From 15d54f74a20e6b5ca687c328ac943f8e459bbc3c Mon Sep 17 00:00:00 2001 From: Aleks Date: Mon, 18 May 2026 14:34:03 +0300 Subject: [PATCH] ingest: rag-agents-knowledge-layer-architecture --- ...rag-agents-knowledge-layer-architecture.md | 129 ++++++++++++++ ...knowledge-layer-architecture.transcript.md | 163 ++++++++++++++++++ 2 files changed, 292 insertions(+) create mode 100644 Business/Nate Corpus/2026-05-18_rag-agents-knowledge-layer-architecture.md create mode 100644 Business/Nate Corpus/2026-05-18_rag-agents-knowledge-layer-architecture.transcript.md diff --git a/Business/Nate Corpus/2026-05-18_rag-agents-knowledge-layer-architecture.md b/Business/Nate Corpus/2026-05-18_rag-agents-knowledge-layer-architecture.md new file mode 100644 index 0000000..aba6214 --- /dev/null +++ b/Business/Nate Corpus/2026-05-18_rag-agents-knowledge-layer-architecture.md @@ -0,0 +1,129 @@ +--- +title: "RAG for AI Agents — Knowledge Layer Architecture" +slug: rag-agents-knowledge-layer-architecture +type: video +source: "https://natesnewsletter.substack.com/p/rag-agents-knowledge-layer-architecture" +author: "Nate (Nate's Substack)" +published: 2026-05-13 +processed: 2026-05-18 +themes: + - "[[RAG]]" + - "[[Agentic Workflow]]" + - "[[Implementation Layer]]" + - "[[Audit Trails]]" + - "[[Systems of Record]]" +frameworks: + - "[[Knowledge Layer]]" + - "[[Retrieval Contract]]" + - "[[Context Assembly]]" +terminology: + - "[[Knowledge Layer]]" + - "[[Retrieval Contract]]" + - "[[Context Assembly]]" + - "[[Provenance]]" + - "[[Stack ADR]]" + - "[[Agentic Workflow]]" + - "[[Implementation Layer]]" + - "[[Audit Trails]]" + - "[[Systems of Record]]" +--- + +# RAG for AI Agents — Knowledge Layer Architecture + +## Суть + +Производственные AI-агенты проваливаются не из-за неверного метода ретривала (retrieval), а потому что система ретривала не способна **собрать** нужный контекст до начала работы агента. Проблема не в поиске — в сборке ([[Context Assembly]]). Векторный поиск деградирует с позиции «всей архитектуры» до одного компонента внутри более широкого [[Knowledge Layer]], который включает ретривал, структуру документов, семантические модели данных, контроль доступа, провенанс, память и write-back. + +## Ключевой тезис + +> "That's the new RAG problem — not a retrieval problem, an assembly problem." +> *Это новая проблема RAG — не проблема ретривала, а проблема сборки.* + +Агент пересоздаёт 85% контекста при каждом запуске — это токены, время и уверенные ошибки. Уверенный неправильный ответ — самый дорогой вид ошибки. + +## Почему классический RAG ломается + +Векторная БД находит текст, семантически связанный с вопросом. Этого недостаточно. + +Агент нуждается в: +- текущей записи аккаунта из [[Systems of Record]] +- правах пользователя (access control) +- актуальной политике +- нужном разделе длинного документа +- таблице за конкретной метрикой +- решении с прошлого совещания +- цепочке источников для [[Audit Trails]] — чтобы человек-ревьюер мог восстановить логику + +Без этого модель **импровизирует** → неверные возвраты клиентам, устаревшие политики в ответах, устаревшие метрики в презентациях для совета директоров. + +## Архитектура Knowledge Layer + +[[Knowledge Layer]] = слой поверх векторного поиска: + +| Компонент (RU) | EN | Роль | +|---|---|---| +| Ретривал | Retrieval | Векторный поиск как один из компонентов, не вся архитектура | +| Структура документов | Document Structure | Навигация по разделам длинных документов | +| Семантические модели данных | Semantic Data Models | Привязка чанков к бизнес-объектам | +| Контроль доступа | Access Control | Права пользователя как часть контекста до передачи в модель | +| [[Provenance\|Провенанс]] | Provenance | Цепочка источников для [[Audit Trails]] и объяснимости | +| Память | Memory | Сессионная и долгосрочная память агента | +| Write-back | Write-back | Возможность агента обновлять базу знаний | + +## Retrieval Contract — 7 вопросов + +[[Retrieval Contract]] — семь вопросов