Policy-Governed Context Slicing for Autonomous Agent Systems: A Lightweight Knowledge Graph Approach

Autonomous AI agent systems face a fundamental challenge: constructing reproducible, trustworthy context windows from large conversational histories while enforcing governance policies over what information may influence downstream decisions. We present the \textbf{Graph Kernel}, a deterministic context slicing engine implemented as a single Rust binary (${\sim}15$ KLOC) that combines a lightweight knowledge graph triple store with cryptographically-signed, policy-governed context window construction. Unlike general-purpose graph databases or retrieval-augmented generation (RAG) pipelines, the Graph Kernel introduces the concept of a \textbf{provenance engine}---a system whose primary purpose is not information retrieval but the production of verifiable, reproducible evidence bundles for autonomous agent reasoning. We evaluate the Graph Kernel across 27 queries spanning five categories (factual recall, relationship mapping, multi-hop reasoning, fuzzy/semantic search, and predicate-specific queries) against three baseline methods: keyword search, BM25, and vector-similarity RAG. Results demonstrate that the Graph Kernel achieves perfect relevance (1.00) on multi-hop traversal queries---returning structurally connected knowledge chains rather than keyword-coincidence result sets---while maintaining sub-300ms average latency over a remote PostgreSQL backend. We further present a comparative analysis against nine industry-grade alternatives (Neo4j, Amazon Neptune, Apache Jena, Dgraph, TypeDB, Weaviate, LangChain/LlamaIndex Knowledge Graphs, Microsoft GraphRAG, and Zep), establishing that no existing system provides the combination of HMAC-signed deterministic context windows, policy-governed access control, and multi-hop provenance tracking that the Graph Kernel offers. Our key contributions are: (1)~a formal model for HMAC-signed deterministic context windows with type-level enforcement of admissibility invariants; (2)~a policy governance framework for phase-weighted, budget-bounded context expansion; (3)~multi-hop provenance at sub-300ms latency with projected sub-30ms under local deployment; and (4)~a hybrid architecture positioning that bridges structural graph reasoning with semantic vector search.