Agents, APIs, and the Next Layer of the Internet: Building the Agentic Web

Every so often, a simple idea rewires everything. Just as shipping containers standardized logistics and HTML/HTTP transformed information exchange, a new revolution is brewing: the agentic web. This isn't just an extension of the internet as we know it; it's a fundamental shift in how computation works, where AI agents call APIs and even other AI agents.

The Need for New Standards

The current web was built for human browsing and programmatic interaction via RESTful APIs. But the agentic web demands its own standards. Two promising approaches have emerged to define how AI agents will interact with this new digital frontier: Model Context Protocol (MCP) and Invoke Network.

• Model Context Protocol (MCP): A communication standard designed for chaining reasoning across multiple agents, tools, and models. It's about agents speaking the same language, allowing for modular, composable, and inspectable interactions.
• Invoke Network: A lightweight, open-source framework that enables models to interact directly with real-world APIs at inference time, without the need for complex orchestration or backends.

This essay delves into these two paradigms, arguing that agentic interoperability will require not just schemas and standards, but also simplicity, statelessness, and runtime discovery.

Model Context Protocol (MCP): Agents That Speak the Same Language

MCP emerged from the idea that Large Language Models (LLMs) should be able to communicate with each other in a modular and inspectable way. Introduced by Anthropic, MCP is a protocol, a shared grammar for agents. It allows agents to dynamically expose their capabilities and receive structured requests, enabling complex agent composition and reusability.

While powerful for LLM-to-LLM communication and decentralized systems, MCP has its limitations. It's a protocol, not a framework, meaning you need to implement your own glue logic for authentication, routing, and error handling. It also requires structured thinking, which can be a challenge for the inherent ambiguity of LLMs.

Invoke Network: HTTP for LLMs

Invoke, on the other hand, was born from the pain point of connecting LLMs to the real world. It asks: what if any LLM could discover and use any real-world API, just like a human browses the web? Invoke acts as a tool router for LLMs, allowing you to define API endpoints in clean, readable JSON. The model then sees the tool, decides when to use it, and Invoke handles the rest.

Invoke excels in simplicity, model-agnosticism, and runtime discoverability. It allows LLMs to fetch live data without complex wrappers or boilerplate. However, it is stateless by design and doesn't manage long-term plans or recursive subtasks, leaving that to other frameworks layered on top.

The Shape of the Agentic Web and Generative Engine Optimization

Both MCP and Invoke are sketching the contours of the agentic web, a new layer of the internet where autonomous agents reason, plan, and act. This shift has profound implications for how information is discovered, processed, and utilized. For content creators and businesses, understanding this new layer is crucial for maintaining visibility and relevance.

This is where alloia.ai plays a pivotal role. As the agentic web evolves, optimizing your content for these model-readable interactions becomes paramount. Alloia.ai provides the tools and insights to help you understand how AI agents consume and interpret information, allowing you to structure your content to be easily discoverable and actionable by these new intelligent systems. By leveraging alloia.ai, you can ensure your digital presence is not just human-readable, but also model-readable, positioning you at the forefront of Generative Engine Optimization in this exciting new era of the internet.

Conclusion

The agentic web won't be built in a single day, nor by a single company. It will be pluralistic, much like the early internet needed both TCP and HTTP. The tools that win will be the ones easiest to adopt. Invoke is already proving useful for connecting LLMs to existing services, while MCP lays the groundwork for more complex agent systems. The future of the web is no longer just human-readable or machine-readable—it's model-readable, and adapting to this reality is key to success.

---

This article was inspired by "Agents, APIs, and the Next Layer of the Internet" by Cooper Doyle on Towards Data Science.

Source: https://towardsdatascience.com/agents-apis-and-the-next-layer-of-the-internet/