Document Navigation Assistant

The Document Navigation Assistant (the namespace-selection agent) is a router that sits in front of a Document Intelligence Assistant. When your organization has many separate knowledge collections — HR, Legal, Engineering, Sales, and so on — searching all of them for every question produces noisy, mixed answers. This agent first works out which collections a question belongs to, confirms that choice with the user, and only then hands the question off to a RAG agent that searches just those collections.

It does not retrieve or answer questions itself. Its job is navigation: pick the right sources, then delegate.

When to reach for this agent

Use it when you have several distinct knowledge bases and a single RAG agent searching all of them would mix unrelated material. If you only have one knowledge base, or a RAG agent searching everything works well enough, you don't need this — just use the Document Intelligence Assistant directly.

Knowledge bases and namespaces

To understand this agent you need two terms:

• A knowledge base (or bucket) is a top-level collection of documents — for example company_policies.
• A namespace is a labelled sub-collection within a knowledge base — for example hr-policies, legal, or benefits inside company_policies.

The Document Navigation Assistant chooses one namespace per knowledge base that best matches the user's question, and passes that selection to the RAG agent so its search is restricted to exactly those namespaces.

What it does

1. Work out the right sources. The agent looks up the namespaces available in the configured knowledge bases and uses a language model to decide which ones the question is about.
2. Ask if unsure. If the question is too vague to choose confidently, the agent asks the user a clarifying question (for example, "Are you asking about employee or customer policies?") and refines its choice from the answer. This can repeat until it is confident.
3. Confirm with the user. Before searching, the agent shows the user the sources it intends to use and asks for approval. If the user rejects them, it proposes a different selection.
4. Hand off to the RAG agent. Once approved, it delegates the question to a configured Document Intelligence Assistant, telling it to search only the chosen namespaces. The RAG agent's answer is passed straight back to the user.

The chosen sources are remembered for the rest of the conversation. Follow-up questions in the same chat skip the selection and approval steps and go straight to the RAG agent with the same sources — so the user only navigates once.

Steps 2 and 3 are human-in-the-loop

The clarifying question and the approval step pause the workflow and wait for the user. This is what keeps routing accurate and transparent: the user always sees and confirms which knowledge sources will be searched before any answer is generated.

What it does not do

• It doesn't retrieve or answer. All searching and answering is done by the RAG agent it delegates to. This agent only decides where to look and confirms it with the user.
• It needs a RAG agent to delegate to. On its own it produces no answers — a configured Document Intelligence Assistant profile is a hard requirement (see below).

Typical scenarios

• Enterprise with siloed knowledge. Engineering, Sales, HR, and Legal each have their own documents; the assistant routes each question to the right team's namespace instead of searching all of them.
• Topic-specialized platforms. A medical knowledge platform with cardiology, neurology, and oncology namespaces asks which specialty applies before answering.
• Reducing noise and cost. Anywhere a "search everything" RAG agent returns mixed or contradictory passages, narrowing the search to the right namespace produces cleaner answers and cheaper retrieval.

Before you start: prerequisites

1. A configured RAG agent profile. This is the single most important prerequisite. The Document Navigation Assistant delegates every answer to a Document Intelligence Assistant profile, so that profile must already exist and work. Set it up and test it first.
2. Knowledge bases organized into namespaces. There must be more than one namespace to choose between for routing to be meaningful. Namespaces are defined when documents are ingested by the data pipelines.
3. A chat model for the routing language model, available through your platform's LiteLLM configuration.

Setting it up

The agent is delivered as a blueprint from which you create configured profiles — see Blueprints & Profiles. With the prerequisites in place:

1. Open the blueprint under Admin > Agents > Blueprints and select Document Navigation Assistant.
2. Create a profile with an Agent ID, Name, Description, and Icon.
3. Choose the chat model used to work out which sources a question belongs to.
4. Select the knowledge databases the assistant is allowed to route between. Their namespaces become the choices it picks from.
5. Select the RAG agent to delegate to. Pick the Document Intelligence Assistant profile that will do the actual searching and answering.
6. Optionally customize the approval message shown to users and how much conversation history the router keeps.
7. Save and test. Ask questions that should land in different namespaces and confirm the routing and approval behave as expected.

Configuration reference

Profile identity

Field	Type	Required	Description
Agent ID	Text	Yes	Unique, URL-safe identifier. Lowercase letters, digits, underscores, hyphens.
Name	Text (per language)	Yes	Display name shown to users.
Description	Text (per language)	Yes	Short explanation shown in the assistant picker.
Icon	Icon picker	No	Visual identifier.

Routing

These settings control how the assistant decides where to search and where it sends the question.

Field	Type	Default	Required	Description
Model	Model picker	—	Yes	The chat model used to work out which namespaces a question belongs to.
Knowledge Databases	Knowledge-base picker	—	Yes	The knowledge bases the assistant may route between. Their namespaces are the options it chooses from. At least one.
RAG Agent	Agent picker	—	Yes	The Document Intelligence Assistant profile that questions are delegated to. The picker lists agents that accept RAG-style requests.

The Model picker also exposes the standard language-model parameters (temperature, timeout, and the log-probability options) described on the Document Intelligence Assistant page; a low temperature is best, since routing should be consistent.

Conversation behaviour

Field	Type	Default	Description
Max history entries	Number	20	How many recent conversation turns the router keeps while working out the right sources. The original question is always kept. Minimum 4, maximum 100.
Approval message template	Long text	(default provided)	The message shown when asking the user to approve the chosen sources. Use the {namespaces} placeholder where the proposed list should appear.

Best practices

Build and test the RAG agent first. This assistant is only as good as the Document Intelligence Assistant it delegates to. Get that working on its own before putting a navigator in front of it.

Give namespaces clear, descriptive names. The routing model decides based on namespace names and descriptions — hr-policies and customer-contracts route far more reliably than docs1 and docs2.

Keep the routing model's temperature low. Routing is a classification task; consistency matters more than creativity.

Use it only when you genuinely have multiple knowledge bases. For a single knowledge base it adds an approval step with no benefit — use the Document Intelligence Assistant directly instead.

Tune the approval message for your users. The default asks for confirmation generically; a message phrased for your audience makes the navigation step feel natural rather than technical.