Total Recall: Agent Memory and Harness Engineering — Ignacio Martinez, Oracle
Treat memory, context control, concurrency, and model routing as harness concerns. The workshop shows how these layers can improve agent behavior without changing model weights.
The workshop separates the model’s fixed reasoning core from the harness around it. It covers **agent memory**, combining files with databases, using worktrees when many agents edit concurrently, and routing tasks between models.
Builders should keep working context deliberately small, persist reusable knowledge outside the conversation, and turn effective sessions into repeatable workflows. For parallel coding agents, isolate file changes rather than expecting shared files to provide transactional consistency.
The workshop separates the model’s fixed reasoning core from the harness around it. It covers **agent memory**, combining files with databases, using worktrees when many agents edit concurrently, and routing tasks between models. Builders should keep working context deliberately small, persist reusable knowledge outside the conversation, and turn effective sessions into repeatable workflows. For parallel coding agents, isolate file changes rather than expecting shared files to provide transactional consistency. The session presents these as practical design patterns, not measured performance results. Claims about context degradation and a future **mixture of small experts** are architectural guidance; the material provides no benchmark against simpler harnesses.
This consolidates small-context operation, external memory, model routing, reusable workflows, and isolated worktrees into one practical harness boundary. The candidates both sharpen and qualify that guidance: fresh-context loops and existing plugins show concrete implementations, while comparative evidence and model-upgrade experience warn that summarization, planning, recovery, and other scaffolding must be selected per model and budget rather than treated as universally beneficial.