我们构建了 AI Agent 记忆的向量 RAG 替代方案

快速回答

面向 AI Agent 的 RAG 让 Agent 能在推理和执行操作时访问外部信息。Agentic RAG 将检索视为 Agent 可以调用、评估并重复执行的工具。向量数据库、Embedding 和 Chunking 依然有用，但它们不应成为每一个文档—Agent 工作流的首选架构。

检索增强生成（Retrieval-Augmented Generation，RAG）已成为将语言模型连接到外部信息的标准方式之一。传统方法广为人知：将文档拆分为多个 Chunk，生成 Embedding，将这些向量存入数据库，并在要求模型回答前检索最接近的段落。

这种架构解决了一个重要的早期问题。但 AI Agent 正在改变“检索”需要具备的含义。Agent 不只是回答问题；它们会选择工具、比较来源、检查中间结果、执行多步骤工作流，并决定何时已经掌握足以采取行动的信息。下一代 RAG 正从固定的“检索后生成”管道，转向 Agentic RAG：检索作为更广泛决策过程的一部分，由 Agent 控制。Microsoft 将这种转变描述为：将检索视为 Agent 可以在必要时调用、评估和重复执行的工具。

这并不意味着向量数据库、Embedding 或 Chunking 会在一夜之间消失。它意味着这些技术不再是唯一值得考虑的架构；对于许多文档—Agent 工作流而言，它们不应是开发者首先构建的内容。

什么是面向 AI Agent 的 RAG？

面向 AI Agent 的 RAG 是一种让 Agent 在推理和采取行动时访问外部信息的系统。传统 RAG 通常遵循单一的固定顺序。Agentic RAG 改变了这一顺序，使 Agent 自行决定何时进行检索。

传统 RAG

用户问题
→ 对问题生成 Embedding
→ 搜索向量数据库
→ 检索 Chunk
→ 将 Chunk 发送给模型
→ 生成回答

Agentic RAG

用户任务
→ Agent 分析任务
→ Agent 决定所需信息
→ Agent 调用检索或文档工具
→ Agent 评估结果
→ 如有必要，Agent 调用另一项工具
→ Agent 采取行动或作出响应

关键区别在于，检索不再是不可见的预处理步骤，而是成为 Agent 可使用的一项能力。LangChain 的文档直接描述了这一原则：Agent 可以使用一个或多个工具来获取外部知识，包括文档加载器、API 和数据库查询，而不是完全依赖预先构建的检索链。

传统 RAG 与 Agentic RAG

传统 RAG	Agentic RAG
生成前进行检索	Agent 决定何时检索
通常使用一条检索管道	可以使用多种工具和来源
检索相似的 Chunk	检索完成任务所需的信息
通常返回单一答案	可以持续执行多个步骤
固定的“查询到上下文”流程	动态规划与迭代
主要为问答设计	为推理与行动设计
上下文由相似度选择	上下文由任务相关性选择
检索是基础设施层	检索是 Agent 能力

因此，Agentic RAG 并不只是给 RAG 加上一个 Agent。它是设计 Agent、文档与外部工具之间关系的一种不同方式。

固定式向量 RAG 的局限

基于向量的 RAG 仍然有用，尤其适用于大型、持久化的文本集合。但当它成为每个 Agent 的默认解决方案时，标准管道存在若干弱点。

Chunking 可能破坏文档结构

Chunking 将文档划分成较小段落，以便进行索引和检索。但文档并非天然由任意文本片段组成。其含义可能取决于章节标题、表格表头、脚注、前一段内容、页码引用、行与行之间的关系、条款及其修订内容，或某个值在表单中的位置。

没有列表头的一行表格数据，并不等同于完整表格。缺少定义章节的合同条款可能会造成误导。从政策中抽取的一句话，可能依赖于数段之后列出的例外情况。

Embedding 衡量相似度，而非业务相关性

Embedding 表示语义关系。它们能够识别与查询内容相似的文本，但相似性并不等同于与业务任务相关。被要求判断一张发票是否与采购订单匹配的 Agent，需要获取供应商、产品、数量、单价、税额、总额和币种。向量搜索可能检索到有关两份文档的文本，但不会自动完成比较。

