Why 2026 Will Be Remembered as New Era of AI

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It’s hard to ignore what’s happening in AI right now. Over just a few months, nearly every major player has moved in the same direction: AI Agents.

Back in May, Google unveiled one of its biggest AI updates yet: Gemini Spark, a personal AI Agent that runs in the cloud 24/7 through MCP (Model Context Protocol) to manage tasks autonomously. And Google wasn't alone. Within the same week, Anthropic introduced Claude Managed Agents for long-running workflows. OpenAI expanded Codex onto AWS and reported more than five million weekly users. Meanwhile, Microsoft took the stage at Build 2026 and officially declared the start of the ‘Agentic Era,’ introducing Windows Agent Runtime and bringing AI Agents directly into the operating system itself.

Looking beyond product hype, the real battleground of 2026 is about Infrastructure. When AI has to plan, coordinate tools, and run independently for days, the backend infrastructure becomes just as vital as the model’s intelligence.

AI Agents are not just better chatbots

A common misunderstanding is that AI Agents are simply improved versions of Generative AI. In reality, the difference is structural.

• Generative AI is reactive: It waits for input, then produces an output. One prompt, one response.

• AI Agents are proactive: They can set goals, plan steps, make decisions, use tools, and complete multi-step tasks without needing constant human instructions.

  
  

To put it simply,  Generative AI is like a skilled analyst who needs detailed instructions at every step while AI Agents are more like an employee who can take a brief, execute the work independently, and only escalate when human judgment is needed.

How The Major Players Are Positioning Themselves

Google

Google's strategy is straightforward: bring AI into the tools people already use. As a result, Gemini is now deeply integrated across Gmail, Docs, Calendar, Maps, YouTube, Android, and Chrome. 

At the same time, Gemini Spark acts as a personal AI Agent running continuously on Google Cloud, capable of managing tasks even when users aren't actively online. 

For developers, Google also introduced Antigravity 2.0, moving beyond AI-assisted coding tools to a full-scale platform for building and managing multi-AI Agents. Powered by Gemini 3.5 Flash, Google claims it delivers significantly faster performance at a lower cost than many frontier models.

Anthropic

Anthropic is taking a different approach. Rather than focusing primarily on personal productivity, it's positioning Claude as enterprise infrastructure. Claude Managed Agents allows organizations to build and manage multi-agent systems within their own environments. Features such as testing sandboxes and private MCP servers help organizations maintain control over sensitive information and meet compliance requirements.

Partnerships with companies like SAP and PwC show its direction toward real enterprise workflows, including regulated industries such as finance and healthcare.

OpenAI

OpenAI's story is particularly interesting. Codex may have started as a coding tool, but its user base is rapidly expanding beyond developers. According to OpenAI, more than 20% of Codex users are now financial analysts, marketers, researchers, and other business professionals. Thus, that group is growing faster than the developer segment itself. In response, OpenAI has begun building role-specific plugins and workflows designed for different professions rather than focusing exclusively on software engineering.

Another notable move is OpenAI's decision to make Codex available directly on AWS. Instead of asking organizations to build new infrastructure, OpenAI is bringing AI Agents into cloud environments that companies already use today.

Microsoft

Microsoft may have made the boldest statement of all. At Build 2026, Satya Nadella described the company's shift from building platforms for applications to building platforms for agents.

Windows Agent Runtime is a major step in that direction. By embedding agents directly into the operating system, Microsoft enables them to interact with applications across Windows rather than operating inside a single tool. At the same time, Copilot Studio now supports agent-to-agent collaboration, allowing agents to hand work off to one another across systems using MCP.

But Microsoft's biggest advantage may not be technology. It's distribution. Most enterprises already rely on Windows, Microsoft 365, Teams, and Azure. That means organizations can deploy Copilot Agents without dramatically changing existing workflows.

The Smarter the Agent, the Heavier the Infrastructure

As AI agents tackle increasingly prolonged and complex workflows, compute costs, latency, and data orchestration become massive bottlenecks. This reality has forced global technology into a game of Vertical Integration, controlling the AI stack from top to bottom (from chips up to applications) to minimize costs and maximize long-term efficiency.

A clear example is Alibaba at its 2026 Cloud Summit. Instead of only launching a new model (Qwen 3.7-Max), Alibaba introduced a full-stack infrastructure strategy, including AI-optimized servers and a custom chip (Zhenwu M890) designed specifically for agent workloads. The roadmap extends to 2028 to reduce dependency on external systems.

Alibaba is also using AI to optimize its own cloud infrastructure, showing how AI is now feeding back into system design.

AI Chips: A Global Strategic Asset

Alibaba's move is not by coincidence; it demonstrates a geopolitical and industrial reality. Over the past few years, strict export restrictions on advanced semiconductors have accelerated domestic R&D within China. This has turned the AI race from a mere corporate sprint into a battle of national strategic capabilities.

And, Alibaba isn't alone in this game:

• Google continues to aggressively iterate on its custom Tensor Processing Units (TPUs).

• Amazon is expanding its Trainium and Inferentia silicon footprints across AWS (the very cloud infrastructure OpenAI selected to scale Codex).

• Microsoft is deeply embedding proprietary AI hardware architectures directly within the fabric of Azure.

Ultimately, owning the infrastructure means owning the variables that dictate business success: cost efficiency, execution speed, and absolute data control.

What Organisation should be aware?

Many organizations are still watching from the sidelines, waiting to see how the market evolves. At first glance, that may seem like a reasonable approach. However, AI adoption has already begun to move. 

AI Agents are already moving beyond IT and engineering teams into everyday business functions. At the same time, standards like MCP are accelerating ecosystem connectivity, making integration easier across systems.

Back in 2006, many legacy enterprises viewed cloud computing as an experimental tool suited only for startups trying to avoid buying physical servers. A decade later, the cloud was the undisputed backbone of global business, and early adopters secured structural advantages that lasted for a generation.

The year 2026 is delivering that same turning point. When the world's largest technology companies simultaneously re-engineer their entire stacks around agentic infrastructure, it isn't just a routine product update cycle. It is a signal that the fundamental nature of work has changed.

Success in this era is no longer about choosing the best model. It is about building the right environment for AI to operate securely, efficiently, and at scale within real business systems. This includes data infrastructure, system integration, security, and governance.

At Sertis, we specialize in architecting this exact foundation. We help enterprises design robust AI infrastructure, manage complex data pipelines, integrate cross-system workflows, and build bespoke Private LLMs that keep your data secure and compliant.

If you're exploring how AI Agents can create real business value for your organization, contact us for more information: contact us

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