At a recent series of roundtables hosted by PALO IT and GitHub, tech leaders and builders gathered to explore one of the most important shifts in modern software delivery: the rise of agentic AI. What unfolded was not a typical product pitch or pilot showcase, but a strategic, on-the-ground playbook for integrating AI agents into real software workflows. 

If you missed it, here’s what you need to catch up and why leaders can’t afford to sit this one out. 

Gen‑e2: Moving AI From Assistant to Architect 

According to Kevin Aubry, CTO at PALO IT Singapore, the real unlock came when teams moved beyond passive AI tooling and began working alongside AI as a design and delivery partner. Enter Gen‑e2™, PALO IT’s AI delivery methodology, designed not just for code generation, but for managing context, architecture, user stories, testing, accessibility, and team alignment. 

“It’s not about giving everyone a Copilot license. It’s about training on real projects, iterating fast, and embedding coaching and feedback loops,” Aubry emphasized. 

Built as the next evolution beyond Agile and DevOps, Gen‑e2 helps teams structure the context AI needs to operate effectively, from meeting transcripts and Figma designs to testing protocols and business goals. The result? Faster iteration with higher alignment.  

GitHub’s Vision: From IDEs to Agentic AI 

GitHub’s Shlomi Shaki, Strategic AI Go-to-Market Lead, shared the larger vision behind GitHub’s agentic platform evolution. Copilot is no longer just a coding companion—it’s an active collaborator in everything from PR generation and issue management to reviewing and auto fixing vulnerabilities. 

“The real delays in development aren’t about writing code. They’re in design handovers, unclear specs, context switching, and dependencies,” Shlomi explained. 

GitHub’s Copilot Agents, coupled with Model Context Protocol (MCP), now allow teams to automate complex multi-step tasks securely—while still giving humans the final say. In some use cases, this has shortened review and deployment cycles by up to 40%. 

What AI in the Software Development Life Cycle Looks Like in Practice

One of the key values of the roundtable was showing—not telling—how Gen‑e2 works: 

  • A Figma file was converted into a structured implementation plan. 
  • API specs were integrated to generate functional, testable code. 
  • Reusable prompt templates enforced company-wide rules and conventions. 
  • Multiple AI models (GPT‑4, Claude, Gemini) were used in tandem, with fallback logic and human review. 

Importantly, human-in-the-loop validation remained essential at every stage, especially in verifying visual designs, styling, and accessibility. 

Real Results from the Field: AI Software Examples

PALO IT shared hard data from early adopters of Gen‑e2: 

  • ⚡ 86% productivity boost in one client project—delivering earlier and with more scope. 
  • 🔁 5-week delivery cycle (down from 9) for a complex project  with Singapore Airlines, including Gen‑e2 onboarding, which was featured in the global news.

These results are not outliers. A 2024 study found that 88% of developers using Copilot completed tasks faster, and 90% reported improved job satisfaction. Salesforce reported that 96% of developers believe AI agents enhance their developer experience, helping shift time from maintenance to meaningful work. 

AI Agents in Business: Why This Matters Right Now

This shift to agentic AI is accelerating faster than many realize: 

  • McKinsey (2024) reported that fewer than 10% of gen-AI pilots scale—but agentic frameworks like Gen‑e2 are changing that, offering repeatable pathways to value. 
  • Gartner (2024) forecasts that by 2028, one-third of enterprise applications will have agentic AI embedded. 
  • Wired (2025) and Business Insider (2025) highlighted the need for frameworks like MCP to ensure responsible, scalable agent orchestration across enterprises. 

AI Transformation: What Tech Leaders Should Be Doing Now

If you're leading a tech, product, or innovation team, here are four actions to take: 

“Agentic software isn’t about replacing developers—it’s about freeing them to focus on what matters,” said Aubry. “And the organizations that embrace that shift early will lead the next wave.”

Final Word: You Didn’t Just Miss a Demo. You Missed the Blueprint. 

This wasn’t a show-and-tell. It was a strategic moment. Gen‑e2 and GitHub’s AI agent vision are showing us what future-ready delivery looks like—fast, traceable, aligned, and human-centered. For leaders, the choice now is clear: scale—or play catch-up later. 

We've worked with Fortune 1000 clients in delivering projects 2 to 5 times faster than conventional approaches, all by using AI to generate 95% of code, documentation, architecture diagrams and infrastructure as code. Get in touch with our team of experts today, and let's explore what's possible at your business.
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