Context, Loops, and Graphs

The vocabulary of agentic AI has turned over five times in about eighteen months — prompt engineering, context engineering, harness engineering, loop engineering, and now graph engineering. For a business leader, the reasonable reaction is to tune it out.

Chris Bradley argues the churn is hiding something worth understanding. Those terms don't replace each other; they stack. And each one names a layer of the same discovery: that the model is not what separates an AI program that works from one that doesn't.

He starts with the picture that makes the stack obvious — the brilliant new hire seated at a desk with no system logins, asked to process an order. From there he walks the context layer and its two halves, access and knowledge, and why grounded context is the highest-leverage way to reduce the verification burden that comes with confident, fluent, wrong output. Then the harness — the runnable environment around the model, and the reason Veritiv built its own for order processing across multiple ERPs and heavily customized systems, deliberately model-agnostic. Then the loop, where reliability actually comes from: what the agent checks its work against, what happens on failure, and when it stops. And finally the graph — coordinating many agents, which turns out to be an org design problem with familiar failure modes.

He closes with an escalation ladder for choosing the layer, a caution against re-platforming every time the field renames something, and the principle that holds as these systems get more elaborate: work distributes across nodes, accountability does not.