一次检索可能不够

固定 RAG 链假设一次检索操作即可提供必要上下文。但 Agent 往往需要多个步骤：找到合同、找到最新修订、检查续约条款、比较日期、判断是否需要通知，然后创建提醒。Agent 会根据已发现的信息决定下一步检查什么。固定的“检索后回答”管道并非为这一过程设计。

向量数据库会增加基础设施负担

• 文档摄取、解析、Chunking 和 Embedding 生成。
• 向量存储、元数据筛选、索引更新和文档删除。
• 版本管理、访问控制和检索评估。
• 引用处理，以及解析方式变更后的重新处理。

Frameworks such as LlamaIndex and LangChain simplify parts of this process, but they do not eliminate the underlying architectural decisions. LlamaIndex remains particularly useful for data connectors, indexing and query engines, while other frameworks focus on orchestration and tool use. The question is not whether these tools are valuable. The question is whether every agent should begin with a full vector retrieval stack.

LlamaIndex 和 LangChain 等框架简化了这一过程的部分环节，但并不能消除底层的架构决策。LlamaIndex 在数据连接器、索引和查询引擎方面依然特别有用，而其他框架则侧重于编排和工具使用。问题不在于这些工具是否有价值，而在于每个 Agent 是否都应从一整套向量检索栈开始构建。

Chunking、Embedding 和向量数据库并非全部未来

如果简单地宣称向量数据库、Embedding 和 Chunking 已经过时，未免过于片面。它们对于大型文档库、持久化企业知识库、语义搜索、相似性发现、推荐系统、长期集合以及高频重复查询，仍然具有价值。

真正的转变在于架构。向量检索正在成为 Agent 信息系统中的一种工具，而不再是每个 AI Agent 的通用基础。Agent 将针对不同任务使用最恰当的工具：对发票使用结构化提取，对财务记录使用 SQL，对实时业务数据使用 API，对短文件进行全文档处理，对案件工作流进行文档比对，对关系数据使用知识图谱，对词法查询使用搜索索引，对语义发现使用向量检索，并在存在歧义时进行人工审核。

什么是 Agentic RAG？

Agentic RAG 是一种检索架构，其中 AI Agent 控制何时以及如何访问外部信息。Agent 可以决定是否需要检索、选择来源、构造更精确的子问题、从多个来源检索、评估结果、提出后续问题、比较输出、识别证据不足的情况，并在采取缺乏依据的行动前停止。

Microsoft 的 Agentic RAG 指南将这种模式描述为动态查询规划、多步骤推理和自主信息收集，而不是一次固定的检索调用。

检索成为工具

• search_documents() 和 query_document()
• process_multiple_documents() 和 compare_documents()
• extract_fields() 和 query_database()
• search_web()、get_contract_amendment() 和 request_human_review()

当被问及发票是否与采购订单匹配时，Agent 可以同时处理两个文件，比较供应商和总额；若两者不一致，则返回异常；若结果不明确，则请求审核。它不需要依赖一项通用语义搜索，并期待检索到的 Chunk 恰好包含所需比较内容。

面向 Agent 的 RAG 演进

第一阶段：检索与生成
查询 → 向量搜索 → Chunk → LLM 回答

第二阶段：检索与重排序
查询 → 向量搜索 → 重排序 → 更优 Chunk → LLM 回答

第三阶段：混合检索
查询 → 向量 + 关键词 + 元数据 → 组合上下文 → LLM 回答

第四阶段：Agentic 检索
任务 → Agent 规划 → 选择工具 → 检索或处理
→ 评估 → 迭代 → 行动

系统不再以单一数据库为中心，而是以 Agent 使用信息工具的能力为中心。

向量 RAG 的替代方案

“RAG 的替代方案”这一说法往往具有误导性，因为许多替代方案仍会为模型提供外部上下文。它们只是采用了不同的检索或处理机制。

结构化提取

结构化提取会将文档转换为 Agent 可直接使用的字段：供应商、总额、税额和到期日，随后进行验证并创建会计记录。当工作流依赖已知字段时，这通常优于 Chunk 检索。

