Overview
This series explains how Hindsight turns conversations and documents into scoped facts, connects them across people, projects, and time, consolidates repeated evidence into revisable observations, and chooses among recall, reflect, mental models, and knowledge pages. It follows the lifecycle from retain through retrieval, diagnosis, deployment, and repair. The central value is continuity with inspectable provenance rather than a longer transcript; the boundary is that model-assisted extraction, stale derived state, scope errors, finite retrieval, operational cost, and governance still limit what the system can safely conclude.
Learning outcomes
- In Hindsight, learning is an inspectable lifecycle: retain, connect, consolidate, retrieve, and reflect.
- Raw facts remain the evidence layer; observations and models are useful projections that need scope, provenance, and refresh.
- Recall assembles bounded evidence, while reflect synthesizes a conclusion and should fail when its evidence path is incomplete.
- Hindsight is most justified when cross-session continuity, entities, time, or repeated evidence outweigh added cost and governance.
Keywords
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PlansFAQ
What makes Hindsight different from ordinary RAG?
Hindsight adds persistent, scoped memory with entities, time, relationships, consolidation, and reflection, rather than only returning similar document chunks. It still cannot guarantee correct extraction or conclusions.
When should an agent use Hindsight instead of a simpler retrieval system?
Use Hindsight when future behavior must change across sessions and questions require identity, time, scope, or repeated evidence; static document Q&A often needs less.
What is the difference between recall and reflect in Hindsight?
Recall returns ranked memory evidence, while reflect uses retrieved memory and an LLM to synthesize a conclusion. Recall suits exact inspection; reflect suits comparison, explanation, or recommendation.
How can Hindsight's conclusions be checked?
Check the source IDs, raw facts, dates, tags, chunks, and attachment provenance behind an observation or model; derived text is a rebuildable projection, not proof by itself.