, and why reliability matters when prescriptions need to move in real time.
04:02
A simple but powerful view of reliability: things will break, but the customer should not know they broke.
06:47
How to adopt new technology without risky hard cutovers, and why parallel systems matter in high stakes environments.
08:53
Upskilling legacy teams, preserving tribal knowledge, and why continuous learning matters more than any single technical skill.
11:58
How regulation can actually help AI in healthcare by creating more consistency in the data.
17:33
Where AI and agentic systems could create meaningful value in prescribing, diagnostics, and clinical workflows.
20:29
Why AI has changed executive and boardroom conversations in a way cloud migration never did.
A line worth remembering
“The customer should not know that something broke.”
Pro Tips
If you are modernizing a high stakes platform, avoid the big overnight cutover. Run systems in parallel where possible and learn behind the scenes before customers ever feel the change.
If you lead technical teams, do not treat upskilling as a one time event. Give people a path to split time between legacy work and emerging systems so the transition is real and sustainable.
If you are evaluating AI in a regulated environment, start with narrow, useful workflows where context, speed, and summarization matter, then expand from there.
Stay connected
If you enjoyed this episode, follow the show, subscribe wherever you listen, and share it with someone building in healthcare, cloud infrastructure, or AI. You can also connect with Amir on LinkedIn for more conversations at the intersection of technology, leadership, and the future of work.

Farzan Karimi, Deputy CISO at Moderna, joins Amir Bormand for a sharp conversation on one of the most misunderstood areas in cybersecurity, the ethics of offensive security. From red team rules of engagement to nation state deception and the limits of AI in security testing, this episode gets into what happens when the job requires you to think like an attacker without crossing the line.
This is a practical conversation for security leaders, engineers, and operators who want a clearer view into how modern security programs actually work under pressure. Farzan shares hard lessons from his own career, explains why red teaming is really about business risk, and makes the case for storytelling over dashboards when security teams need executive buy in.
Key Takeaways
• Offensive security is not about finding every weakness. It is about simulating what a real attacker would do to reach the business’s worst case scenario.
• The gray area is real. Just because you are authorized to test a system does not mean every possible action is justified.
• Nation state level threats force teams to think differently. Attackers look across the connective tissue of systems, not just isolated tools or apps.
• Good red teaming can make the rest of the business stronger by helping teams see real risk, align on priorities, and justify investment.
• AI can speed up security work, but it still misses too much to replace experienced human operators.
Timestamped Highlights
02:02 What offensive security actually means, and why the best programs are built around business impact, not just technical findings.
03:46 Where the ethical gray area starts, from phishing and social engineering to the personal judgment calls that can end careers.
06:03 A story from Farzan’s Microsoft days that shows how a valid finding can still go too far when judgment slips.
11:06 Why security leaders have to explain to executives that attackers do not care about internal process, approvals, or red tape.
14:46 A nation state honeypot turned the red team into the target, and forced a complete shift in approach.
24:14 AI is changing the workflow, but Farzan explains why current tools still fall short of real red team depth.
A line worth remembering
“Just because you can doesn’t mean you should abuse those permissions.”
Pro Tips
• Tie offensive security work to the business’s real doomsday scenario, not a generic list of vulnerabilities.
• When you find a serious issue, know exactly where the rules of engagement stop, and stop there.
• Use attack stories and patterns to earn trust internally. Raw metrics rarely move people the same way.
• Treat AI as an accelerator, not a replacement for experienced security judgment.
Listen and follow
If this episode gave you a better lens on how modern security teams think, subscribe to The Tech Trek, follow the show, and share this episode with someone building, securing, or scaling technology in the real world.

Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, joins Amir to break down what it actually takes to build agents for the enterprise, not in theory, but in environments where complexity, governance, observability, and real business outcomes matter.
This conversation gets into the part of enterprise AI that most people skip. Not just what agents can do, but what changes when you have to deploy them across regulated systems, measure performance in production, manage model drift, and rethink how product and engineering teams ship software. It is a smart look at where enterprise AI is going, and what technical leaders need to understand before the market catches up.
What stood out
• Enterprise agents are only as strong as their data, context, and deployment model. In large companies, that means dealing with hybrid environments, air gapped systems, privacy controls, and process level context, not just model quality.
• AI is changing more than coding. Adi explains how his team is using AI across the full software development lifecycle, from spec creation and test generation to production event triage and release workflows.
• The release process is shifting from periodic launches to continuous iteration. That puts more pressure on observability, because teams now have to track model behavior, latency, and runtime performance as features roll out.
• Security can no longer sit off to the side. Prompt injection, shared tenant risk, and post production anomaly detection all require security teams to work much closer to AI and product teams.
• Mass adoption is not just a technology problem. The tools are improving fast, but enterprises still need change management, clear use cases, internal operating models, and people who know how to make AI part of daily work.
Timestamped Highlights
00:00 Adi Kuruganti joins the show to unpack what enterprise agent development really looks like today, from deployment models to governance to observability.
02:07 Why enterprise agents are different. Adi explains why context, data control, and environment complexity matter more in large organizations.
04:57 How AI is reshaping the software development lifecycle. From code suggestions to automated tests to incident triage, AI is moving deeper into product delivery.
10:13 The old handoff model is breaking. Product, design, and engineering are starting to work in a much more fluid, AI assisted way.
12:22 What changes in release management when AI writes part of the code and teams ship continuously instead of waiting for big release cycles.
18:17 How enterprises should judge agent performance, from human review and exception handling to evals, runtime benchmarks, and model drift.
27:21 Adi on the real AI adoption curve, job disruption, and why the bigger shift is not replacement, but making AI part of how people actually work every day.
A line worth sitting with
“AI should be a core element of how they work.”
Worth applying
• If you are building with AI, evaluate more than accuracy. Cost, latency, and consistency matter too.
• If you are leading teams, do not treat observability as a nice to have. Runtime visibility is part of the product now.
• If you are thinking about adoption, start with a real business problem and scale from early wins instead of trying to automate everything at once.
Follow the show for more conversations with the builders, operators, and technology leaders shaping how modern companies are actually being built.

