## Getting Started and Further Resources

Developing for the Agentic Web is an exciting frontier, and education is a domain poised to benefit greatly from these advancements. Here are some actionable steps and resources to continue your journey:

* **Explore Open Standards Documentation:** Check out the official specs and guides for [**A2A Protocol**](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols/02_a2a) and [**Anthropic’s MCP**](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols/01_mcp) (Model Context Protocol). These will give you details on how to implement and use these protocols. 

* **Prototype with Existing Tools:** Start small by giving a single agent tool-using capabilities. For instance, using OpenAI’s function calling and make a mini agent that can fetch definitions from Wikipedia or do a simple quiz. This will get you familiar with how an agent can decide to use a tool when needed. Once comfortable, you can scale up to multi-agent – perhaps have two OpenAI agents talk to each other (there are examples in the community of agents in conversation, often called “self-play” scenarios).

* **Community and Research:** The Agentic Web is a fast-moving field. Join communities or forums like the [**A2A Community discussion**](https://github.com/a2a-org/A2A/discussions) or follow the SafeRL-Lab’s [Agentic Web GitHub](https://github.com/SafeRL-Lab/agentic-web) which curates research papers. Keeping abreast of new research will alert you to improved techniques for things like multi-agent coordination strategies, negotiation protocols, or safety approaches. For example, new research on “agent attention economy” might yield libraries for implementing economic incentives among agents, which could be interesting for educational resource sharing.

* **Testing Environment:** Set up a sandbox environment to test agent interactions. This could be as simple as a local network where you run multiple agent instances (processes or containers) that communicate via HTTP (implementing basic A2A). Or use a platform like **Hugging Face Spaces** or **Streamlit** to deploy a prototype interface for your agentic tutor and iterate with user feedback. There are also academic environments like the BEAM benchmark (if available) for multi-agent emergent behaviors, or simulation environments for things like conversational agents.

* **Measure and Iterate:** Use the evaluation methods discussed to continuously improve your agent. Start with a baseline (maybe your agent initially just answers questions with an LLM and no fancy orchestration) and measure its performance. Then add components (a tool here, an extra agent there) and see if metrics improve (did students get more answers right? did the session length drop because the agent was more efficient?). This empirical approach will guide you on where agentic design truly adds value versus where a simpler solution might suffice.


In conclusion, the Agentic Web offers a powerful vision for the next generation of educational technology: systems that are **intelligent, interactive, and interconnected** in ways previously impossible. By understanding its foundations and leveraging the emerging tools and protocols, developers can create applications that make learning more personalized, engaging, and effective. Whether it’s a tutor that feels like a personal teacher for every student, or a research assistant that brings the world’s knowledge to a learner’s fingertips, the possibilities are vast. As you embark on building these systems, remember to keep the human user’s goals and context at the center – the technology is there to augment human learning, not replace the human touch that is often vital in education.

With careful design, rigorous evaluation, and responsible implementation, agentic web applications could become invaluable partners in education – empowering learners and educators alike in this new era of AI-driven collaboration.

**Sources:** The concepts and examples in this tutorial draw upon recent research and proposals on the Agentic Web, multi-agent frameworks, and LLM agent evaluation methodologies, as cited throughout. These provide a foundation for interested readers to explore deeper. Happy building in the Agentic Web!