How enterprises build, structure, and govern the context AI agents need to work reliably. This includes Nexla’s Context Layer series, covering tool schema design, context freshness, identity, and evaluation, alongside broader coverage of context engineering for production AI systems.
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.
Context engineering is the systematic practice of designing and controlling the information AI models consume at runtime, ensuring outputs are accurate, auditable, and compliant.
