Context

An agent’s usefulness is bounded by what it knows about your business, and it knows nothing by default. General knowledge is not the point here: a model that can discuss thermodynamics but cannot say what your return policy is has no place in your operation.

Context is the machinery that closes that gap.

Your sources, not general knowledge

Answers are drawn from documents you supplied and records the agent looked up. Where those are silent, the agent says so rather than reasoning its way to something plausible.

That constraint is the single most important design decision in the whole system. A plausible answer about your refund policy is worse than no answer, because the customer acts on it.

Thin documentation makes a thin agent

This is the uncomfortable part and it comes up in almost every engagement. If your documentation is vague, contradictory or absent, the agent inherits all three properties.

The fastest route to a better agent is usually to fix the source rather than to change the model, which means writing before buying. That is covered under QAIYU Docs, and the gap list it produces is worth having even if no agent follows.

Sources are cited

Where an answer comes from a document, the agent can say which one. That turns a claim into something checkable, and it is what lets a person verify an answer in seconds rather than hunting for the source.

What the agent produces becomes context in turn

Every resolved case, every escalation, every question the agent could not answer becomes material the next iteration learns from. That is described under agentic data, and it is where the compounding value sits: an agent that has run for six months knows things about your operation that were never written down anywhere. How people see and correct that as it happens is under interfaces.