Total Recall: Agent Memory and Harness Engineering — Ignacio Martinez, Oracle

Building reliable AI agents requires moving beyond the frozen reasoning of large language models by implementing an "agent harness"—a structural layer comprising memory, tools, and perception. This harness transforms non-deterministic model outputs into predictable, repeatable workflows. Effective agent memory management utilizes a hybrid approach, combining the flexibility of files with the transactional consistency and vector search capabilities of databases. Techniques like context compaction and the toolbox pattern mitigate context degradation, ensuring that agents remain focused and efficient. By integrating these components into a converged database architecture, developers can streamline data synchronization and security. This framework, supported by tools like the Oracle Agent Memory Package, allows for continual learning and the promotion of successful workflows into reusable skills, ultimately reducing the cognitive load on both AI engineers and the agents themselves.