Update documentation

Author: vawsgit

docs/experimental/config-loader/agent-config.md

@@ -1,8 +1,8 @@
 # Agent Configuration Loading
 
-The Agent Configuration Loader enables you to create and configure Strands Agents using Python dictionaries. This approach provides a declarative way to define agent behavior, making it easier to manage complex agent configurations, version control agent definitions, and dynamically create agents at runtime.
+The Agent Configuration Loader enables you to create and configure Strands Agents using Python dictionaries. This approach provides a programmatic way to define agent behavior, making it easier to manage complex agent configurations, build configuration management systems, and dynamically create agents at runtime.
 
-**Note**: This is an experimental feature that provides programmatic configuration loading. For file-based configuration (YAML/JSON), use the main Agent constructor's `config` parameter.
+**Note**: This is an experimental feature that provides programmatic configuration loading through dictionaries. For file-based configuration (YAML/JSON), use the main Agent constructor's `config` parameter.
 
 ## Quick Start
 

docs/experimental/config-loader/graph-config.md

@@ -1,12 +1,14 @@
 # GraphConfigLoader
 
-The GraphConfigLoader enables serialization and deserialization of Strands `Graph` instances to/from YAML configurations, supporting persistence, version control, and dynamic graph construction. This implementation allows complex multi-agent workflows to be defined declaratively and managed as configuration.
+The GraphConfigLoader enables serialization and deserialization of Strands `Graph` instances to/from dictionary configurations, supporting persistence, version control, and dynamic graph construction. This implementation allows complex multi-agent workflows to be defined programmatically and managed as configuration.
+
+**Note**: This is an experimental feature that provides programmatic graph configuration loading through dictionaries. For file-based configuration, use the main Graph constructor's `config` parameter.
 
 ## Overview
 
 The GraphConfigLoader provides functionality to:
-- Load graphs from YAML/dictionary configurations
-- Serialize existing graphs to YAML-compatible configurations
+- Load graphs from dictionary configurations
+- Serialize existing graphs to dictionary configurations
 - Support all graph elements: nodes, edges, entry points, and conditions
 - Maintain referential integrity between graph components
 - Enable dynamic graph construction from configuration
@@ -17,12 +19,51 @@ The GraphConfigLoader provides functionality to:
 ### Method 1: Using GraphConfigLoader directly
 
 ```python
-import yaml
 from strands.experimental.config_loader.graph import GraphConfigLoader
 
-# Load graph from YAML configuration
-with open('workflow.yml', 'r') as f:
-    config = yaml.safe_load(f)
+# Define graph configuration
+config = {
+    "graph_id": "research_workflow",
+    "name": "Research Team Workflow",
+    "description": "Processes research requests through specialized team members",
+    "nodes": [
+        {
+            "node_id": "classifier",
+            "type": "agent",
+            "config": {
+                "name": "classifier",
+                "model": "us.amazon.nova-lite-v1:0",
+                "system_prompt": "You are a request classifier.",
+                "tools": []
+            }
+        },
+        {
+            "node_id": "processor",
+            "type": "agent",
+            "config": {
+                "name": "processor",
+                "model": "us.amazon.nova-pro-v1:0",
+                "system_prompt": "You are a request processor.",
+                "tools": []
+            }
+        }
+    ],
+    "edges": [
+        {
+            "from_node": "classifier",
+            "to_node": "processor",
+            "condition": {
+                "type": "expression",
+                "expression": "state.results.get('classifier', {}).get('status') == 'completed'"
+            }
+        }
+    ],
+    "entry_points": ["classifier"],
+    "max_node_executions": 100,
+    "execution_timeout": 300.0,
+    "node_timeout": 30.0,
+    "reset_on_revisit": False
+}
 
 loader = GraphConfigLoader()
 graph = loader.load_graph(config)
@@ -31,12 +72,12 @@ graph = loader.load_graph(config)
 result = graph("Analyze the impact of remote work on productivity")
 ```
 
-### Method 2: Using GraphBuilder.from_config() (Recommended)
+### Method 2: Using GraphBuilder.from_config() (File-based)
 
 ```python
 from strands.multiagent import GraphBuilder
 
-# Load from YAML file and customize
+# Load from YAML file and customize (not GraphConfigLoader)
 builder = GraphBuilder.from_config('workflow.yml')
 
 # Add custom nodes or modify configuration
@@ -46,58 +87,6 @@ builder.set_max_node_executions(100)
 # Build final graph
 graph = builder.build()
 ```
-
-## Configuration Schema
-
-### Basic Structure
-
-```yaml
-# Basic graph configuration
-graph_id: "research_workflow"
-name: "Research Team Workflow"
-description: "Processes research requests through specialized team members"
-
-nodes:
-  - node_id: "classifier"
-    type: "agent"
-    config:
-      name: "classifier"
-      model: "us.amazon.nova-lite-v1:0"
-      system_prompt: "You are a request classifier."
-      tools: []
-
-  - node_id: "processor"
-    type: "agent"
-    config:
-      name: "processor"
-      model: "us.amazon.nova-pro-v1:0"
-      system_prompt: "You are a request processor."
-      tools: []
-
-edges:
-  - from_node: "classifier"
-    to_node: "processor"
-    condition:
-      type: "expression"
-      expression: "state.results.get('classifier', {}).get('status') == 'completed'"
-
-entry_points:
-  - "classifier"
-
-# Graph execution configuration
-max_node_executions: 100
-execution_timeout: 300.0
-node_timeout: 30.0
-reset_on_revisit: false
-```
-
-### Node Types
-
-#### Agent Nodes
-
-```yaml
-nodes:
-  - node_id: "data_processor"
     type: "agent"
     config:
       # Full agent configuration (as supported by AgentConfigLoader)