Found docs updates needed from ADK python release v1.17.0 to v1.18.0

This issue outlines the necessary documentation updates for the ADK Python release from v1.17.0 to v1.18.0. You can find the full comparison of changes here: https://github.com/google/adk-python/compare/v1.17.0...v1.18.0

Here are the recommended documentation updates:

1. **New Feature: ADK Visual Agent Builder**
A new visual workflow designer for agent creation has been introduced. This is a major feature and requires comprehensive documentation.

**Proposed Change:**
Create a new documentation section for the "ADK Visual Agent Builder" that covers its core features, including:
- Visual workflow designer for agent creation.
- Support for multiple agent types (LLM, Sequential, Parallel, Loop, Workflow).
- Agent tool support with nested agent tools.
- Built-in and custom tool integration.
- Callback management.
- Assistant for building agents with natural language.
- Saving and testing with chat interfaces.
- Real-time building and debugging in `adk web`.

**Reasoning:**
The ADK Visual Agent Builder is a significant new feature that provides a user-friendly way to create and manage agents. Detailed documentation is essential for users to understand and utilize this new tool effectively.

2. **New Feature: `run_debug()` Helper Method**
A new `run_debug()` helper method has been added to simplify agent interaction for debugging and experimentation.

**Proposed Change:**
Add a new section to the debugging or getting started guide that explains how to use the `run_debug()` method. The documentation should include:
- An explanation of the method's purpose and benefits.
- A simple code example demonstrating its usage.
- A comparison of the code required before and after the introduction of `run_debug()`.

**Reasoning:**
The `run_debug()` method significantly simplifies the process of testing and debugging agents. Documenting this feature will help developers to write cleaner and more concise code for experimentation.

**Reference:**
`contributing/samples/runner_debug_example/main.py`

3. **New Feature: `McpInstructionProvider`**
A new `McpInstructionProvider` class has been introduced to support MCP prompts.

**Proposed Change:**
Add documentation for the `McpInstructionProvider` class, explaining how to use it to fetch agent instructions from an MCP server.

**Reasoning:**
This feature allows for more dynamic and flexible agent instruction management. Documentation is needed to explain its configuration and usage.

**Reference:**
`src/google/adk/agents/mcp_instruction_provider.py`

4. **New Feature: `ApigeeLlm` Model**
A new `ApigeeLlm` model has been added to allow ADK agents to connect with an Apigee proxy.

**Proposed Change:**
Add a new section to the models documentation for the `ApigeeLlm`. The documentation should explain how to configure and use this model to connect to an Apigee proxy.

**Reasoning:**
This feature provides a valuable integration point for enterprise users who use Apigee to manage their APIs. Clear documentation is essential for them to leverage this feature.

**Reference:**
`contributing/samples/hello_world_apigeellm/agent.py`

5. **New BigQuery Tools: `detect_anomalies` and `get_job_info`**
Two new tools have been added to the BigQuery toolset: `detect_anomalies` and `get_job_info`.

**Proposed Change:**
Update the BigQuery tools documentation to include `detect_anomalies` and `get_job_info`. Provide a description of each tool and an example of how to use it.

**Reasoning:**
These new tools expand the capabilities of the BigQuery toolset. They need to be documented so that users are aware of them and know how to use them.

**Reference:**
`src/google/adk/tools/bigquery/metadata_tool.py` and `src/google/adk/tools/bigquery/query_tool.py`

6. **New Feature: `BigQueryLoggingPlugin`**
A new `BigQueryLoggingPlugin` has been added for event logging to BigQuery.

**Proposed Change:**
Create a new documentation page for the `BigQueryLoggingPlugin` under the observability or plugins section. Explain how to configure and use this plugin to log agent events to BigQuery.

**Reasoning:**
This plugin provides a powerful way to monitor and analyze agent behavior. Documentation is needed to guide users on how to set it up.

**Reference:**
`src/google/adk/plugins/bigquery_logging_plugin.py`

7. **New Feature: LLM-Backed User Simulator**
An LLM-backed user simulator has been added to the evaluation framework.

**Proposed Change:**
Add a new section to the evaluation documentation that explains how to use the LLM-backed user simulator for testing agents.

**Reasoning:**
This new feature provides a more realistic and dynamic way to test agent performance. Documentation is needed to explain its usage and benefits.

**Reference:**
`src/google/adk/evaluation/llm_backed_user_simulator.py`

8. **New Feature: Vertex AI Express Mode**
Support for Vertex AI Express Mode has been added.

**Proposed Change:**
Update the Vertex AI documentation to include instructions on how to use Express Mode with ADK.

**Reasoning:**
This feature simplifies the setup and deployment of ADK agents on Vertex AI. It should be documented to make users aware of this option.

**Reference:**
`src/google/adk/sessions/vertex_ai_session_service.py`

9. **Update: LiteLLM Fallback Models**
The `LiteLlm` model now supports a fallback mechanism.

**Proposed Change:**
Update the `agents/models.md` and `tutorials/agent-team.md` files to include information about the new fallback mechanism for `LiteLlm`. Provide an example of how to configure fallback models.

**Reasoning:**
This is an important feature for improving the resilience of agents. It needs to be documented so that users can take advantage of it.

**Reference:**
`contributing/samples/litellm_with_fallback_models/agent.py`