Agentic AI works good only if your data foundations are strong enough. Before jumping into agents, prompts, and orchestration, it helps to pause and look at the basics. Most challenges with agentic systems do not come from models or tools, but from how data is assessed, structured, governed, and trusted. These five fundamentals must be done right before anything else

1. Thorough Data Assessment (Day 0 Matters)

Strong agentic systems start with early and deep data assessment. Bringing data engineers, data architects, and data analysts or business analysts into the discovery phase sets the direction for everything that follows. This early involvement shapes data pipelines, semantic models, BI reports, and eventually how AI agents reason and act.

Why role-wise assessment matters:

Data Analysts and Business Analysts
Their focus is on value creation. To operationalize agentic AI, they need a clear understanding of business workflows and how data supports decision-making. They help identify relevant data sources and tables, relationships and dependencies, high-impact attributes, metadata for unstructured data, and alignment between data and business problem statements.

Data Engineers
They turn business flows into technical reality. This includes data profiling and quality checks, defining transformation logic, fixing data issues, building consumption-ready data models, and working closely with analysts to co-create the semantic layer used by BI tools and AI agents.

Data Architects
While engineers focus on implementation, architects focus on sustainability. They ensure SLA adherence, cost optimization, and performance at scale. This involves evaluating data volume, velocity, growth patterns, cardinality, refresh frequency, and consumption needs across different time horizons such as hourly, daily, or monthly.