Split the tool-call entry into standalone vs. durable variants and highlighted the new runner-focused learning goals.

Signed-off-by: Roberto Rodriguez <9653181+Cyb3rWard0g@users.noreply.github.com>

Author: Cyb3rWard0g

daprdocs/content/en/developing-applications/dapr-agents/dapr-agents-quickstarts.md

@@ -17,10 +17,10 @@ description: "Get started with Dapr Agents through practical step-by-step exampl
 
 | Scenario                                                                                                                                                                                                                                                                           | What You'll Learn |
 |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------|
-| [Hello World](https://github.com/dapr/dapr-agents/tree/main/quickstarts/01-hello-world)<br>A rapid introduction that demonstrates core Dapr Agents concepts through simple, practical examples.                                                                                    | - **Basic LLM Usage**: Simple text generation with OpenAI models <br> - **Creating Agents**: Building agents with custom tools in under 20 lines of code <br> <br> - **Simple Workflows**: Setting up multi-step LLM processes |
+| [Hello World](https://github.com/dapr/dapr-agents/tree/main/quickstarts/01-hello-world)<br>A rapid introduction that demonstrates core Dapr Agents concepts through simple, practical examples.                                                                                    | - **Basic LLM Usage**: Simple text generation with OpenAI models <br> - **Creating Agents**: Building agents with custom tools in under 20 lines of code <br> <br> - **Simple Workflows**: Setting up multi-step LLM processes <br> - **DurableAgent Hosting**: Learn `AgentRunner.run`, `AgentRunner.subscribe`, and `AgentRunner.serve` using the `03_durable_agent_*.py` samples |
 | [LLM Call with Dapr Chat Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_dapr)<br>Explore interaction with Language Models through Dapr Agents' `DaprChatClient`, featuring basic text generation with plain text prompts and templates.             | - **Text Completion**: Generating responses to prompts <br> - **Swapping LLM providers**: Switching LLM backends without application code change <br> - **Resilience**: Setting timeout, retry and circuit-breaking <br> - **PII Obfuscation**: Automatically detect and mask sensitive user information |
 | [LLM Call with OpenAI Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_open_ai)<br>Leverage native LLM client libraries with Dapr Agents using the OpenAI Client for chat completion, audio processing, and embeddings.                               | - **Text Completion**: Generating responses to prompts <br> - **Structured Outputs**: Converting LLM responses to Pydantic objects <br><br> *Note: Other quickstarts for specific clients are available for [Elevenlabs](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_elevenlabs), [Hugging Face](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_hugging_face), and [Nvidia](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_nvidia).* |
-| [Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-agent-tool-call)<br>Build your first AI agent with custom tools by creating a practical weather assistant that fetches information and performs actions.                                            | - **Tool Definition**: Creating reusable tools with the `@tool` decorator <br> - **Agent Configuration**: Setting up agents with roles, goals, and tools <br> - **Function Calling**: Enabling LLMs to execute Python functions |
+| Standalone & Durable Agents <br> [Standalone Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-standalone-agent-tool-call) · [Durable Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-durable-agent-tool-call) | - **Standalone Agents**: Build conversational agents with tools in under 20 lines using the `Agent` class <br> - **Durable Agents**: Upgrade to workflow-backed `DurableAgent` instances with `AgentRunner.run/subscribe/serve` <br> - **Tool Definition**: Reuse tools with the `@tool` decorator and structured args models <br> - **Function Calling**: Let LLMs invoke Python functions safely |
 | [Agentic Workflow](https://github.com/dapr/dapr-agents/tree/main/quickstarts/04-llm-based-workflows)<br>Dive into stateful workflows with Dapr Agents by orchestrating sequential and parallel tasks through powerful workflow capabilities.                                          | - **LLM-powered Tasks**: Using language models in workflows <br> - **Task Chaining**: Creating resilient multi-step processes executing in sequence <br> - **Fan-out/Fan-in**: Executing activities in parallel; then synchronizing these activities until all preceding activities have completed |
 | [Multi-Agent Workflows](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflows)<br>Explore advanced event-driven workflows featuring a Lord of the Rings themed multi-agent system where autonomous agents collaborate to solve problems. | - **Multi-agent Systems**: Creating a network of specialized agents <br> - **Event-driven Architecture**: Implementing pub/sub messaging between agents <br> - **Workflow Orchestration**: Coordinating agents through different selection strategies|
 | [Multi-Agent Workflow on Kubernetes](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflow-k8s)<br>Run multi-agent workflows in Kubernetes, demonstrating deployment and orchestration of event-driven agent systems in a containerized environment.   | - **Kubernetes Deployment**: Running agents on Kubernetes <br> - **Container Orchestration**: Managing agent lifecycles with K8s <br> - **Service Communication**: Inter-agent communication in K8s |