直接文档查询

应用程序不预先为文档建立索引，而是发送文档并对其提出问题。这种方式适合一次性文件、用户上传文件、临时案例、短文档和动态工作流。

多文档处理

可以在一次操作中处理并比较多份文档：一张发票、一份采购订单和一张送货单，并返回匹配或不匹配的结果。这并非传统 RAG，而是为理解文件间关系而设计的文档处理操作。

基于工具的检索、全文档、图谱与 SQL

Agent 可以调用工具来查询 SQL 数据库、CRM、API、内部系统、文档存储、网络搜索和知识图谱。LangChain 的检索文档清楚地区分了这一点：现有数据库或内部系统可以作为 Agent 工具接入，而无需将其重建为向量知识库。

对于小型或中等规模文档，发送结构化的全文档表示可能比将其拆分为 Chunk 更可靠，尤其是当章节之间的关系很重要、表格需要保持完整，或 Agent 必须比较多个文件时。知识图谱能够表示相似度检索无法自然捕捉的实体和关系。许多企业问题——逾期发票、供应商限额、不完整的入职流程——用 SQL 表达会更加精确。

成熟的 Agent 可以结合文档处理、结构化提取、SQL、API、搜索、知识图谱和人工审核，并选择与任务匹配的能力。

面向 Agent 的文档 RAG

文档 RAG 常被视为将 PDF 放进向量数据库的同义词。但这只是其中一种实现方式。处理文档的 Agent 可能需要读取文件、提取字段、提出后续问题、比较文档、跟踪来源证据、理解表格、检测缺失文件、维护上下文、创建任务并升级不确定性。

• extract_document() 和 query_document()
• process_multiple_documents() 和 compare_documents()
• get_source_evidence() 和 detect_missing_information()
• retain_context() 和 request_review()

这种模型更接近 Agent 在实际工作流中的工作方式。Agent 被赋予一组文档能力，并决定如何将它们组合起来。

Claix 与下一代文档 RAG

Claix 可以被定位为面向 Agent 的文档层，适用于不希望手动构建每个 RAG 组件的场景。Claix 不会强制每个工作流都经历解析、分块、Embedding、存储和检索，而是提供文档能力：将文件转换为供 Agent 使用的结构化 Markdown、将文件转换为供工具调用的结构化 JSON、将多个文件转换为跨文档比较，以及将文档转换为用于后续问题的持久上下文。

核心理念并不是每个向量数据库都已过时，而是 Agent 通常可以从更直接的文档操作开始。有关本文所述的两个层级，请参阅“Document to Markdown”和“Multi-Document Processing”。

Markdown 作为 Agent 上下文

Claix 可以将 PDF、Excel 和 CSV、Word 文档、图像、音频，以及 TXT、HTML 或 XML 转换为结构化 Markdown。Markdown 可以保留标题、章节、列表、表格、转录内容、文档顺序和人类可读的结构。应用程序可以将这些 Markdown 传递给 Agent、存入知识空间、稍后建立索引、转换为 JSON、与另一份文档比较，或在工作流中使用。

用 JSON 执行操作，用 Markdown 提供上下文

实用架构通常同时使用两种格式。当 Agent 需要理解文档时，Markdown 很有用；当 Agent 需要调用函数或更新系统时，JSON 很有用。这避免了在一开始就强迫每个文档工作流在非结构化文本与严格 Schema 之间二选一。

多文档处理

有些 Agent 任务根本不需要知识库。它们需要回答一组相关文件之间的问题：合同与修订案之间发生了什么变化、发票与采购订单总额是否匹配、申请材料是否与身份证明文件相符，或者哪些费用违反了政策。与为临时案例构建向量索引相比，直接的多文档操作可能更简单、更合适。

Agentic RAG 的 LlamaIndex 替代方案

LlamaIndex 是一个用于将模型连接到数据、构建索引和创建检索工作流的有用框架。但它不是唯一选择，也未必是每个 Agent 的正确抽象。合适的替代方案取决于你正在构建什么。

• 直接调用模型工具：当你希望控制工具定义、状态、权限、重试和可观测性时。
• LangChain 和 LangGraph：适用于 Agent 图、分支工作流、持久状态和人工审批节点。
• Mastra：适用于使用 TypeScript 构建 Agent 的团队，wo

Quick answer

RAG for AI agents gives an agent access to external information while it reasons and acts. Agentic RAG treats retrieval as a tool the agent can invoke, evaluate and repeat. Vector databases, embeddings and chunking remain useful, but they should not be the first architecture for every document-agent workflow.

