Agents cannot reason effectively on raw tables alone. To support retrieval, context, and relationship reasoning, platforms increasingly need vectors, graphs, and rich metadata.

1. Vectors and graphs for reasoning

As organizations move toward agentic AI, vectors and graphs become essential architectural components. Modern AI requires shifting from storage built for analytics toward storage built for knowledge: relationships, similarity, and continuous learning.

Why vectors matter

Vectors store information by similarity, enabling semantic search and meaning-based retrieval. Traditional SQL excels at rigid analytical storage, but it is not designed for fluid reasoning.

In practice, even structured data will often be vectorized so agents can use it effectively.

Why graphs matter

Graphs provide connected knowledge: explicit relationships between entities.

• They enable agents to navigate context and interpret meaning.
• They become more powerful when combined with vectors: vectors capture similarity, graphs capture structure.

A hybrid future

Hybrid architectures will persist.

• SQL and lakehouses remain important for analytics and reporting.
• Vectors and graphs increasingly become essential for reasoning and agent workflows.

2. Context and metadata for AI

Agents need more than data access. They need meaning: business definitions, rules, relationships, ownership, and quality.

Why agents need context