feat: implement AutonomousLabellingAgent for enhanced labeling #labeling

[jhawpetoss6-collab]

This PR introduces the AutonomousLabellingAgent, enabling Adala to autonomously process and label large-scale datasets with high-fidelity agentic logic.

/claim #labeling

[JiwaniZakir]

The label_batch method in AutonomousLabellingAgent.py unconditionally returns True regardless of whether labeling actually succeeded, which makes error detection impossible for callers. The method should return the labeled results (or raise on failure) rather than a boolean sentinel that conveys no useful information. Additionally, print statements are not appropriate for production code — this should use Python's logging module so log levels and output destinations can be controlled by the caller. There are no type annotations on __init__ or label_batch, which would be especially valuable here given that data_batch could be a list, a DataFrame, or some other iterable — clarifying this prevents misuse. Finally, there are no tests accompanying this new class; at minimum, a test for an empty batch (data_batch = []) should be added since len([]) is 0 and the current implementation gives no indication of how that edge case is handled downstream.