Context Engineering for Agentic AI: 2026 BARC Study
Most agentic AI pilots look flawless in the demo, then confuse claim statuses, misclassify scans, or cite outdated policy the moment they hit production. Data, AI, and IT leaders are tracing these failures to a missing discipline: context engineering. This report from BARC, sponsored by DataHub, shows what separates organizations that scale agentic AI safely from those stuck re-running pilots.
What you’ll learn
- Why accuracy, reliability, and consistency of agentic AI are the top reasons data, AI, and IT leaders cite for investing in context engineering
- The seven must-have characteristics of a context engineering program
- The architectural elements of context engineering, and the security and governance programs that provide the necessary guardrails
- What separates “context leaders,” the 42% of organizations with a mature technical foundation, from everyone else, and why they’re four times more likely to also lead in overall AI maturity
- A six-step value cycle for scaling context engineering from a first use case to enterprise scale, with governance and security built in at every stage
Download the Report
Get the full BARC report for the architecture, adoption data, and case studies behind context engineering, including why the 42% of organizations that qualify as context leaders are four times more likely to lead in overall AI maturity.
