What I Used Hindsight's reflect and recall For (and What I Haven't Proven About Them)

The author built an incident-response agent on top of Hindsight, an agent memory system. They chose Hindsight over a vector store because they wanted the agent to reason across incidents, recognizing patterns that may not be contained in any single paragraph. The agent stores postmortem incidents, extracting facts, experiences, observations, memories, and links between them.

The integration surface of Hindsight is small, consisting of three calls: store_incident, recall_incidents, and reflect_on_incidents. The author used both reflect and recall on every request, with reflect returning a synthesized answer based on cross-incident statements, and recall providing the underlying memories to rank and cite as evidence.

They found that using Hindsight led to accurate diagnoses, such as identifying a dependency-capacity issue in a checkout service returning 500s, and warning against rolling back, as the memory flagged rollback as a trap for this failure class. In contrast, without memory, the model fabricated irrelevant errors and recommended an immediate rollback.

The author did not run a memory vs. vector search comparison or perform ablation tests on reflect and recall, as they wanted to keep the integration thin and easy to swap between memory and non-memory versions. They also did not test Hindsight against a plain vector store, as they did not build one. The author emphasizes the importance of separating synthesis from evidence and structuring the memory for the questions being asked.

They conclude by stating the need for future experiments to compare Hindsight against plain vector search.