Context Provisioning
Context provisioning is a method of supplying information to AI agents through pre-organized, structured formats rather than unprocessed document uploads. Instead of passing raw documents to an agent and relying on retrieval-augmented generation (RAG) systems to extract relevant passages during execution, context provisioning establishes explicit relationships and hierarchical organization within information before the agent encounters it. This approach addresses fundamental limitations of traditional RAG systems, which can struggle with relevance ranking, context window management, and maintaining coherent relationships across fragmented retrieved passages.
Architecture and Implementation
The core of context provisioning uses a map-first architecture, where information is organized into interconnected structures that reflect domain logic and semantic relationships. Rather than storing flat document collections, this system creates explicit connections between concepts, defines hierarchy levels, and pre-computes relevant context clusters. When an agent requires information, it receives structured context already filtered and organized for its specific task, reducing the computational overhead and ambiguity inherent in real-time retrieval and ranking operations.
Advantages Over Traditional Approaches
By provisioning context before agent execution, this method reduces hallucination risks through explicit information boundaries, improves response consistency by working with pre-validated relationships, and decreases latency by eliminating runtime retrieval overhead. Agents can maintain clearer reasoning chains when context arrives pre-organized, and the structured format enables better verification and auditing of information sources. This is particularly valuable for agents handling specialized domains where precise relationship mapping is critical to correct decision-making.