Context becomes infrastructure when it is maintained, permissioned, refreshed, and reused across agent work.
Most early agent setups treat context like prompt stuffing. Grab some docs, paste a summary, add a few examples, hope the model behaves. That can work for demos. It is a brittle way to run business processes.
Infrastructure has owners and operating rules. Agent context needs the same. Which facts expire? Which sources outrank others? Which context is safe for external writing? Which fields are internal-only? Which workflow state changes what the agent is allowed to infer or do?
A context layer should not be a dumping ground. It should be curated around actions. A renewal-risk agent needs account identity, contract dates, product usage, support history, owner, open obligations, and permission rules. It does not need every sales enablement page ever written. A support triage agent needs incident state and entitlement context, not a board deck.
Treating context as infrastructure also means testing it. If a source goes stale, does the agent stop, warn, or escalate? If a user lacks permission, is the context withheld or redacted? If systems disagree, does the agent know which source wins?
This is where governance becomes practical. Not a policy binder. A maintained context supply chain.
The useful question is: could ten agents use the same context rule without ten custom prompts? If not, the company does not have context infrastructure yet. It has handcrafted instructions.
This is part 2 of 10 in The AI Context Layer.
