# Next Steps

Congratulations on completing the A2A Python SDK Tutorial! You've learned how to:

- Set up your environment for A2A development.
- Define Agent Skills and Agent Cards using the SDK's types.
- Implement a basic HelloWorld A2A server and client.
- Understand and implement streaming capabilities.
- Integrate a more complex agent using LangGraph, demonstrating task state management and tool use.

You now have a solid foundation for building and integrating your own A2A-compliant agents.

## Where to Go From Here?

Here are some ideas and resources to continue your A2A journey:

- **Explore Other Examples:**
    - Check out the other examples in the `a2a-python-sdk/examples/` directory in the [A2A GitHub repository](https://github.com/google/A2A/tree/main/a2a-python-sdk/examples) for more complex agent integrations and features.
    - The main A2A repository also has [samples for other languages and frameworks](https://github.com/google/A2A/tree/main/samples).
- **Deepen Your Protocol Understanding:**
    - 📚 Read the complete [A2A Protocol Documentation site](https://google.github.io/A2A/) for a comprehensive overview.
    - 📝 Review the detailed [A2A Protocol Specification](../../specification.md) to understand the nuances of all data structures and RPC methods.
- **Review Key A2A Topics:**
    - [A2A and MCP](../../topics/a2a-and-mcp.md): Understand how A2A complements the Model Context Protocol for tool usage.
    - [Enterprise-Ready Features](../../topics/enterprise-ready.md): Learn about security, observability, and other enterprise considerations.
    - [Streaming & Asynchronous Operations](../../topics/streaming-and-async.md): Get more details on SSE and push notifications.
    - [Agent Discovery](../../topics/agent-discovery.md): Explore different ways agents can find each other.
- **Build Your Own Agent:**
    - Try creating a new A2A agent using your favorite Python agent framework (like LangChain, CrewAI, AutoGen, Semantic Kernel, or a custom solution).
    - Implement the `a2a.server.AgentExecutor` interface to bridge your agent's logic with the A2A protocol.
    - Think about what unique skills your agent could offer and how its Agent Card would represent them.
- **Experiment with Advanced Features:**
    - Implement robust task management with a persistent `TaskStore` if your agent handles long-running or multi-session tasks.
    - Explore implementing push notifications if your agent's tasks are very long-lived.
    - Consider more complex input and output modalities (e.g., handling file uploads/downloads, or structured data via `DataPart`).
- **Contribute to the A2A Community:**
    - Join the discussions on the [A2A GitHub Discussions page](https://github.com/google/A2A/discussions).
    - Report issues or suggest improvements via [GitHub Issues](https://github.com/google/A2A/issues).
    - Consider contributing code, examples, or documentation. See the [CONTRIBUTING.md](https://github.com/google/A2A/blob/main/CONTRIBUTING.md) guide.

The A2A protocol aims to foster an ecosystem of interoperable AI agents. By building and sharing A2A-compliant agents, you can be a part of this exciting development!
