Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 18·5 min read
Building Enterprise Agent Systems that People can Trust, Verify and Improve
TL;DR
An enterprise agent system was developed for a $100M+ company, focusing on trust, verification, and improvement. The key outcome is a framework that enhances user confidence and system reliability.
✦ Why It Matters
Implement user feedback mechanisms in your agent systems to enhance trust and reliability today.
Key Takeaways
- Design agent systems tailored to specific business workflows.
- Use deterministic code for clear rules and agent systems for complex judgments.
- Incorporate human feedback at critical decision points.
- Ensure outputs are easy to verify to build trust.
How It Works
The agent system operates by continuously monitoring service tickets for each client, summarizing them to identify recurring issues. It uses semantic similarity to group tickets that may represent the same underlying problem, and employs an investigation agent to determine if these patterns are significant enough to alert account managers.