Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Sep 3·5 min read
How to Solve the Right Problem in the Age of Agentic AI
TL;DR
A practical framework is introduced to help software engineers and AI researchers identify and solve the right problems before deploying agentic AI systems. This approach aims to reduce uncertainty and enhance implementation effectiveness.
✦ Why It Matters
Start using stakeholder interviews to clarify project objectives and reduce uncertainty in your AI implementations today.
Key Takeaways
- Document business prerequisites to ensure alignment on goals.
- Assess IT prerequisites to understand existing system constraints.
- Define functional requirements to clarify what the solution must do.
- Establish governance to clarify decision-making roles and responsibilities.
How It Works
The framework systematically reduces uncertainty by addressing one decision domain at a time, producing documents that clarify business goals, technical feasibility, and functional requirements. Each step builds on the previous one, ensuring that all stakeholders have a shared understanding of the project before implementation begins.