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, such as context window constraints and the loss of semantic nuance that often occurs when documents are fragmented into isolated chunks.
Map-First Architecture
The core mechanism of context provisioning is a map-first architecture, which prioritizes the structural integrity of data over simple content retrieval. By defining explicit connections between data points, the system allows agents to navigate information based on logical relationships rather than vector similarity alone. This structure ensures that the agent receives a coherent view of the domain, reducing the cognitive load required to reconstruct context from disparate pieces of retrieved text.
Overcoming RAG Limitations
Traditional RAG systems often struggle with maintaining global context and handling complex queries that require synthesizing information across multiple documents. Context provisioning mitigates these issues by providing a curated knowledge graph or structured schema that the agent can query directly. This results in more accurate responses, as the agent operates within a defined boundary of verified relationships rather than guessing relevance from unstructured text fragments.