Why context comes before the agents
A language model, however advanced, only knows the world up to its training. It does not know your company’s documents, rules and data. Without a prepared context layer, it fills the gaps with plausible but wrong assumptions. That’s why order matters: context first, then the agent.
Before the agents comes the context →
AI-ready data: the raw material of context
Context is not just any data: it is data with a defined source, clear scope and traceability. An outdated or ambiguous document becomes noise. Preparing data for agents means deciding what the source of truth is, what stays out, and how each answer points to its origin.
What is data ready for AI agents →
How context reduces hallucination
Most hallucinations in enterprise settings don’t come from a “dumb” model, but from an agent answering without a source. When context defines where the answer must come from and requires citing the origin, hallucination drops and the answer becomes auditable.
How to reduce hallucination in enterprise agents →
RAG: the technique that delivers context
RAG (Retrieval-Augmented Generation) is the most common way to bring context to the agent at answer time: retrieve the relevant passages from the base and use them to ground the answer. It’s a piece of context engineering, not an end in itself.
What is RAG and why it matters for companies →
How Contextfy helps
Contextfy treats context as a product: we prepare sources, scope, permissions and traceability before plugging in any agent. It’s this layer that makes AI answer with confidence, and it’s where we recommend starting.