Docs: v0.20.5 - Added Framework prompt block documentation for the Agent component (#10006)

### What problem does this PR solve?

### Type of change

- [x] Documentation Update

docs/guides/agent/agent_component_reference/agent.mdx

@@ -18,6 +18,14 @@ An **Agent** component fine-tunes the LLM and sets its prompt. From v0.20.4 onwa
 
 An **Agent** component is essential when you need the LLM to assist with summarizing, translating, or controlling various tasks. 
 
+## Prerequisites
+
+1. Ensure you have a chat model properly configured:
+
+![Set default models](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/set_default_models.jpg)
+
+2. If your Agent involves dataset retrieval, ensure you [have properly configured your target knowledge base(s)](../../dataset/configure_knowledge_base.md).
+
 ## Configurations
 
 ### Model
@@ -57,13 +65,44 @@ Click the dropdown menu of **Model** to show the model configuration window.
 
 Typically, you use the system prompt to describe the task for the LLM, specify how it should respond, and outline other miscellaneous requirements. We do not plan to elaborate on this topic, as it can be as extensive as prompt engineering. However, please be aware that the system prompt is often used in conjunction with keys (variables), which serve as various data inputs for the LLM. 
 
-:::danger IMPORTANT
 An **Agent** component relies on keys (variables) to specify its data inputs. Its immediate upstream component is *not* necessarily its data input, and the arrows in the workflow indicate *only* the processing sequence. Keys in a **Agent** component are used in conjunction with the system prompt to specify data inputs for the LLM. Use a forward slash `/` or the **(x)** button to show the keys to use.
-:::
+
+#### Advanced usage
+
+From v0.20.5 onwards, four framework-level prompt blocks are available in the **System prompt** field. Type `/` or click **(x)** to view them; they appear under the **Framework** entry in the dropdown menu.
+
+- `task_analysis` prompt block
+  - This block is responsible for analyzing tasks — either a user task or a task assigned by the lead Agent when the **Agent** component is acting as a Sub-Agent.
+  - Reference design: [analyze_task_system.md](https://github.com/infiniflow/ragflow/blob/main/rag/prompts/analyze_task_system.md) and [analyze_task_user.md](https://github.com/infiniflow/ragflow/blob/main/rag/prompts/analyze_task_user.md)
+  - Available *only* when this **Agent** component is acting as a planner, with either tools or sub-Agents under it.
+  - Input variables:
+    - `agent_prompt`: The system prompt.
+    - `task`: The user prompt for either a lead Agent or a sub-Agent. The lead Agent's user prompt is defined by the user, while a sub-Agent's user prompt is defined by the lead Agent when delegating tasks.
+    - `tool_desc`: A description of the tools and sub_Agents that can be called.
+    - `context`: The operational context, which stores interactions between the Agent, tools, and sub-agents; initially empty.
+- `plan_generation` prompt block
+  - This block creates a plan for the **Agent** component to execute next, based on the task analysis results.
+  - Reference design: [next_step.md](https://github.com/infiniflow/ragflow/blob/main/rag/prompts/next_step.md)
+  - Available *only* when this **Agent** component is acting as a planner, with either tools or sub-Agents under it.
+  - Input variables:
+    - `task_analysis`: The analysis result of the current task.
+    - `desc`: A description of the tools or sub-Agents currently being called.
+    - `today`: The date of today.
+- `reflection` prompt block
+  - This block enables the **Agent** component to reflect, improving task accuracy and efficiency.
+  - Reference design: [reflect.md](https://github.com/infiniflow/ragflow/blob/main/rag/prompts/reflect.md)
+  - Available *only* when this **Agent** component is acting as a planner, with either tools or sub-Agents under it.
+  - Input variables:
+    - `goal`: The goal of the current task. It is the user prompt for either a lead Agent or a sub-Agent. The lead Agent's user prompt is defined by the user, while a sub-Agent's user prompt is defined by the lead Agent.
+    - `tool_calls`: The history of tool calling
+    - `call.name`：The name of the tool called.
+    - `call.result`：The result of tool calling
+- `citation_guidelines` prompt block
+  - Reference design: [citation_prompt.md](https://github.com/infiniflow/ragflow/blob/main/rag/prompts/citation_prompt.md)
 
 ### User prompt
 
-The user-defined prompt. Defaults to `sys.query`, the user query.
+The user-defined prompt. Defaults to `sys.query`, the user query. As a general rule, when using the **Agent** component as a standalone module (not as a planner), you usually need to specify the corresponding **Retrieval** component’s output variable (`formalized_content`) here as part of the input to the LLM.
 
 
 ### Tools 
@@ -100,4 +139,24 @@ Increasing this value will significantly extend your agent's response time.
 
 ### Output
 
-The global variable name for the output of the **Agent** component, which can be referenced by other components in the workflow.
+The global variable name for the output of the **Agent** component, which can be referenced by other components in the workflow.
+
+## Frequently asked questions
+
+### Why does it take so long for my Agent to respond?
+
+An Agent’s response time generally depends on two key factors: the LLM’s capabilities and the prompt, the latter reflecting task complexity. When using an Agent, you should always balance task demands with the LLM’s ability. See [How to balance task complexity with an Agent's performance and speed?](#how-to-balance-task-complexity-with-an-agents-performance-and-speed) for details.
+
+## Best practices
+
+### How to balance task complexity with an Agent’s performance and speed?
+
+- For simple tasks, such as retrieval, rewriting, formatting, or structured data extraction, use concise prompts, remove planning or reasoning instructions, enforce output length limits, and select smaller or Turbo-class models. This significantly reduces latency and cost with minimal impact on quality.
+
+- For complex tasks, like multi-step reasoning, cross-document synthesis, or tool-based workflows, maintain or enhance prompts that include planning, reflection, and verification steps.
+
+- In multi-Agent orchestration systems, delegate simple subtasks to sub-Agents using smaller, faster models, and reserve more powerful models for the lead Agent to handle complexity and uncertainty.
+
+:::tip KEY INSIGHT
+Focus on minimizing output tokens — through summarization, bullet points, or explicit length limits — as this has far greater impact on reducing latency than optimizing input size.
+:::