Retrieval-Augmented Generation has become one of the standard ways to connect language models to external information. The traditional approach is familiar: split documents into chunks, generate embeddings, store those vectors in a database and retrieve the nearest passages before asking the model to answer.

That architecture solved an important early problem. But AI agents are changing what retrieval needs to mean. Agents do not only answer questions. They choose tools, compare sources, inspect intermediate results, perform multi-step workflows and decide when they have enough information to act. The next generation of RAG is moving away from a fixed retrieve-then-generate pipeline and toward agentic RAG: retrieval controlled by the agent as part of a broader decision process. Microsoft describes this shift as treating retrieval as a tool that an agent can invoke, evaluate and repeat when necessary.

This does not mean vector databases, embeddings or chunking disappear overnight. It means they are no longer the only architecture worth considering, and for many document-agent workflows they should not be the first thing developers build.

What is RAG for AI agents?

RAG for AI agents is a system that gives an agent access to external information while it reasons and acts. Traditional RAG usually follows one fixed sequence. Agentic RAG changes the sequence so the agent decides when to retrieve.

Traditional RAG

User question
→ Embed the question
→ Search a vector database
→ Retrieve chunks
→ Send chunks to the model
→ Generate an answer

Agentic RAG

User task
→ Agent analyses the task
→ Agent decides what information it needs
→ Agent calls a retrieval or document tool
→ Agent evaluates the result
→ Agent calls another tool if necessary
→ Agent takes an action or responds

The important difference is that retrieval is no longer an invisible preprocessing step. It becomes a capability available to the agent. LangChain’s documentation describes this principle directly: an agent can use one or more tools to fetch external knowledge, including document loaders, APIs and database queries, instead of relying exclusively on a pre-built retrieval chain.

Traditional RAG versus agentic RAG

Traditional RAG	Agentic RAG
Retrieval happens before generation	The agent decides when to retrieve
Usually uses one retrieval pipeline	Can use multiple tools and sources
Retrieves similar chunks	Retrieves information required for a task
Often returns a single answer	Can continue through multiple steps
Fixed query-to-context flow	Dynamic planning and iteration
Designed primarily for question answering	Designed for reasoning and action
Context is selected by similarity	Context is selected by task relevance
Retrieval is an infrastructure layer	Retrieval is an agent capability

Agentic RAG is therefore not simply RAG with an agent added. It is a different way of designing the relationship between agents, documents and external tools.

The limitations of fixed vector RAG

Vector-based RAG remains useful, especially for large, persistent collections of text. But the standard pipeline has several weaknesses when it becomes the default solution for every agent.

Chunking can destroy document structure

Chunking divides a document into smaller passages so they can be indexed and retrieved. Documents are not naturally collections of arbitrary text fragments. Meaning can depend on a section title, a table header, a footnote, a previous paragraph, a page reference, the relationship between rows, a clause and its amendment, or the position of a value inside a form.

A table row without its column headers is not equivalent to a complete table. A contract clause without the definitions section may be misleading. A sentence extracted from a policy may depend on exceptions stated several paragraphs later.

Embeddings measure similarity, not business relevance

Embeddings represent semantic relationships. They can identify text that resembles a query, but similarity is not the same as relevance to a business task. An agent asked whether an invoice matches a purchase order needs supplier, product, quantity, unit price, tax, total and currency. A vector search may retrieve text about both documents. It does not automatically perform the comparison.

One retrieval call may not be enough

A fixed RAG chain assumes that one retrieval operation can provide the necessary context. Agents often need several steps: find the contract, find the latest amendment, check the renewal clause, compare the date, determine whether notice is required, then create a reminder. The agent decides what to inspect next based on what it discovered. A fixed retrieve-then-answer pipeline is not designed for that process.

Vector databases add infrastructure

• Document ingestion, parsing, chunking and embedding generation.
• Vector storage, metadata filtering, index updates and document deletion.
• Version management, access control and retrieval evaluation.
• Citation handling and reprocessing when parsing changes.

