Drag & Drop

AI coding tools are generating more code than ever, but enterprise engineering teams are not necessarily delivering software faster.

In this episode of Drag & Drop, Feroz Khan sits down with Martin Reynolds, Field CTO at Harness, to explore the AI velocity paradox and what it means for software delivery, DevOps, AgentOps, platform engineering, and AI agents in production.

Martin explains why building an AI agent can be relatively easy, but deploying one safely requires a completely different level of governance, identity, testing, observability and accountability.

The conversation explores why code is no longer the main bottleneck, why traditional delivery processes are struggling to keep pace with AI-generated output and what engineering organizations need to operate increasingly autonomous systems safely at scale.

In this episode, you’ll learn:

➤ Why faster code generation isn’t producing faster software delivery
➤ What prevents AI agents from reaching production
➤ Why choosing the right AI model is only a small part of the challenge
➤ Why governance and guardrails cannot be added afterwards
➤ How AgentOps extends DevOps from code changes to autonomous decisions
➤ When deterministic automation is better than AI
➤ Why AI agents need identities, permissions and escalation paths
➤ How observability prevents autonomous systems from becoming dangerous black boxes
➤ What engineering teams need to operate AI safely at scale
➤ How Harness approaches everything that happens after code
Tell us what you loved about this Episode.
Website: itracusa.com
Contact: Feroz Khan
🎙️ This episode is brought to you by ITRAC.

ITRAC builds AI-ready, low-code-enabled teams.

We help organizations move from AI pilots to real production execution through:

• Strategic Staffing
• Enterprise AI & Low-Code Training
• Agentic AI Bootcamps
• CoE & Governance Advisory

AI creates momentum.
Execution creates advantage.

Whether you're scaling delivery or building your AI capability — we help you build it right.

AI is lowering the barrier to building software.

But it is not lowering the bar for initiative, judgment, or solving real problems.

In this episode of Drag & Drop, Feroz Khan sits down with Lazar Jovanovic, the first Vibe Coding Engineer at Lovable, to discuss how he went from a non-technical background to building software at one of the fastest-growing AI companies globally.

Lazar shares how building in public, learning by doing, and solving real problems helped him create an opportunity before the role even existed.

They also explore how AI is changing who can build software, why generalists have an advantage, and what enterprises need to do to enable more employee-led innovation without creating a new generation of application silos.

In this episode, you’ll learn:

➤ How Lazar became Lovable’s first Vibe Coding Engineer
➤ Why generalists have an AI advantage
➤ How building in public creates opportunity
➤ Why “vibe coding” has moved beyond prototypes
➤ How AI is changing the build versus buy decision
➤ How a $1 million tool can be built for less than $5,000
➤ Why people closest to the problem can now build the solution
➤ How enterprises can prevent AI sprawl
➤ Where to start if you want to build an AI role inside your organization
➤ Why good judgment will matter more than raw effort
Tell us what you loved about this Episode.
Website: itracusa.com
Contact: Feroz Khan
🎙️ This episode is brought to you by ITRAC.

ITRAC builds AI-ready, low-code-enabled teams.

We help organizations move from AI pilots to real production execution through:

• Strategic Staffing
• Enterprise AI & Low-Code Training
• Agentic AI Bootcamps
• CoE & Governance Advisory

AI creates momentum.
Execution creates advantage.

Whether you're scaling delivery or building your AI capability — we help you build it right.

Most organizations don’t have a shortage of AI pilots.

The real challenge is getting them governed, connected, and into production.

In this episode of Drag & Drop, Feroz Khan sits down with Scott Ditch, Principal Solutions Architect at OutSystems, to discuss what it will take for US government agencies to move beyond AI experimentation and create measurable value.

Scott shares what he is seeing across the public sector, including why many AI agents remain trapped in a “never-ending proof-of-concept state” and how disconnected projects could create the next generation of technology silos.

