Summary: AI is no longer a feature—it's the foundation. As enterprises embrace AI-first mandates, the traditional Software Development Lifecycle must evolve. This article explores how leading organizations are rethinking roles, retooling processes, and rebuilding the SDLC to support human–AI collaboration at scale. From agentic development to digital twins, here’s how software is being reimagined for the AI-first era.
As AI becomes a standard expectation across the enterprise, organizations must reengineer their software development lifecycle (SDLC)—shifting from siloed delivery models to orchestrated human–AI collaboration.
Executive Intent: AI as a Mandate, Not a Tool
A wave of CEO memos from Shopify, Duolingo, and Box makes one thing clear: AI isn’t optional—it’s foundational.
At Shopify, CEO Tobi Lütke set the tone: “Using AI effectively is now a fundamental expectation of everyone at Shopify… If you’re not climbing, you’re sliding.” Reflexive AI use is now baked into performance reviews, prototype phases, and even hiring conversations.
Duolingo’s Natalie Glance echoed this mindset: “Start with AI for every task… We’re not aiming for more code, but for a better product built smarter—with AI.”
Box CEO Aaron Levie made the shift personal: “AI has been increasingly changing my daily work as CEO… Content, research, product prototyping—it all starts with AI now.”
These aren’t isolated initiatives—they’re enterprise mandates. But bold declarations require more than new tools—they demand a fundamental rethinking of how software is built, structured, and governed.
Using AI effectively is now a fundamental expectation of everyone at Shopify… If you’re not climbing, you’re sliding.
The Agentic Shift: When AI Builds the Stack
At AI Native DevCon and beyond, one theme is clear: the Software Development Lifecycle (SDLC) must be reimagined to support autonomous agents.
AI copilots have moved beyond code suggestions. They now build functional components across the entire stack—from frontend UI and backend logic to infrastructure configuration and deployment workflows.
So what does this mean for traditional roles? If an agent can cut across functional silos in a single pass, why do our org charts still reflect those silos?
This isn’t a marginal productivity boost—it’s a structural shift. The SDLC, once defined by linear handoffs and human execution, must now support fluid, multi-agent orchestration.
Supervision Over Production: The New Developer Role
The developer role is evolving—from builder to curator and supervisor.
As JetBrains’ Anton Arkhipov noted, AI support spans a continuum—from autocomplete to full-stack generation. As developers hand off more execution to agents, their value shifts toward articulating intent, overseeing quality, and making architectural decisions.
And with AI dissolving the boundaries between frontend, backend, DevOps, and QA, developers now operate across the entire application surface. This demands a systems-level mindset. The developer of tomorrow begins to resemble the system architect of today. Reviewing agent output isn’t just a syntax check—it’s a design audit across multiple layers.
This reframes talent evaluation. It’s no longer about velocity or ticket volume—it’s about the ability to guide AI, validate outputs, and uphold systemic integrity.
Guardrails Must Move Left
As AI accelerates development, it also introduces a paradox: more code, less clarity.
Danny Allan of Snyk put it bluntly: “We’re entering the age of risky software.” Agents create risk not just through hallucinations, but via non-deterministic, untraceable changes that bypass traditional review processes.
The solution? Shift governance upstream.
Modern SDLCs must embed security policies, dependency validation, and compliance checks at the point of generation—not just at the pull request. Agent behavior should be observable, with lineage tracked in version control.
In the agentic era, governance isn’t just about catching mistakes—it’s about creating programmable trust.
Vision to Execution: Why Digital Twins Matter Now
AI-first transformation starts with vision—but it succeeds through operational rigor. Rebuilding the SDLC for human–agent collaboration raises high-stakes questions:
- How many AI-assisted developers do we actually need?
- Which workflows will break as autonomy increases?
- How do we verify quality across invisible, AI-generated layers?
- Where are the bottlenecks hiding in our processes?
You can’t answer these through theory alone. You need simulation. That’s where Process Digital Twins become essential.
At VSOptima, we build operational digital twins of the SDLC. By modeling interactions between humans, agents, and tools, we help leaders:
- Test new team structures for hybrid work
- Evaluate supervision capacity and review strategies
- Measure throughput gains and quality trade-offs at different automation levels
- Simulate rollouts before they hit production
This shifts AI adoption from experimentation to engineered transformation—anchored in data, not assumption.
Just as digital twins of supply chains help forecast inventory and demand, digital twins of the SDLC help quantify capacity, control risk, and uphold quality at scale.
From Experiments to Operating Models
Across the tech landscape, executive messaging is aligned: AI isn’t the future of work—it’s the present reality.
But moving beyond scattered experiments means building sustainable, measurable, and governed systems. The agentic SDLC is the blueprint. Process Digital Twins are the control surface. This transformation isn’t just technical—it’s systemic.
Organizations that move from AI experimentation to orchestration—with simulation, supervision, and strategic clarity—will define the next era of software excellence.
Pavel Azaletskiy
Editor Viktoryia Karalionak