для проверки, способен ли [[Knowledge Layer]] поддержать производственного агента. Полный список — за paywall. + +TBD — детали контракта недоступны без подписки. + +> Открытый вопрос: какие именно 7 вопросов включает [[Retrieval Contract]] и есть ли у них публичный аналог в open-source литературе? + +## Сигналы рынка + +Четыре компании — Pinecone, PageIndex, SAP, Dremio — демонстрируют один и тот же сдвиг: «ретривал» для агентов больше не означает только поиск. Это сборка задаче-ориентированного контекста. + +## Артефакты (paste-ready, за paywall) + +| Артефакт | Назначение | +|---|---| +| Retrieval Contract Spec | Спецификация контракта ретривала для конкретного агента | +| Failure Triage | Диагностика отказов — где именно ломается сборка контекста | +| [[Stack ADR]] | Architecture Decision Record — обоснование выбора стека знаний | + +## Терминология + +| Термин (RU) | EN | Определение | +|---|---|---| +| [[Knowledge Layer]] | Knowledge Layer | Слой знаний поверх векторного поиска; включает ретривал + структуру + ACL + провенанс + память + write-back | +| [[Retrieval Contract]] | Retrieval Contract | 7 вопросов, определяющих готовность слоя знаний к производственному [[Agentic Workflow]] | +| [[Context Assembly]] | Context Assembly | Сборка задаче-ориентированного контекста до запуска агента — в отличие от поиска по запросу в реальном времени | +| [[Provenance]] | Provenance | Цепочка источников, позволяющая аудитору восстановить логику решения агента; часть [[Audit Trails]] | +| Write-back | Write-back | Возможность агента записывать результаты работы обратно в базу знаний | +| [[Stack ADR]] | Stack ADR | Architecture Decision Record — документ обоснования архитектурных решений по стеку | + +## Что использовать для нашего портфеля + +**Контекст: AI-интегратор, [[Implementation Layer]], бизнес-объекты как единица знания, PE как канал.** + +**1. Переформулировать RAG в пресейле** +Не «векторный поиск», а «сборка контекста» ([[Context Assembly]]). Клиенту важно не то, чем ищем, а что агент получает до начала работы. Это точка дифференциации от коробочных RAG-решений. + +**2. Бизнес-объект как единица ретривала** +Классический RAG режет документы на чанки без структуры. Правильный [[Knowledge Layer]] привязывает чанки к бизнес-объектам (аккаунт, политика, метрика, контракт). Это архитектурный принцип нашего [[Implementation Layer]]. + +**3. [[Audit Trails]] и [[Provenance]] — не опция** +Для enterprise-клиентов (финансы, страхование, юридика) объяснимость агента — регуляторное требование. Продаём [[Provenance]] как встроенную функцию, а не постфактум-дополнение. + +**4. Контроль доступа внутри [[Knowledge Layer]]** +Права пользователя должны фильтровать ретривал до передачи контекста в модель. Это архитектурное требование, а не фильтр на выходе. + +**5. PE-канал: апгрейд существующих RAG** +Портфельным компаниям PE, у которых уже есть базовый RAG (только вектор), предлагаем надстройку: структура документов + ACL + [[Provenance]] + память. Входная точка — [[Failure Triage]] по текущим сбоям агентов. + +**6. Write-back как дорогостоящая фича следующего цикла** +Агенты, которые обновляют [[Systems of Record]] по результатам своей работы — следующий уровень [[Agentic Workflow]]. Стоит заложить в product roadmap для клиентов с высокой агентной зрелостью. + +> Открытый вопрос: есть ли у текущих клиентов метрика доли контекста, пересобираемого при каждом запуске агента? Это готовый KPI для обоснования апгрейда с классического RAG до полноценного [[Knowledge Layer]]. + +--- +*Источник: Nate's Substack, опубликовано 2026-05-13. 