ai · 1 min read
Agent Memory Architectures: Short-Term, Episodic and Semantic
Memory is what turns a stateless LLM into a useful colleague. Here is the three-tier architecture Bhogar AI uses in production and the trade-offs of each tier.
BABhogar AI TeamProduct & Engineering
The single biggest predictor of "did the agent feel useful" in our customer interviews is memory quality. Memory is the difference between a polished demo and a teammate who remembers your name, your preferences and last quarter's decisions.
Why it matters
Naively dumping everything into the context window does not scale and quickly burns budget. Naively storing everything in a vector database leads to recall of irrelevant trivia. The solution is tiered memory aligned to how humans actually remember.
How Bhogar AI approaches it
Bhogar AI implements three tiers: short-term (live conversation buffer), episodic (summarised interactions with importance scores) and semantic (extracted facts in a vector store). A lightweight memory manager decides what to consolidate, when to forget, and what to surface for any given turn.
- Importance scoring so trivia ages out and decisions persist
- PII redaction before any memory is written
- Per-user memory partitioning enforced at the storage layer
- Cross-session retrieval via the Bhogar AI Knowledge Base
- Memory deletion APIs for GDPR right-to-be-forgotten
What you get
Production agents using tiered memory show a 40% lift in user-reported helpfulness scores and a 22% reduction in tokens-per-conversation versus full-context approaches.