Frameworks such as LlamaIndex and LangChain simplify parts of this process, but they do not eliminate the underlying architectural decisions. LlamaIndex remains particularly useful for data connectors, indexing and query engines, while other frameworks focus on orchestration and tool use. The question is not whether these tools are valuable. The question is whether every agent should begin with a full vector retrieval stack.

Chunking, embeddings and vector databases are not the whole future

It is too simplistic to say that vector databases, embeddings and chunking are obsolete. They remain valuable for large documentation libraries, persistent enterprise knowledge bases, semantic search, similarity discovery, recommendation systems, long-term collections and high-volume repeated queries.

The real shift is architectural. Vector retrieval is becoming one tool inside an agentic information system, not the universal foundation of every AI agent. Agents will use the most appropriate tool for each task: structured extraction for invoices, SQL for financial records, APIs for live business data, full-document processing for short files, document comparison for case workflows, knowledge graphs for relationships, search indexes for lexical queries, vector retrieval for semantic discovery, and human review for ambiguity.

What is agentic RAG?

Agentic RAG is a retrieval architecture in which an AI agent controls when and how external information is accessed. The agent may decide whether retrieval is necessary, choose the source, formulate a more precise sub-question, retrieve from more than one source, evaluate the result, ask a follow-up, compare outputs, detect insufficient evidence and stop before taking an unsupported action.

Microsoft’s agentic RAG guidance describes this pattern as dynamic query planning, multi-step reasoning and autonomous information gathering rather than a single fixed retrieval call.

Retrieval becomes a tool

• search_documents() and query_document()
• process_multiple_documents() and compare_documents()
• extract_fields() and query_database()
• search_web(), get_contract_amendment() and request_human_review()

Asked whether an invoice matches a purchase order, the agent can process both files together, compare supplier and totals, return an exception if they differ, and request review if the result is unclear. It does not need a generic semantic search that hopes the retrieved chunks contain the comparison.

The evolution of RAG for agents

Stage one: retrieve and generate
Query → Vector search → Chunks → LLM answer

Stage two: retrieve and rerank
Query → Vector search → Reranking → Better chunks → LLM answer

Stage three: hybrid retrieval
Query → Vector + keyword + metadata → Combined context → LLM answer

Stage four: agentic retrieval
Task → Agent plans → Selects tools → Retrieves or processes
→ Evaluates → Iterates → Acts

The system is no longer centred on one database. It is centred on the agent’s ability to use information tools.

Alternatives to vector-based RAG

The phrase “alternative to RAG” is often misleading because many alternatives still provide external context to a model. They simply use a different retrieval or processing mechanism.

Structured extraction

Structured extraction converts a document into fields an agent can use directly: supplier, total, tax and due date, then validation and an accounting record. This is often better than chunk retrieval when the workflow depends on known fields.

Direct document querying

Instead of indexing documents in advance, the application sends a document and asks questions about it. This works well for one-off files, user uploads, temporary cases, short documents and dynamic workflows.

Multi-document processing

Several documents can be processed together and compared in one operation: an invoice, a purchase order and a delivery note returning a match or mismatch. This is not traditional RAG. It is a document-processing operation designed to understand relationships across files.

Tool-based retrieval, full documents, graphs and SQL

The agent can call tools that query SQL databases, CRMs, APIs, internal systems, document stores, web search and knowledge graphs. LangChain’s retrieval documentation makes the distinction clear: an existing database or internal system can be connected as an agent tool without rebuilding it as a vector knowledge base.

For small or moderate documents, sending a structured full-document representation may be more reliable than splitting it into chunks, especially when section relationships matter, tables need to stay intact, or the agent must compare several files. Knowledge graphs represent entities and relationships that similarity retrieval does not capture naturally. Many enterprise questions — overdue invoices, supplier limits, incomplete onboarding — are more precise as SQL.

A mature agent may combine document processing, structured extraction, SQL, APIs, search, a knowledge graph and human review, and choose the capability that matches the task.

RAG over documents for agents

RAG over documents is often treated as synonymous with putting PDFs in a vector database. That is only one implementation. An agent working with documents may need to read the file, extract fields, ask follow-up questions, compare documents, track source evidence, understand tables, detect missing files, maintain context, create a task and escalate uncertainty.

