Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestration
TL;DR
A new architecture called GRACE was developed for goal-oriented conversational systems, achieving high accuracy in state transitions and evidence handling. It effectively balances business objectives with user engagement in real estate and cleaning service contexts.
✦ Why It Matters
Implement GRACE to enhance your conversational AI systems for better user engagement and business outcomes.
Key Takeaways
- Utilize GRACE for flexible, goal-oriented conversations.
- Implement a Business Goal Compiler for structured objectives.
- Evaluate conversational systems with defined metrics.
- Focus on user-generated evidence for response accuracy.
How It Works
GRACE operates by first compiling business goals into a set of canonical questions and objectives. During a conversation, it processes the entire history and the latest user message, using a separate RAG (Retrieval-Augmented Generation) component to generate grounded answers.
The system then selects contextually relevant follow-ups while ensuring that user utility is prioritized, thus maintaining engagement without sacrificing business goals.