## Core Foundations: Intelligence, Interaction, and Economics

Building the Agentic Web requires rethinking three fundamental dimensions of the Web’s architecture. Yang et al. (2025) define these as **Intelligence, Interaction, and Economy** – together forming the “three pillars” of the Agentic Web’s conceptual framework. Let’s break down each dimension and what it means for developers:

* **Intelligence Dimension:** This refers to the AI capabilities an agent must have to function autonomously. Instead of narrow, hardcoded behaviors, agents need *generalizable intelligence*. Key skills include: understanding context (e.g. parsing natural language instructions, interpreting web content), **long-horizon planning** (strategizing over multiple steps to achieve a goal), **learning and adaptation** (improving from experience or feedback), and reliable reasoning processes. In practice, this dimension is about the **cognitive algorithms and models** powering agents – usually LLMs augmented with memory, tool-use abilities, and domain knowledge. For an educational agent (say a tutor bot), the intelligence dimension means it can comprehend a student’s question, plan a multi-turn teaching dialog, fetch relevant examples, and adjust its approach based on whether the student seems to understand (learning from the interaction).

* **Interaction Dimension:** This covers how agents **communicate and act** within digital environments. It includes agent-to-environment interaction (using tools, calling APIs) and agent-to-agent interaction (coordination protocols). To enable rich interactions, agents rely on standardized **semantic protocols** to exchange messages or call functions. In the Agentic Web, web services expose not just web pages for humans, but also **APIs and schemas that agents can utilize**. Agents may converse in natural language (for flexibility) but structured protocols ensure mutual understanding. We’ll explore specific protocols (MCP, A2A) in the next section. For developers, the interaction dimension means designing your services and agents to speak a common language. For example, an educational content repository might implement an API that agents (from any vendor) can query for articles, or two tutoring agents might negotiate a teaching strategy via a defined message format. Without common standards here, we’d end up with isolated “agent silos.” Indeed, researchers stress that **standardized communication** is imperative to avoid a fragmented, incompatible agent ecosystem.

* **Economic Dimension:** This dimension is about **value creation, exchange, and incentives** in an agent-driven ecosystem. When agents can autonomously produce and consume content or services, they effectively become economic actors. They might generate data or solutions that are **consumed by other agents** rather than directly by humans. This opens up the idea of **machine-to-machine markets** and an “Agent Attention Economy” where agents trade resources or services (possibly using digital credits or micropayments). For instance, an educational curriculum-building agent might pay another specialized agent for access to a premium dataset or for proofreading its generated content – all automatically. The economic dimension also involves **governance**: ensuring fair and trustworthy agent behavior when they make decisions with financial or ethical stakes. As a developer, this means thinking about how your agent can **access services sustainably** (will it need an API key or to pay per use?), how to prevent abuse if agents consume each other’s outputs, and how to incorporate human oversight in high-stakes decisions (like spending real money or grading students). This area is still emerging, but concepts like decentralized identity and secure value exchange between agents are being explored to support a healthy agent economy.

The interplay of these three foundations can be summarized as: **Intelligence** gives agents the brains, **Interaction** gives them the common language and tools to operate, and **Economics** provides the rules and incentives of the ecosystem. All three are essential for an open Agentic Web. For example, if you have intelligent agents but no interaction protocol, they can’t work together; if they interact but have no economic model, you might have resource contention or no motivation for agents to share services, etc. As we implement educational agents, we must ensure they are smart enough (domain knowledge, reasoning), speak the right protocols, and adhere to usage policies or incentives (perhaps ensuring academic integrity, proper credit to content creators, etc.).