Three Layers of Agentic AI Memory
Architect agent memory as three integrated concentric layers — Short-Term Memory (outer), Long-Term Memory (middle), Feedback Loops (core) — operating together as a unit rather than as separable optional components.
Problem
Treating the three memory concerns as independent leads to silos: the STM forgets what LTM stored; the LTM never gets refined by feedback; feedback loops don't update either memory cleanly. Bornet's onion model insists they're one architecture, not three add-ons.
Solution
Three coordinated layers. STM: bounded session context, attention mechanisms, token management. LTM: persistent, structured, indexed (typically vector or graph). Feedback Loops: ingest explicit (corrections, ratings) and implicit (engagement, errors) signals to refine both STM and LTM over time. Define promotion rules (when STM content gets written to LTM) and refinement triggers. Pair with short-term-memory, episodic-memory, semantic-memory, procedural-memory, memory-type-storage-specialization, agentic-memory.
When to use
- Agent persists across sessions and benefits from learning.
- Engineering team can build and maintain all three layers.
- Feedback signal is collectible.
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