• extract_document() and query_document()
• process_multiple_documents() and compare_documents()
• get_source_evidence() and detect_missing_information()
• retain_context() and request_review()

This model is closer to how agents work in real workflows. The agent is given a set of document capabilities and decides how to combine them.

Claix and the next generation of document RAG

Claix can be positioned as a document layer for agents that do not want to build every RAG component manually. Instead of forcing every workflow through parse, chunk, embed, store and retrieve, Claix exposes document capabilities: a file to structured Markdown for agent context, a file to structured JSON for a tool call, several files to a cross-document comparison, and a document to persistent context for follow-up questions.

The key idea is not that every vector database is obsolete. It is that an agent can often begin with a more direct document operation. See Document to Markdown and Multi-Document Processing for the two layers this article describes.

Markdown as agent context

Claix can convert PDFs, Excel and CSV, Word documents, images, audio, and TXT, HTML or XML into structured Markdown. Markdown can preserve headings, sections, lists, tables, transcribed content, document order and a human-readable structure. The application can pass that Markdown to the agent, store it in a knowledge space, index it later, transform it into JSON, compare it with another document or use it in a workflow.

JSON for action, Markdown for context

A practical architecture often uses both formats. Markdown is useful when the agent needs to understand a document. JSON is useful when the agent needs to call a function or update a system. This avoids forcing every document workflow to choose between unstructured text and rigid schemas at the beginning.

Multi-document processing

Some agent tasks do not need a knowledge base at all. They need to answer a question across a small set of related files: what changed between a contract and an amendment, whether invoice and purchase-order totals match, whether an application matches an identity document, or which expenses violate a policy. A direct multi-document operation can be simpler and more appropriate than building a vector index for a temporary case.

Alternatives to LlamaIndex for agentic RAG

LlamaIndex is a useful framework for connecting models to data, building indexes and creating retrieval workflows. It is not the only option, and it may not be the right abstraction for every agent. The right alternative depends on what you are building.

• Direct model tool calling, when you want control over tool definitions, state, permissions, retries and observability.
• LangChain and LangGraph, for agent graphs, branching workflows, persistent state and human approval nodes.
• Mastra, for TypeScript teams building agents, workflows and retrieval in the JavaScript ecosystem.
• Haystack, for modular Python document processing, retrieval and agent pipelines.
• DSPy, when the main challenge is systematically improving prompts, reasoning modules or retrieval strategies.
• A custom agent runtime plus a managed document API, when you want fewer abstraction layers around extraction, Markdown, JSON and cross-document processing.

Claix is better understood as a document-processing layer that can be used with a custom runtime or with frameworks such as LangChain and LangGraph. It does not replace every capability of a complete agent framework.

When vector databases still make sense

A vector database may still be appropriate when you have millions of document segments, a large permanent knowledge collection, high query volume, repeated semantic search, many users querying the same data, complex filtering and access policies, long-term indexing requirements, or a search experience as the primary product.

The better question is not whether to use vectors or agents. It is which information operation the task requires. Use vector retrieval for semantic discovery, structured extraction for fields, multi-document processing for comparison, SQL for structured records, APIs for live data and a knowledge graph for relationships. An agent can orchestrate all of them.

A better architecture for document agents

Layer one: format-aware extraction
PDF, Excel, Word, image, audio, HTML
        ↓
Readable Markdown or structured JSON

Layer two: document operations
Query, compare, extract, validate, find missing information, get evidence

Layer three: agent reasoning
Plan, select tools, evaluate results, ask follow-ups, decide whether to continue

Layer four: business actions
Create a record, approve, reject, notify, open a ticket, update a CRM, request review

This architecture is more flexible than treating a vector database as the centre of every document application.

Finance, legal and onboarding

A fixed vector pipeline can parse an invoice, chunk it, embed it and ask a model for the total. It does not automatically verify the invoice. An agentic document approach processes the invoice with the purchase order and the delivery note, compares supplier, quantities and totals, and lets the agent approve or escalate.

A legal agent that only retrieves the most similar renewal clause still misses the relationship between the contract, the amendment and the schedule. The useful operation is to extract dates and obligations, compare original and current terms, identify changed clauses and create a review task.

Customer onboarding is the same pattern: an application form, an identity document and a company record. The core operation is verification across documents, not a search for similar paragraphs.

