Key take-aways from Microsoft Build & Google I/O 

Generative AI (GenAI) is transforming businesses at an unprecedented pace. Tools such as ChatGPT, GitHub Copilot, Vercel, and others now offer powerful ways to support product teams in creating better products faster.

In late May, Microsoft Build and Google I/O landed a coordinated message: the paradigm has shifted, we’re not just integrating AI into software, we’re engineering systems designed around it. 

Announcements from both events marked a clear departure from chat-based assistants to fully agent-native systems. AI agents don’t just assist tasks, but navigate platforms, write code, make decisions, and interface across tools and systems independently. (blogs.windows.com, blog.google) 

At PALO IT, we’ve been closely tracking how organisations are putting AI agents to work as part of our trademarked Gen-e2 methodology.  

Unlocking true quality and efficiency at scale isn’t just about asking questions, it’s about taking bold steps forward. As AI accelerates digital transformation, the real challenge for digital leaders is no longer deciding whether to adopt agents, but discovering how to scale them swiftly, responsibly, and with lasting impact. Here's what was teased, released at these events and why we are excited at PALO IT: 

1 · Agents become the operating layer

We’ve crossed a threshold. AI agents no longer sit on the side as productivity boosts—they are now deeply embedded into the platform fabric: Windows 11, Chrome, GitHub, Vertex AI, Figma. 

MCP acts as a signed, language-agnostic bridge between AI agents and conventional code, so a capability we wrapped once as an MCP skill can be reused by any compliant agent with minimal glue—shrinking the integration time we usually burn in multi-cloud projects. It’s architecture-level transformation.  (blogs.windows.com, microsoft.com, blog.google) 

Think of the Model Context Protocol as a universal power socket: build one “adapter” for a task—say, pulling Jira tickets—and every agent (Copilot, Gemini, or next week’s newcomer) plugs in without rewiring, trimming integration cost each time you expand the stack. For your technical teams, that means wrapping the capability once as an MCP skill, publishing it to the repo, and letting any compliant agent call it with a single YAML line—no bespoke wrappers, less glue code, more feature work.

Where we are headed: Systems will be increasingly agent-operable by design. Not AI-enhanced, AI-driven.

2 · Hybrid AI goes mainstream 

Both vendors solved a pain that haunts regulated projects: where should the prompt run?  
 
Why do you have to choose?  Both vendors made hybrid AI a first-class concern. Azure AI Foundry’s Model Router can choose an on-device model when data is sensitive, or a cloud large language models (LLM) when context is huge; Gemini Live applies the same split-brain approach between device NPUs and Vertex AI. This forces teams to make architectural choices, not just tooling ones. (azure.microsoft.com, azure.microsoft.com, blog.google) 

Choosing where a prompt runs now feels like picking between a home safe and a bank vault on demand: payroll or patient data stays on-device, while heavy analytics still tap cloud horsepower, delivering speed, compliance, and roughly a 45 percent cut in progress cost. For your technical teams, the playbook is simple—tag prompts “private/low-latency” to route to the laptop NPU and “public/large-context” to the cloud LLM; Azure Foundry Router and Gemini Live handle the switchboard, eliminating custom routing code. 

Bottom line: AI delivery is now about orchestration, not just generation. 

3 · Content pipelines are now one prompt away 

Google has stitched its entire AI-powered creative stack into a seamless, timeline-style editor called Flow. Every frame, note and pixel arrives stamped with an invisible SynthID watermark for provenance. Even better, a half-day AI-tooling session now lets us leave customers with a self-serve kit: prompt templates, guard-rails and clear guidance so their own teams can spin out high-fidelity content that fits their context instead of generic stock.  

Brief marketing at 9 a.m. and review a fully branded promo video by lunch—usage rights and watermarking included—so campaign cycles compress from weeks to hours.  

For your technical teams, Google’s Flow timeline exposes every asset as JSON blocks; drop those blocks into the CMS or design system and the next campaign inherits the same prompt templates, ending the scavenger hunt for old PSDs and enabling lightning-fast iterations. 

Changing priorities: The creative bottleneck shifts from hunting for the right assets to simply deciding which prompt to run or orchestrating the perfect custom prompt for their use case; an upgrade every product owner can immediately appreciate.

4 · Responsible tech gets concrete  

Security & provenance announcements were just as big as the shiny demos. 

Security and provenance announcements were every bit as significant as the headline demos. The new agent stacks bake trust in from the first keystroke: 

• MCP on Windows 11 mandates that each tool call is cryptographically signed and user-permissioned. 

• Google’s SynthID invisibly watermarks AI-generated images, audio, video—even fragments—so auditors can trace them back to source. 

Auditors now receive a one-click provenance report—who generated what, when, and with which model—slashing compliance overhead and boosting stakeholder trust. For your technical teams, MCP signing and SynthID turn security hooks into first-class APIs: log the signature, embed the watermark, ship, and watch security reviews shrink from marathons to quick checklists.  (blogs.windows.com, blog.google) 

 Currently, most teams rely on existing regulations to manage risk, but struggle keep pace with how fast AI evolves. Tools like SynthID and cryptographic signing aren’t just technical upgrades; they offer a new foundation to build smarter governance strategies around. Instead of chasing compliance retroactively, we get to shape it from the start. 

Change in approach: Engineers should code provenance by default. Security turns into a built-in feature, not a retrofitted checkbox. 

Reshaping developer roles at PALO IT 

2025 marks the moment AI stopped being a bolt-on. Agentic systems are here to stay, and they’re changing the way we work, design, and think about product development. With growing maturity and confidence in using AI tools seamlessly within their workflows, developers are redefining focus inside their development environments, prioritising and refining AI-augmented solutions. 

If you’re in software delivery, especially in consultancy or solutioning roles, this is where we see key changes happening:  

1. Map “agent-able” workflows. Look for repeatable → high-value → API-accessible tasks (think pull-request hygiene, contract review, user-research synthesis). 

1. Define a hybrid policy early. Classify prompts/data by privacy and latency to decide which run local vs cloud. 

1. Build your own MCP server—using the reference implementation and documentation— give development, security, and operations teams a low-stakes environment for validating agent permissions, telemetry, and audit trails before scaling to production workflows. 

We’ve baked these into our Gen-e2 framework—because real value in AI delivery isn’t just about generating faster. It’s about architecting responsibly, scaling cleanly, and giving teams back time to solve the problems that matter. (palo-it.com) 

The road ahead 

My takeaway from Build and I/O is simple: design with agents in mind, architect for hybrid execution, and treat provenance as non-negotiable. The yak-shaving is done by code that writes itself; that frees us to solve the problems that matter.

At PALO IT, we're already helping teams across finance, health-tech, and mobility stand up secure, AI-native agent pilots—often in under four weeks. From Gen-e2 automation to hybrid deployment strategies and agent-ready toolchains, we’re actively shaping what responsible, future-ready development looks like.

Curious about what this could mean for your team? Let’s explore how to make real impact with agentic AI—responsibly, and at speed.