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In November 2024, I did something that was both rare and deeply necessary for myself: I stepped away from the world of professional tech.
6 months off with no code, no tickets, no Teams pings. Just space to rest, reset, and rediscover who I am beyond the title of software engineer. The review? It was absolutely worth it.
The Value of Doing ‘Nothing’
I dedicated this time to non-tech pursuits: baking, ticking off bucket-list travel destinations, and most importantly, being present with loved ones, because time waits for no one. What I learned is that rest is not a break from progress, it is progress. It gave me clarity, fresh perspectives, and renewed energy. Ironically, while I took a step back, AI was evolving to do everything.
When I returned in May 2025, the world had indeed evolved, and AI had leapt. As I got back into the flow of engineering work, I realized I was not just re-onboarding into a company with many new faces, I was re-onboarding into an entirely new way of working with new shiny AI tools.
From Assistants to Agents: What Changed While I Was Away
Before I left, AI tools in engineering mostly lived on the edges with its autocomplete suggestions, Copilot snippets, or one-off code generation. We welcomed it with open arms - tools that help us become efficient & useful? Absolutely. But reactive, not proactive. They waited for input and helped us go faster, but only in ways we directly initiated and reduced the time I spent searching for solutions.
I returned to a landscape reshaped by something much bigger than just automation. Now, AI has stepped into the driver's seat.
We’re entering the era of autonomous AI agents or simply put agentic AI: systems that can reason, plan and execute independently across multiple steps. These tools can decide what to do, how to do it and even act as our blind spots detectors or suggest improvements along the way. Some can even take autonomous action all the way only if we allow it.
Re-Onboarding into an AI-Native Workplace - Gen-e2
After returning from sabbatical in May 2025, I was quickly onboarded into one of my company’s most promising initiatives — the Gen-e2 offering.
Unlike traditional tools, Gen-e2 is not just reactive—it’s proactive. It redefines how we build, design, and ship software. All employees went through dedicated training to shift how we apply Gen-e2, challenging us to shift our mindset around how we deliver software from the ground up.
Gen-e2 can be introduced in focused and practical ways. A great starting point is to pilot it on a moderately complex product or internal tool, where full-stack AI integration can be tested with lower risk. Have clear goals for the pilot such as faster delivery, fewer revisions and reducing team capacity can help to track the impact.
It is also important to form a cross-functional team, where designers, engineers and business leads all collaborate making Gen-e2 as an active partner. The teams that treat Gen-e2 as a true collaborator (not just another tool) are already discovering how it can shift their opinions about speed, quality and what’s possible within a typical product development lifecycle.
And the results? Gen-e2 has allowed us deliver projects two to five times faster than traditional methods by using AI to handle around 95% of the coding, documentation and architecture work. The code isn’t just faster—it’s often more aligned with product goals, thanks to tighter context modeling, build provenance, and domain-driven prompting.
Traditional Approach vs AI first approach Style
Software engineering has always been built on structure — we write code, test and deploy. Then, we repeat again. In the traditional model, we are responsible for all the planning, coding and decision making. Tools like linters, CI/CD pipelines and documentation generators help, but they are passive where they wait for us to take action and initiate.
Key Shifts in Engineering with Gen-e2
Here’s how using Gen-e2 way of delivering is reshaping my day-to-day work:
1) Synthetic Programming over Manual Coding
Collaboration in software engineering has traditionally meant pairing with another engineer, reviewing each other’s code and depending on each other’s availability to move work forward. While valuable, these steps created friction - introducing delays, coordination overheads and the mental effort of syncing up constantly.
With Gen-e2 and AI tools, many of those bottlenecks are being lifted. Code can now be generated, reviewed and improved with minimal input, letting us shift from manual, line-by-line programming. With this synthetic programming shift, the engineers focus on guiding architecture and logic, while agents handle the heavy lifting.
We are being freed up to focus on what matters: architecture designing, expanding new feature offerings and real collaboration.
2) Bridging code and domain knowledge
In fast-moving projects, the biggest threat isn’t just tech debt—it’s context loss. Domain knowledge gets siloed, decisions are forgotten, and reasoning behind designs erodes over time. Over time, solutions may get re-written, and context gets lost as people roll off projects or shift focus. The possibility of lack of shared context can lead to duplicate efforts or misaligned decisions.
Gen-e2 addresses this using context prompting protocols that retain why and how we build. It integrates domain models, architecture logic, and even prior decision logs. It supports domain-driven design principles by reinforcing consistent language, logic and architecture. This tight coupling between device models, code, and team understanding helps reinforce domain-driven design and prevents duplicate efforts.
3) Adapting to New Tech Stacks — Learning Quickly with AI
In consulting, one of the most common challenges I have faced is jumping into projects with unfamiliar tech stacks. Sometimes we don’t have all the required skills and learning fast, under pressure, can be overwhelming. There is often little time to truly understand the tools before you are expected to ship.
With Gen-e2, I can now onboard onto new frameworks and languages faster than ever. It doesn’t just generate boilerplate—it explains what the code is doing, recommends best practices, and flags anti-patterns. We spend less time being stuck and more time expanding with quality.
This shift supports a more adaptive, resilient engineering culture, especially for cross-functional teams handling diverse client needs. From doing every step ourselves to orchestrating smarter systems that handle the groundwork and surface the complex, creative, human decisions.
What excites me most is how this opens up space to think more deeply, to experiment faster and to focus on what really matters: solving meaningful problems with our stakeholders.
Closing Thoughts: Rethinking the Role of the Engineer
Here is where it gets funny. I took a sabbatical to escape constant context-switching, short delivery notices resulting in mental overload. Now, I’m back and AI is helping me to reduce that very burdened work.
Perhaps the next phase of software engineering is not just about building smarter systems but building sustainable careers where we engineers can thrive without burning out. We are also no longer just doing every step ourselves; we are now orchestrators of intelligence. I feel that is already how things are happening, quietly and quickly.
Lastly, to anyone considering a sabbatical: take it. The world will keep evolving to something greater. And if you are lucky, your AI teammates might even take care of a few tickets while you are away pursuing whatever makes you feel most human. When you return, whether it’s to a familiar team or a reimagined tech landscape, you might just find that the work has shifted in your favour. Not because the pressure disappeared, but because the tools got smarter, the workflows more human, and the role you play more intentional.
We’re not here to outrun change anymore. We’re here to co-create with it. So if you’re curious where to start, start with a conversation.
At PALO IT, we’re not just adopting AI, we’re rethinking how people, process, and purpose come together in a new kind of engineering.
Let’s chat about how we can help your team work better, build faster, and thrive more sustainably in the era of agentic AI.