What happens when software engineers stop thinking like coders and start thinking like orchestrators?
In this episode, Amir sits down with Scott Gale, CTO and Founder of Fluency, to unpack one of the biggest shifts happening in engineering right now: the move from writing code by hand to directing AI agents with context, judgment, and intent. Scott shares how his team is already using coding agents in production, what that means for hiring and team design, and why the engineers who adapt fastest will be the ones who gain leverage, not lose relevance.
This conversation gets into the real change beneath the AI hype. Not just better tools, but a different shape of engineering work. Less manual syntax, more planning, auditing, collaboration, and system level thinking.
Key Takeaways
• The value of an engineer is shifting away from typing code and toward directing intent clearly
• Teams that give AI better context can get dramatically better output from coding agents
• Engineers do not need to become people managers, but they do need to learn how to manage agent driven work
• Hiring is starting to favor people who can collaborate, learn the product, and work effectively with AI
• Faster software delivery does not mean less to build, it often means companies can finally tackle more of the backlog
Timestamped Highlights
00:01 Scott Gale, CTO and Founder of Fluency, joins Amir to break down the shift from builder to orchestrator in modern engineering
02:36 How Fluency introduced coding agents with a three part approach: safe experimentation, mindset shift, and stronger context
04:35 Is this just the next step in software engineering, or does AI fundamentally change the role?
08:16 Why some engineers resist AI tools, and what helps people move from skepticism to real adoption
11:26 How technical interviews are changing as AI becomes part of everyday engineering work
16:59 Scott on whether companies will actually need fewer engineers, and why the demand for meaningful work is not going away
21:09 The practical lesson teams miss: better structured systems and better context make coding agents far more effective
One line worth remembering
“It’s not about losing your craft. It’s about managing a workforce of junior agents.”
Practical edge
Scott shares a useful operating principle for teams already experimenting with AI in engineering: if you want better output, do not start with prompts alone. Start with structure. The more clearly a system is organized, and the more context an agent can access, the more useful and reliable the result becomes.
That applies to hiring too. Technical skill still matters, but the engineers who stand out now are the ones who can collaborate across product and engineering, understand the business context, and make good decisions with AI in the loop.
Call to Action
If you are thinking through what AI means for engineering careers, team design, or product velocity, follow the show and share this episode with someone building in this new environment. For more conversations with founders and operators shaping where tech is headed, connect with Amir on LinkedIn.

What happens when software engineers stop thinking like coders and start thinking like orchestrators?
In this episode, Amir sits down with Scott Gale, CTO and Founder of Fluency, to unpack one of the biggest shifts happening in engineering right now: the move from writing code by hand to directing AI agents with context, judgment, and intent. Scott shares how his team is already using coding agents in production, what that means for hiring and team design, and why the engineers who adapt fastest will be the ones who gain leverage, not lose relevance.
This conversation gets into the real change beneath the AI hype. Not just better tools, but a different shape of engineering work. Less manual syntax, more planning, auditing, collaboration, and system level thinking.
Key Takeaways
• The value of an engineer is shifting away from typing code and toward directing intent clearly
• Teams that give AI better context can get dramatically better output from coding agents
• Engineers do not need to become people managers, but they do need to learn how to manage agent driven work
• Hiring is starting to favor people who can collaborate, learn the product, and work effectively with AI
• Faster software delivery does not mean less to build, it often means companies can finally tackle more of the backlog
Timestamped Highlights
00:01 Scott Gale, CTO and Founder of Fluency, joins Amir to break down the shift from builder to orchestrator in modern engineering
02:36 How Fluency introduced coding agents with a three part approach: safe experimentation, mindset shift, and stronger context
04:35 Is this just the next step in software engineering, or does AI fundamentally change the role?
08:16 Why some engineers resist AI tools, and what helps people move from skepticism to real adoption
11:26 How technical interviews are changing as AI becomes part of everyday engineering work
16:59 Scott on whether companies will actually need fewer engineers, and why the demand for meaningful work is not going away
21:09 The practical lesson teams miss: better structured systems and better context make coding agents far more effective
One line worth remembering
“It’s not about losing your craft. It’s about managing a workforce of junior agents.”
Practical edge
Scott shares a useful operating principle for teams already experimenting with AI in engineering: if you want better output, do not start with prompts alone. Start with structure. The more clearly a system is organized, and the more context an agent can access, the more useful and reliable the result becomes.
That applies to hiring too. Technical skill still matters, but the engineers who stand out now are the ones who can collaborate across product and engineering, understand the business context, and make good decisions with AI in the loop.
Call to Action
If you are thinking through what AI means for engineering careers, team design, or product velocity, follow the show and share this episode with someone building in this new environment. For more conversations with founders and operators shaping where tech is headed, connect with Amir on LinkedIn.