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Please upgrade. +123 +5 +6 +Your AI agent is rediscovering 85% of its context every run. Here's the architecture fix (+ Contract Spec, Failure Triage, and Stack ADR) +Why production agents need more than a vector database. +Nate +May 13, 2026 +∙ Paid +123 +5 +6 +Share +There’s a debate going on right now about whether vector search is obsolete. That’s the wrong layer to be arguing about. The agents I’m watching fail in production aren’t failing because the retrieval method is wrong — they’re failing because the retrieval system can’t assemble what the agent actually needs before it starts acting. +A vector database can find text semantically related to a question. Useful, but nowhere near enough. Agents need the current account record, the user’s permissions, the controlling policy, the right section of a long document, the table behind a metric, the prior decision from a meeting, and the source trail that lets a human reviewer reconstruct why the agent did what it did. When the system doesn’t prepare that context, the model improvises — and the cost shows up everywhere except the place you’re looking. Wrong refunds get issued. Stale policies get cited. Outdated metrics make it into board decks. The agent burns tokens and wall-clock time rebuilding context every run, and when the answer finally lands, it lands confidently — which is the most expensive way to be wrong. That’s the new RAG problem — not a retrieval problem, an assembly problem. +So the next move isn’t “vectors versus something else.” It’s that vector search is quietly getting demoted from the whole architecture to one component inside a broader knowledge layer for agents — a layer that includes retrieval, but also document structure, semantic data models, access control, provenance, memory, and write-back. I want to be careful not to overstate this: vector search isn’t going anywhere. But the conversation about where the real work happens has moved. +Here’s what’s inside: +Why classic RAG worked — and where it stops working. +How to spot the moment your retrieval architecture became the bottleneck instead of the solution. +What Pinecone, PageIndex, SAP, and Dremio are all saying. +Four different companies, one shared shift in what “retrieval” actually means for agents. +The practical architecture. +Seven questions to test whether your knowledge layer can support a production agent. +What could go wrong. +Where this new architecture quietly breaks, and how to tell if you’re overbuilding. +How to put this to work. +A Retrieval Contract Spec, a Failure Triage, and a Stack ADR: paste-ready artifacts for the three states a builder hits when working on retrieval. +Let’s walk through how this shift is playing out across the stack, and what it means for how you build. +Subscribers get all posts like these! +Subscribe +Listen to this episode with a 7-day free trial +Subscribe to +Nate’s Substack +to listen to this post and get 7 days of free access to the full post archives. +Start trial +Already a paid subscriber? +Sign in +Nate's Notebook +Welcome to my podcast! In these audio reviews of my newsletters, I am to break down complex AI topics in a way that's approachable and relatable. I want you to walk away with the confidence to leverage AI more effectively at home and at work! +Welcome to my podcast! In these audio reviews of my newsletters, I am to break down complex AI topics in a way that's approachable and relatable. I want you to walk away with the confidence to leverage AI more effectively at home and at work! +Subscribe +Listen on +Substack App +RSS Feed +Appears in episode +Nate +Recent Episodes +Executive Briefing: Stop asking if AI can do this. Start asking what shape the work is. +20 hrs ago +• +Nate +Exclusive: a conversation with Tibo from Codex on what your company has to become when the model can actually do the work +May 16 +• +Nate +The 2 prompts I'd run before any 2026 SaaS renewal (especially if you're deploying agents) +May 15 +• +Nate +Six things have to be true before AI changes a workflow. Most companies have built two. +May 14 +• +Nate +Six layers your agent has to handle. Most products have only thought about two. + a responsibility-layer audit. +May 12 +• +Nate +You gave your AI agent real tools. 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