OMNE-Engram: A Programmable Knowledge Substrate for Agents with Governed Schema Evolution

Long-horizon agents need knowledge representations that evolve with their domains, tasks and information. Retrieval-augmented generation and memory systems often couple representation and retrieval within application-specific pipelines, leaving each application to coordinate structural revisions, validation and query support. We present OMNE-Engram, a programmable knowledge substrate whose domain schemas and governing contracts (covenants) configure reusable write validation, declaration activation and query admission. We establish its representation capacity through theory and experiments: finite typed relational structures admit lossless encoding under stated assumptions, and reconstructed structures and predefined query results match three heterogeneous sources exactly. Fixed-domain applications approach the strongest baselines across five knowledge-QA and memory benchmarks. Agents driven by three different LLMs further design schemas and covenants and revise them through system checks. Together, these results support OMNE-Engram as a reusable foundation for knowledge applications and governed, agent-driven knowledge construction and evolution.