New Agents:

H2OMLAgent(): The first in a series of ML agents designed to make Machine Learning Models with AI. This AI Agent is trained in h2o AutoML and is capable of creating 100's of ML models in seconds.
New Example: https://github.com/business-science/ai-data-science-team/blob/master/examples/ml_agents/h2o_machine_learning_agent.ipynb

Improvements

Workflow Summary Report: The explain code step was replaced with a much faster step for documenting the agentic workflow. A get_workflow_summary() method returns formatted summary reports of every step taken in the agentic workflow.
Smart Schema Pruning: SQL Database Agent gained a new parameter, smart_schema_pruning, which uses an extra LLM call to prune tables and columns. This is useful when database schemas are very large. Pruning is based on Uber QueryGPT blog article which implements a Column Prune Agent. Read more here: https://www.uber.com/blog/query-gpt/

Full Changelog: 0.0.0.9008...0.0.0.9009