Why agentic RAG is likely to become the default pattern

• Agents need dynamic context across several sources and steps.
• Retrieval is only one tool, next to databases, CRMs, calculators, web search and human review.
• Agents must evaluate whether evidence is sufficient, whether documents disagree and whether it is safe to act.
• Tasks such as approving a supplier cannot be represented as one vector search followed by one response.
• The best source may be a document, a spreadsheet, a database, an API, a graph, a user or a reviewer.

What changes for developers

The developer’s job shifts from building one large RAG pipeline to designing a set of reliable agent capabilities. Instead of asking how to index every document, ask what the agent should be able to do with documents: read a document, extract fields, compare documents, query a document, find missing information, get evidence and create a review task.

A practical decision framework

• Use direct document processing when files are uploaded for one task, the set is small, storage is unnecessary, or you need an immediate cross-document answer.
• Use structured extraction when fields are known, validation matters, or the output will update another system.
• Use a Knowledge Space when documents must persist and users will query them again.
• Use vector search when semantic discovery is central, the collection is large and similarity is genuinely the right operation.
• Use agentic orchestration when the task needs several tools, planning, comparison, actions and approvals.

These options are complementary, not mutually exclusive.

Common mistakes when building agentic RAG

• Treating every document problem as semantic search when the workflow needs totals, dates or identifiers.
• Building a vector database before the product’s document behaviour is understood.
• Sending raw files directly to the model instead of a consistent representation.
• Ignoring document relationships: an invoice, an order and a delivery note are often one case.
• Treating missing information as a negative answer. Unknown is not the same as false.
• Allowing unsupported actions on financial, legal or customer systems.
• Hiding the evidence behind the result.

The future of RAG for agents

The future is unlikely to be one universal retrieval architecture. The emerging pattern is a tool-driven information layer: document extraction, multi-document processing, structured JSON, Markdown context, search, SQL, APIs, knowledge graphs and human review. Vector databases and embeddings remain part of this ecosystem, selected for specific retrieval tasks rather than mandatory infrastructure for every agent.

Traditional RAG retrieves similar text. Agentic RAG gives agents the tools to obtain, compare and use the right information for the task. For developers building document-aware agents, the strategic question is no longer which vector database to use. It is which document and information capabilities the agent should be able to call.

Frequently asked questions

What is RAG for AI agents? RAG for AI agents gives an agent access to external information through retrieval or document tools while it reasons and acts. The agent can decide when to retrieve, which source to use and whether more information is needed.

What is agentic RAG? Agentic RAG is a dynamic retrieval architecture in which the agent controls when and how to obtain external context. It can call tools, refine queries, compare results and continue until it has enough information to complete the task.

Is agentic RAG replacing traditional RAG? Agentic RAG is becoming the preferred pattern for complex agent workflows, but traditional vector RAG remains useful for large, persistent semantic-search collections. The two approaches are often combined.

Are vector databases or embeddings obsolete? No. They remain useful for large-scale semantic retrieval and similarity discovery. They are not required for every document-agent workflow. Structured extraction, direct document processing, SQL and APIs may be more appropriate.

Is chunking the past? Fixed chunking is becoming less universal. Structure-aware extraction, full-document processing, structured outputs and tool-based document operations can be better options depending on the task.

What are alternatives to vector database RAG? Structured extraction, direct document querying, multi-document processing, SQL, keyword search, knowledge graphs, APIs, full-document context and agent tools.

What are alternatives to LlamaIndex? Direct model tool calling, LangChain, LangGraph, Mastra, Haystack, DSPy, custom agent runtimes and managed document-processing APIs. The right choice depends on whether the main need is indexing, retrieval, orchestration or document processing.

Can I build an agent without a vector database? Yes. You can use direct document APIs, structured extraction, multi-document processing, SQL or other tools. A vector database is necessary when the use case genuinely needs large-scale semantic retrieval.

Is Claix a replacement for LlamaIndex? Claix is a document-processing layer that can be used with a custom agent runtime or with frameworks such as LangChain and LangGraph. It does not replace every capability of a complete agent framework.

How does Claix fit into agentic RAG? Claix can provide document tools for extraction, Markdown conversion, JSON output, context retention and cross-document processing. The agent decides when to use those capabilities and what action to take with the result.