Deploy Models on Modal/Cerebrium and Integrate with AutoGen Tools

To ensure low-latency inference for the autonomous vehicle agents, deploy your GPT-5 Pro and Claude 4 Sonnet instances (or custom fine-tuned models) on Modal and Cerebrium respectively. Then, create custom AutoGen tools that allow your `PathPlanner` and `DecisionMaker` agents to make API calls to these deployed models for their specific reasoning tasks, rather than relying solely on direct `llm_config`. ```python
# Conceptual Modal deployment client
class ModalGPT5ProClient: def plan_path(self, sensor_data: str, current_location: dict, destination: dict) -> str: # Simulate API call to Modal-deployed GPT-5 Pro endpoint return f"Path from Modal GPT-5 Pro for {sensor_data}" # Conceptual Cerebrium deployment client
class CerebriumClaude4SonnetClient: def make_tactical_decision(self, current_situation: str, path_segment: str) -> str: # Simulate API call to Cerebrium-deployed Claude 4 Sonnet endpoint return f"Decision from Cerebrium Claude 4 Sonnet for {current_situation}" # Define AutoGen tools for agents
def modal_path_planning_tool(sensor_data: str, current_location: dict, destination: dict) -> str: """Tool for PathPlanner to query Modal-deployed GPT-5 Pro for path plans.""" return ModalGPT5ProClient().plan_path(sensor_data, current_location, destination) def cerebrium_tactical_decision_tool(current_situation: str, path_segment: str) -> str: """Tool for DecisionMaker to query Cerebrium-deployed Claude 4 Sonnet for tactical decisions.""" return CerebriumClaude4SonnetClient().make_tactical_decision(current_situation, path_segment) # Register tools with agents (example for path_planner)
# path_planner.register_for_llm(name="modal_path_planning", description="...")(modal_path_planning_tool)
```