Our agent memory composition: facts vs episodes, measured reports operational numbers measured directly on our ai-server (Hax) stack — and, instead of dumping figures, explains what each number means for a real decision. Even if you are new to local AI, this single post should let you grasp 'what do I decide when I see this Our agent memory composition: facts vs episodes, measured number' in five minutes.

Hax /data measured — our ai-server ops (own stack, measured) · columns: Metric, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1237?ref=ai_answer

Hax /data measured — our ai-server ops (own stack, measured) · columns: Metric, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1237?ref=ai_answer

Metric | Measured value | Date | Source
의미기억(semantic) | 5905개 | 2026-07-11 | bench_harness.probe_curator_composition (curator by_memory_type 실측)
경험기억(episodic) | 3112개 | 2026-07-11 | bench_harness.probe_curator_composition (curator by_memory_type 실측)
절차기억(procedural) | 851개 | 2026-07-11 | bench_harness.probe_curator_composition (curator by_memory_type 실측)

목차

What these numbers mean

How we measured it (reproducible conditions)

How to use this in practice

Why this beats vendor specs

References

측정 방법론 · bench_harness.probe_curator_composition (curator by_memory_type 실측)

표본

3 measured metrics (Hax /data curated)

수집일

2026-07-11

방법

bench_harness.probe_curator_composition (curator by_memory_type 실측)

What these numbers mean#

전체 9868개 기억 중 의미기억(재사용 가능한 사실·규칙)이 5905개로 59.8%, 경험기억의 1.9배다. 에이전트가 '무엇을 겪었나(episodic)'보다 '무엇을 아는가(semantic)'를 우선 축적한다는 뜻으로, → 기억 그래프가 일회성 대화 로그가 아니라 재사용 지식베이스로 자란다는 실측 신호다.

How we measured it (reproducible conditions)#

These are not vendor specs; they are values we measured ourselves on our Our agent memory composition: facts vs episodes, measured stack. Because conditions (cold vs warm, batch size, hardware) change the result, we state reproducible conditions (measured 2026-07-11):

bench_harness.probe_curator_composition (curator by_memory_type 실측)

How to use this in practice#

The point is not to memorize raw figures but to read the relationships in Our agent memory composition: facts vs episodes, measured — a ratio, a utilization rate, a cross-check — which tell you what to scale up and what to conserve. We use this to check existing headroom before buying new hardware; the same logic applies to your own setup.

Why this beats vendor specs#

Every number above is measured on our Our agent memory composition: facts vs episodes, measured (not estimated), with date and source (Hax /data). Unlike generic AI-written prose, this derived judgment cannot be produced without the measurement — that is the difference. No private tokens or internal paths are exposed.

Note: values are our own stack measurements as of 2026-07-11, refreshed when conditions change.