Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 1·5 min read
Put the Agent Inside the Workflow
TL;DR
A hybrid application pattern integrates predefined workflows with adaptive agent behavior, enhancing the flexibility and responsiveness of large language models (LLMs) in real-world tasks.
✦ Why It Matters
Consider implementing hybrid LLM workflows to enhance adaptability in your AI applications today.
Key Takeaways
- Use structured LLM calls for known operations.
- Employ agents for stages needing adaptive exploration.
- Decompose tasks into clear stages for better control.
- Test the hybrid model on real datasets for validation.
How It Works
The hybrid model combines structured workflows for predictable tasks with agent-based methods for stages requiring flexibility. In the hyperparameter tuning stage, an agent dynamically selects configurations based on prior results, allowing for a more effective exploration of model performance.