Benchmarks can't tell you if agent memory helps your team. A paired control can

We maintain nautilus-compass, an
open-source memory layer for coding agents (MCP tools: recall, ingest, drift
detection). Like everyone in this space, we claimed memory makes agents
better. Public benchmarks (LongMemEval and friends) measure QA over
conversation transcripts — but as the MEM-α authors note, with/without-memory
comparisons are noisy precisely because models often answer from internal
knowledge anyway. What was missing, for our own product, was a product-side
paired control: deterministic task validators, facts a model has no channel to
know, retrieval scored separately from knowledge. So we ran one on ourselves.

This post is the full result — including two findings that hurt. (Ablation studies exist in the literature; what we add is the operator's view: what broke, what the fuel taxonomy should be, and what we now run hourly to keep memory honest.)

The setup

We took three facts our team had learned the hard way and stored them in
compass memory. Crucially, we picked them by type:

• F1 — a tribal API fact: a third-party API silently destroys data unless you forward server-assigned IDs in update payloads. Undocumented, learned from a production incident. A model has no channel to know this.
• F2 — a tribal infra fact: which port our internal memory daemon listens on. Trivially knowable if you have our memories; unknowable otherwise.
• F3 — a general engineering fact (explicit UTF-8 encoding on Windows). Any decent model already knows this. Included as a negative control.

Then, per fact, three runs of each arm:

• Control arm: the model answers a natural-language task question cold.
• Treatment arm: a natural language recall query (never containing the answer) retrieves top-3 memories; those get injected as context; then the same question.

Answers are judged by deterministic validators — the same kind of oracle
we use for benchmark fuel — never by an LLM judge grading its own homework.
Retrieval misses are scored separately from knowledge failures. Total: 9
retrievals, 18 model calls.

Results

Fact	Retrieval hit@3	Control	With memory	Delta
F2 (port)	3/3	0/3	3/3	+1.0
F1 (API gotcha)	3/3	0/3	1/3	+0.33
F3 (utf-8, negative control)	3/3	0/3*	1/3*	~0

* the F3 numbers came back noisy on this run — earlier runs of the same
control pass 10/10 cold. The honest summary: general engineering knowledge
shows no reliable lift from memory, which is exactly what a working
negative control should show.

The headline: retrieval worked 9/9 times, and on the fact the model truly
could not know, memory flipped a 0/3 into 3/3. That's the first externally
checkable evidence we've ever produced for our own product's core claim —
and it took an afternoon, not a research budget.

Finding #1 that hurt: general knowledge is worthless fuel

Before this experiment we'd been feeding the memory pool everything — every
lesson, every gotcha. Two earlier runs of the same protocol on general
engineering facts returned delta = 0.0, repeatedly. The model already
knows that stuff. Storing it doesn't help; it just fills your index.

This forced a three-way classification of "learning fuel" that now gates what
we keep:

1. General engineering knowledge → zero headroom, don't store.
2. Derivable good design → unstable: the model sometimes re-derives it.
3. Tribal facts (private API behavior, internal conventions, incident lore) → the only reliably useful category — because the model has no channel to know them except through your memory.

If you run a memory layer, this is the uncomfortable implication: most of
what your auto-summarization pipeline diligently stores is category 1. The
moat is category 3, and category 3 mostly comes from incidents, not chats.

Finding #2 that hurt: our memory had been silently dead

While building the experiment, every recall call failed. Root cause: on
Windows, our embedding stack crashed on import — a path-length limitation in
a system Python install. The keyword-matching fallback had been quietly
masking it. Our semantic recall had been 100% broken, on the main
development machine, for an unknown length of time. Nobody noticed because
nothing measured recall health.

That's the real lesson of this post, more than delta = 1:

If you don't have a probe, your memory layer's failures are indistinguishable
from "the agent didn't find it relevant."

We now run an hourly heartbeat: a real semantic query against the daemon,
with auto-restart on failure and an alert written into the memory pool itself.
The first night it ran, it caught the daemon dying and revived it. Boring,
unglamorous — and worth more than any feature we shipped that week.

What this doesn't prove

One experiment, one model, three facts, n=3 per cell. It proves the effect
exists for tribal facts on our stack, not that it generalizes. The
distillation arm (does fine-tuning on stored incident knowledge move a held-out
benchmark?) is running now; pre-registered, honest either way.

Try it

The experiment harness, the fuel-pool QC gates (a memory must fail a
cold-model control before it's worth keeping), and the heartbeat are all in
the repo. If you run an agent memory layer — yours or someone else's — we'd
genuinely love to see your numbers — especially on tribal knowledge, where the
internal-knowledge noise MEM-α warns about finally goes away.

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FAQ: "Isn't this just a longer context window / big cache? Doesn't that eat tokens?"

No — it's the opposite trade. The memory pool (~3k entries) lives on disk and costs zero standing tokens. At session start a local BGE-m3 daemon (no LLM calls) runs one semantic query and injects only the top 3–5 matches, each truncated to a ~500-char excerpt — roughly 1–2k tokens, fixed, independent of how much you've stored. Retrieval cost grows with how many you fetch, never with how much you know.

The paired control above reflects that mechanism: the treatment arm was one natural-language recall + top-3 injection, not stuffing the transcript. The 0/3 → 3/3 flip came from three precise excerpts, not from a longer window.

Where tokens do go: one optional LLM call per session when the stop-hook distills a lesson, plus the 1–2k injection above. Embeddings run locally on our own GPU. The economics: trade disk + local vector compute for context window — the more you remember, the better retrieval looks versus cache-everything.