In this episode, you’ll learn:

➤ Why trust remains one of the biggest barriers to government AI adoption
➤ What prevents AI pilots from reaching production
➤ Why agentic systems require a different development lifecycle
➤ How agencies can govern an entire portfolio of AI systems
➤ Why human accountability remains essential in high-stakes decisions
➤ How AI can modernize decades-old government systems
➤ How organizations can preserve institutional knowledge as experienced employees retire
➤ How AI is changing the roles of developers, architects and technology teams
➤ Why one agent or one AI model cannot solve every problem
➤ How model selection and token costs affect enterprise AI architecture
➤ How agencies can adapt when AI evolves faster than procurement cycles
➤ Whether AI could eventually make traditional applications disappear
Tell us what you loved about this Episode.
Website: itracusa.com
Contact: Feroz Khan
🎙️ This episode is brought to you by ITRAC.

ITRAC builds AI-ready, low-code-enabled teams.

We help organizations move from AI pilots to real production execution through:

• Strategic Staffing
• Enterprise AI & Low-Code Training
• Agentic AI Bootcamps
• CoE & Governance Advisory

AI creates momentum.
Execution creates advantage.

Whether you're scaling delivery or building your AI capability — we help you build it right.

In this episode of Drag & Drop, I’m joined by Chris Merz from Siemens to discuss how enterprise AI is moving beyond chatbots, copilots, and isolated use cases.

We talk about why moving fast with AI is not enough, why data context and governance matter more than ever, and how platforms like Mendix are evolving beyond low-code into a much bigger enterprise AI conversation.

Chris shares his perspective on what enterprises are getting right, where many are going wrong, and why AI adoption needs to be connected to real business outcomes, not just individual productivity.

We also discuss the role of partners, the future of developers, the risk of creating AI silos, and what organizations need to think about if they want to move from AI experiments to scalable, governed AI systems.

If you’re involved in enterprise AI, application development, low-code, digital transformation, or technology strategy, this conversation is for you.
Tell us what you loved about this Episode.
Website: itracusa.com
Contact: Feroz Khan
🎙️ This episode is brought to you by ITRAC.

ITRAC builds AI-ready, low-code-enabled teams.

We help organizations move from AI pilots to real production execution through:

• Strategic Staffing
• Enterprise AI & Low-Code Training
• Agentic AI Bootcamps
• CoE & Governance Advisory

AI creates momentum.
Execution creates advantage.

Whether you're scaling delivery or building your AI capability — we help you build it right.

Enterprise AI is moving fast… but most companies still aren’t ready to operate it at scale.

In this episode of Drag & Drop, Feroz Khan is joined by Hugo Fragoso, Director, Strategic Customers - EMEA at OutSystems, to discuss what enterprise leaders are getting wrong about AI adoption, governance, internal capability, and scaling delivery in an AI-driven world.

As organizations rush to experiment with Agentic AI, copilots, and automation platforms, many are discovering that building something quickly is the easy part. The real challenge is creating the operating model, governance, and internal capability needed to run AI reliably inside the enterprise.

Hugo shares insights from working directly with strategic enterprise customers across EMEA and explains why the companies succeeding with AI are thinking far beyond individual use cases.

What you’ll learn in this episode:

• Why the most successful enterprises are “building factories” instead of individual apps
• The biggest mistake companies are making with AI strategy right now
• Why platform governance matters more than choosing a single LLM
• The danger of relying on “hero developers” inside enterprise teams
• Why internal enablement is critical for long-term low-code and AI success
• How enterprises should balance partners vs internal capability
• The role architecture, governance, and operating models play in scaling AI
• Why security and risk teams need to be involved from day one
• What separates enterprises that scale from those that stall
• Why governed AI will define the winners over the next few years

The conversation around AI is shifting.

From simply generating outputs…to building systems that enterprises can actually trust, govern, scale, and operate long term.
Tell us what you loved about this Episode.
Website: itracusa.com
Contact: Feroz Khan
🎙️ This episode is brought to you by ITRAC.

ITRAC builds AI-ready, low-code-enabled teams.

We help organizations move from AI pilots to real production execution through:

• Strategic Staffing
• Enterprise AI & Low-Code Training
• Agentic AI Bootcamps
• CoE & Governance Advisory

AI creates momentum.
Execution creates advantage.

Whether you're scaling delivery or building your AI capability — we help you build it right.