Updated documentation on functional agents (#881)

Updated documentation on functional agents

---

#### Type of the changes
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [x] Documentation update
- [ ] Tests improvement
- [ ] Refactoring

#### Checklist
- [ ] The pull request has a description of the proposed change
- [ ] I read the [Contributing
Guidelines](https://github.com/JetBrains/koog/blob/main/CONTRIBUTING.md)
before opening the pull request
- [ ] The pull request uses **`develop`** as the base branch
- [ ] Tests for the changes have been added
- [ ] All new and existing tests passed

##### Additional steps for pull requests adding a new feature
- [ ] An issue describing the proposed change exists
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- [ ] Docs have been added / updated

Author: innateteniuk

docs/docs/act-ai-agent.md

@@ -3,6 +3,9 @@
 In addition to single-run agents, the `AIAgent` class lets you build agents that handle complex workflows by defining 
 custom strategies, tools, configurations, and custom input/output types.
 
+!!! tip
+    If you are new to Koog and want to create the simplest agent, start with [Single-run agents](single-run-agents.md).
+
 The process of creating and configuring such an agent typically includes the following steps:
 
 1. Provide a prompt executor to communicate with the LLM.
@@ -20,7 +23,7 @@ The process of creating and configuring such an agent typically includes the fol
     Use environment variables or a secure configuration management system to store your API keys.
     Avoid hardcoding API keys directly in your source code.
 
-## Creating a single-run agent
+## Creating a complex workflow agent
 
 ### 1. Add dependencies
 
@@ -39,7 +42,7 @@ For all available installation methods, see [Installation](index.md#installation
 Prompt executors manage and run prompts.
 You can choose a prompt executor based on the LLM provider you plan to use.
 Also, you can create a custom prompt executor using one of the available LLM clients.
-To learn more, see [Prompt executors](prompt-api.md#prompt-executors).
+To learn more, see [Prompt executors](prompt-api.md#running-prompts-with-prompt-executors).
 
 For example, to provide the OpenAI prompt executor, you need to call the `simpleOpenAIExecutor` function and provide it with the API key required for authentication with the OpenAI service:
 
@@ -115,6 +118,7 @@ val processNode by node<InputType, OutputType> { input ->
 }
 ```
 <!--- KNIT example-complex-workflow-agents-04.kt -->
+
 !!! tip
     There are also pre-defined nodes that you can use in your agent strategy. To learn more, see [Predefined nodes and components](nodes-and-components.md).
 
@@ -165,6 +169,7 @@ edge(sourceNode forwardTo targetNode transformed { output ->
 edge(sourceNode forwardTo targetNode onCondition { it.isNotEmpty() } transformed { it.uppercase() })
 ```
 <!--- KNIT example-complex-workflow-agents-05.kt -->
+
 #### 3.2. Implement the strategy
 
 To implement the agent strategy, call the `strategy` function and define nodes and edges. For example:
@@ -391,7 +396,7 @@ fun main() {
 
 ## Working with structured data
 
-The `AIAgent` can process structured data from LLM outputs. For more details, see [Structured data processing](structured-data.md).
+The `AIAgent` can process structured data from LLM outputs. For more details, see [Structured data processing](structured-output.md).
 
 ## Using parallel tool calls