# Examples: branching-agent-pattern

## Usage Example

```python
# How to use this skill
# Inputs: CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field, LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml, Storage backend configuration in agentmap_config_storage.yaml
# Process: Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure) → Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary → Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
# Outputs: Executed workflow with results stored in the specified output_field, Detailed execution trace showing success/failure decisions at each branching point, Updated workflow state persisted in the configured storage backend
```