The quality of an AI agent is mostly decided before the model generates a single token — by what's in its context. Context engineering is the discipline of getting the right knowledge into the window at the right time: the relevant document chunk, the decision from last week, the fresh page from this morning. Prompts tell the model how to think; context gives it something to think about.
What a context engineering platform provides
- Ingestion: any format — PDF, Office, images (OCR), URLs including JS-rendered pages — normalized to clean Markdown.
- Retrieval: hybrid semantic + keyword search with reranking, so the right chunk wins the context budget.
- Memory: context that persists — decisions and facts saved across sessions, recalled by meaning.
- Delivery: MCP tools for agents, REST + SDK for code, chat with citations for humans.
Why "context that persists" is the hard part
Anyone can stuff a prompt once. The compounding value is context that survives: what the agent learned Monday shaping what it does Friday. That takes a store with retrieval quality, dedup, and versioning — a memory layer, not a scratchpad.
Build vs buy
The DIY stack is a parser, an OCR engine, a browser renderer, an embedder, a vector DB, a reranker, and glue — then you maintain it. Kit for AI packages the whole loop behind one account: ingest, retrieve, remember, deliver. Your engineering time goes into the agent, not its plumbing.
Engineer your agent's context instead of its infrastructure — Start free.