What problem does context engineering solve?

Context engineering prevents enterprise AI agents from making decisions based on incomplete, outdated, or conflicting information by assembling the exact runtime context needed for each step, including relevant data sources, semantic consistency, and access controls.

What does a good runtime context look like for enterprise AI agents?

A small decision packet that includes the facts that matter, governing rules, permissions, and links back to source data, enabling AI agents to make accurate and auditable decisions in production.

How do you keep runtime context from drifting over time?

Version sources, monitor retrieval for known checks, watch for upstream changes, and maintain rollback paths so policies, schemas, and permissions remain correct and current.

Nexla Blog

Semantic Intelligence

Artificial Intelligence, Data Engineering, Data Integration, Data Products, GenAI

The Context Graph Paradox: When More Data Makes AI Agents Worse

Discover why context graphs fail at scale and how semantic structure delivers reliable runtime context for enterprise AI agents.

By Nexla Team

Artificial Intelligence, Data Engineering, Data Integration, Data Products, GenAI

Context Engineering: The Missing Discipline in Enterprise AI

Enterprise AI agents fail when the context behind their decisions is incomplete, stale, or conflicting. Context engineering ensures agents receive accurate, permission-aware runtime context for reliable decisions.