Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
Data Science
Towards AI·26d ago·5 min read
Building CoWork Agents That Understand Business Context
TL;DR
A CoWork agent was developed to answer business questions using SQL and natural language. However, it provided incorrect data due to flawed logic in understanding business context.
✦ Why It Matters
Engineers should prioritize integrating contextual understanding in AI models to enhance accuracy in business analytics.
Key Takeaways
- Ensure AI agents understand business context to avoid data inaccuracies.
- Utilize semantic views for better data interpretation in AI applications.
- Test AI outputs rigorously against real-world business scenarios.
- Incorporate feedback loops to refine AI logic based on user queries.
Full Summary
Building a CoWork agent aimed to facilitate business analytics by connecting to relevant data tables and generating SQL queries. Initially, the agent performed well, answering questions in natural language, but it failed to accurately calculate net retention excluding trials, yielding a 12-point discrepancy.
This error stemmed from the agent's lack of understanding of business context, which is crucial for interpreting queries correctly. The development process involved integrating semantic views and utilizing tools like Cortex Analyst and Cortex Search to enhance data retrieval.
The findings underscore the importance of contextual awareness in AI systems, particularly in business applications. Engineers must ensure that AI agents are not only technically proficient but also contextually aware to avoid misleading outputs.