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Microsoft’s ‘open agentic web’ represents a significant evolution of the internet, moving from a system primarily designed for human consumption of information to one in which AI agents can autonomously understand, interact with, and act upon web content.
Unlike AI chatbots, which simply respond to commands, AI agents work autonomously, collaborating with each other across different platforms and services to get things done. This shift will have profound impacts on both users and enterprises.
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In a post on X, Nadella elaborated, “we are building the open agentic web. It is reshaping every layer of the stack, and our goal is to help every developer build apps and agents that empower people and organisations everywhere.”
For instance, coding agents are being built into GitHub, Microsoft’s platform for software developers, to allow AI to autonomously fix bugs, add new features or simply maintain code.
Let’s take a closer look at the changes and their implications.
What’s the ‘open agentic web’?
Championed by Microsoft and others, the open agentic web is a vision of a future web in which AI agents seamlessly interact across platforms, services and devices to help users and organisations complete tasks. It’s a vision of the web in which AI agents autonomously retrieve, process and act on information.
On the open agentic web, AI agents will communicate with each other, making decisions and solving problems without constant human input. Enterprises will be able to create custom AI agents that understand their unique processes and workflows.
This marks a step towards making AI more useful and independent, allowing people and businesses to automate tasks, improve efficiency, and create smarter and more intuitive digital experiences.
All of this will be enabled by a standardised AI communication protocol developed by Microsoft called NLWeb, in which NL stands for natural language.
Can you tell me more about NLWeb?
NLWeb is an open project that aims to make it easy for any website to integrate natural language interfaces and AI-powered search capabilities. It allows users to engage with web content using conversational language, just like chatting with an AI assistant.
NLWeb is similar to Hyper Text Markup Language (HTML), the foundation of the web as we know it. HTML is used to structure and organise web pages, and text, images and multimedia content on the web). NLWeb will do the same, but with AI agents instead of humans performing the tasks.
The difference is that while HTML structures content for humans to read and interact with directly, NLWeb aims to structure content so that AI agents can understand, process, and act upon it autonomously or to better serve human queries.
What difference will NLWeb make?
Microsoft envisions NLWeb as a cornerstone of the open agentic web. Just like HTML revolutionised web accessibility, NLWeb aims to make AI-powered interactions standard.
As AI agents become more prevalent, NLWeb will ensure websites remain discoverable and interactive. Developers will be able to tailor NLWeb to their needs, integrating custom AI models and data sources.
How will different AI agents collaborate?
AI agents will collaborate through multi-agent systems, in which specialised AI agents work together to complete complex tasks.
For example, people’s experiences with customer service chatbots can be frustrating because these bots cannot comprehend various nuances. In the open agentic web, one AI agent may handle query retrieval while another focuses on sentiment analysis and response generation, ensuring a much better experience.
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AI agents will also be able to delegate tasks to other specialised agents, ensuring each focuses on its area of expertise. Agents will communicate using Agent2Agent (A2A) protocols, allowing seamless interaction across platforms.
How will the open agentic web impact users and enterprises?
For starters, it will create more personalised and context-aware AI. AI agents will be able to recall past interactions and provide more relevant responses.
AI agents will also be able to process and act on web information autonomously. Eventually, websites will become interactive through NLWeb, allowing users to engage with them through natural language.
For instance, the open agentic web will be able to create personalised experiences for users. AI agents will understand individual preferences and proactively assist with tasks such as research, planning trips, or summarising content, without users needing to hunt for information themselves.
A user interested in, say, FIFA World Cup 2026, to be hosted jointly by the US, Canada and Mexico, will get information and updates on it once AI agents know of his interest.
For routine tasks, AI-driven tools could generate content, suggest ideas, or automate tasks—freeing up users to focus on more creative and meaningful work.
For businesses, it will be easier to deploy AI agents to handle customer service inquiries, manage supply chains, streamline workflows and more. It will also allow for better interoperability and quicker innovation, with AI agents collaborating to deliver results.
What are the potential downsides?
While AI agents acting autonomously sounds exciting, there are potential risks as well. That’s because they are not passive, like AI chatbots, but can make decisions on their own. There could therefore be issues spanning lack of transparency, trust, ethics, biases, security, accountability to regulators, and other challenges.
AI agents may make decisions in ways that are difficult for users or developers to understand, leading to trust issues. AI systems can also unintentionally reinforce biases present in their training data, leading to unfair or discriminatory outcomes. For instance, if all the CEOs of a 100-year-old company have been male, it might overlook female candidates when asked to select potential replacements.
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Autonomous AI agents could also be exploited by malicious actors, leading to data breaches, misinformation, or unauthorised actions. Besides, over-reliance on AI is in itself a risk. Users and businesses may become too dependent on AI agents, reducing human oversight and critical thinking in decision-making.
While the risks of autonomous AI systems are significant, they can be mitigated (though perhaps not completely eliminated) through strong governance, ethical AI development, and user education.
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