The Library Behind The Agent

123 books, a keyword lookup instead of a vector store, and a citation log that measures whether a book changed the method — not whether it got quoted.

What it is

123 books plus 4 periodicals, organised into 19 clusters — strategy, pricing, supply chain,

negotiation, data analytics, and so on. Every book that counts as "in the library" has three

things: the full text as markdown, a one-page brief, and a trigger card (keywords, mental

models, one line on when it applies). A PDF sitting on disk without those three is not in the

library — the lookup tool cannot find it, and for this system that means it does not exist.

How retrieval works

No vector database, no embeddings, no similarity search. A keyword index over the trigger

cards, weighted — trigger phrases and title count 3x, mental models and the one-line hook

count 2x, the cluster name counts 1x. A query like *"how do I structure a price increase

without losing the account"* gets tokenized, matched against all 127 cards, and returns the

top 3.

The reason is the same one behind this system's memory layer generally: retrieval was never

the interesting problem. Knowing which framework actually applies was. A vector search

returns the closest match; it has no opinion on whether the match is decoration or a genuine

fit for the situation. That judgment call — the Framework-Fit Gate — happens after

retrieval, not instead of it: before applying a book's method, check whether the context

actually matches the book's assumptions. A blind application of a good framework to the wrong

situation is a worse failure than using no framework at all.

The number that mattered: not citations, but method changes

The first version of this system measured how often a book got cited per session and set a

floor — roughly one citation per business conversation. It produced exactly what you'd expect

from optimising a proxy metric: books got quoted at the end of an answer that had already been

decided some other way. A decorative citation.

The fix wasn't a better floor. It was changing what gets logged. Every consultation now

records one of four outcomes — accepted, rejected, ignored, tbd — and the honest

answer is a first-class outcome, not a failure to suppress:

2026-08-14 | Reconciling supplier invoicing — mapped clusters, but the task
was pure numeric reconciliation | none — hook fired, book not used | ignored

Across 144 logged consultations, roughly 84% ended accepted — the book changed a

method, a sequence, or a decision rule, not just the wording of an answer. The rest are kept,

not deleted. A citation log that only records successes is measuring its own confidence, not

its own accuracy.

What actually gets cited, and how often

The most-consulted books by logged count: Cialdini's Influence (7), Muller's *Essentials of

Inventory Management* (6), Baker/Marn/Zawada's The Price Advantage (5), Hormozi's *$100M

Offers* (5), Simon's Confessions of the Pricing Man (5), O'Brien's *Supplier Relationship

Management* (4), Voss's Never Split the Difference (3), Knaflic's Storytelling with Data

(3), Meadows's Thinking in Systems (3), Lewis's Moneyball (3).

Where a surprise score was logged — how much the framework changed the answer from what would

have happened without it, 1–5 — the average across 63 rated entries sits at 2.97. Not

every consultation is a revelation; most are a framework confirming and sharpening a direction

that was already roughly right. The log keeps those too, because a framework that only earns

its place on the dramatic cases is not actually being used as doctrine.

What broke

The lookup tool's first version worked well on the queries it was tested with — two or three

keywords, exact terms — and badly on the queries it actually receives, which are full

sentences. An internal audit found **93% top-3 accuracy on exact short queries and 37% on

natural-language ones**, the gap between a demo and production.

Two causes, both mechanical. First, no stopword list: short filler words in the query language

matched as substrings inside unrelated words — a three-letter function word turned up embedded

in an unrelated eight-letter term and pulled in the wrong card. A book with a broad, generic

trigger card beat a book with a precise, literal match on the actual topic, purely because the

broad card accumulated more substring hits. Second, no handling of word inflection: a plural

or declined form of a search term didn't match the singular stem stored in the trigger card,

which sinks retrieval hard in any language with rich morphology.

The fix was a stopword list, prefix-based matching at a word boundary, and a requirement that

part of the match score come from the high-signal fields — triggers, title, mental models —

rather than letting a weak match in a low-signal field win on volume alone. A regression

harness now runs before any change to the ranking logic and blocks it if exact-match accuracy

drops or natural-language accuracy doesn't improve. The lesson generalises past this one tool:

a retrieval system tested only on the query shape you'd write by hand will quietly fail on the

query shape a real conversation actually produces.

Acquisition has a filter too

Not every book that looks relevant gets added. One recurring signal: ISBN prefix. A 979-8

prefix marks Amazon KDP self-publishing — assigned to anyone who uploads a file, not a signal

of editorial vetting. A batch evaluation of candidate titles in one fast-moving technical

category found 9 of 11 candidates carried that prefix, and the verdict on one specific title

reversed once the prefix was checked against the actual publisher. The second filter is

half-life: a book that teaches syntax has a shelf life of months; a book that teaches a method

survives the tools changing under it. The library buys for the second kind.

From brief to playbook

A one-page brief is enough to decide whether a book applies. It is not enough to actually

carry out its method under pressure — a brief tells you what a book argues, not the

decision rules and step order you need mid-conversation. Ten books have graduated to a second

artefact: a playbook. Not a summary, an executable sequence — when to activate it, what inputs

it needs, the procedure with its decision rules spelled out, what trace in the final output

proves the method was actually followed, and where it conflicts with another book's advice

when two frameworks would point different directions.

Promotion to playbook is deliberately lazy: a book earns one only after it has already been

used for real, twice, on two separate occasions — not on the strength of how good the book

looks on a first read. Writing the executable version before the framework has proven itself

in practice would mean encoding untested judgment calls as procedure, which is a more

expensive mistake than leaving a good book at the brief stage a little longer than necessary.

Delegated work (see Twelve Agents, One Memory)

carries this further: a sub-agent handling a domain question gets a line

naming the one to three books relevant to its task, and its output is expected to show the

book's method applied, not the book's name dropped. The distinction is the same one that

killed the citation-frequency floor — citing a source and following it are different

behaviours, and only one of them changes what actually happens next.

Where this is going

Brief coverage sits at 100% of the 123 books — every title that made it into the library has

the three artefacts retrieval depends on. What is not yet done: a systematic pass on whether

the ISBN-prefix filter has ever produced a false negative — a legitimately good book dismissed

on prefix alone — because the filter has only ever been tested for false positives.

[The Library Behind The Agent](https://stillvalid.dev/architecture/the-library-behind-the-agent) — stillvalid, SV-5000 (deep-dive, verified 2026-08-17)