AI agent costs are driven not just by inference but by the token-heavy setup work agents do before reasoning begins — schema resolution, joins, deduplication, and reconciliation. A live context graph addresses this by pre-joining and maintaining real-time data products that agents can query directly, eliminating redundant discovery and assembly work. This reduces token usage, lowers API call volume, cuts egress costs, and enables use of smaller, cheaper LLMs for routine tasks. The approach follows an evolved data mesh pattern where each data product models a core business entity and is kept consistent in real time. Teams are advised to start with a single use case, building out reusable data products incrementally so each new agent inherits prior context engineering work.
