diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 919bb09..78ae0ce 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -75,4 +75,21 @@ L["agent.generate_response()"] --> M{"response == PASS?"}
 
 M -->|Yes| N["Skip turn, return skipped: True"]
 M -->|No| O["Append to history with timestamp"] --> P["Return turn_data"]
+```
 
+## Custom Model Integrations (Bring Your Own Code)
+
+If you do not want to use LiteLLM at all, the framework allows you to inject any arbitrary python script as the "brain" for an agent.
+
+1. Create a python file (e.g. `my_model.py`). 
+2. Write a function that accepts a `List[Dict[str, str]]` (the conversation history) and returns a `str` (the agent's reply).
+3. In the CLI wizard, select `custom_function` as the Model Type.
+4. Provide the path to `my_model.py` and the exact name of the function you wrote.
+
+The framework will dynamically import your file at runtime and use it exclusively for that agent's turns.
+
+## CI/CD and Robustness
+To ensure the framework remains stable as it grows, we maintain a comprehensive CI/CD pipeline using **GitHub Actions**. Every contribution is automatically tested against Python 3.13 for:
+- **Linting**: High-standard code hygiene via `flake8`.
+- **Logic Robustness**: Detailed edge-case testing including word-boundary expertise matching and non-repeating HITL triggers.
+- **Regression Testing**: Ensuring core orchestration types (Round Robin, Dynamic, Argumentative) remain deterministic.