Company Knowledge Agent
The Company Knowledge Agent (the expert-RAG agent) is the Document Intelligence Assistant with a human safety net. It answers questions from your documents exactly like the Document Intelligence Assistant — and when it can't find a good enough answer, instead of giving up it offers to ask a human expert. With the user's permission, it hands the question to an Expert Coordinator Agent, which reaches a person on Slack or Teams, and then answers the user using the expert's reply.
It combines two strengths: the speed and scale of document-based answers, and the reliability of a human for the questions your documents don't cover yet. And because expert answers are captured into organization memory, the agent gradually needs the human less often.
When to reach for this agent
Use it when document coverage is incomplete and a wrong or missing answer matters — you'd rather involve a human than have the agent guess or say "I don't know." If your documents fully cover the topic, the plain Document Intelligence Assistant is simpler and sufficient.
What it does
It runs the full RAG workflow first; the expert path only opens when the retrieved context isn't enough to answer.
- Try to answer from documents. It performs the same retrieve → rerank → check-sufficiency flow as the Document Intelligence Assistant. If the documents are sufficient, it answers with citations and stops — identical to a normal RAG answer.
- Detect a gap. If the context-sufficiency check decides the retrieved material isn't enough, the expert path begins.
- Ask the user's permission. The agent tells the user it doesn't have the answer and asks whether it may consult an expert. Nothing is forwarded without consent. If the user declines, the agent simply responds with what it has.
- Consult an expert. On approval, it delegates the question to the configured Expert Coordinator Agent, which posts it to a human on Slack or Teams. The user is told their question has been forwarded.
- Answer with the expert's reply. When the expert answers, that reply is folded back in as context and the agent composes the final answer. (The Expert Coordinator also stores the answer in organization memory, so next time the agent can answer it directly.)
Two kinds of human involvement
The Company Knowledge Agent uses human-in-the-loop for the user's consent ("may I ask an expert?"), and the Expert Coordinator it delegates to uses bot-in-the-loop to reach the expert on a chat channel. Together they keep the escalation transparent: the user always approves before a colleague is contacted.
What it does not do
- It doesn't contact experts directly. All human contact happens through the Expert Coordinator Agent it delegates to — which must be configured separately.
- It doesn't escalate without consent. The user is always asked first; declining keeps the conversation purely document-based.
- It doesn't ingest documents. Like all RAG agents, it reads what a pipeline has indexed.
Before you start: prerequisites
This agent sits on top of two others, so both of their setups must be in place first:
- Everything the Document Intelligence Assistant needs. A populated knowledge base, a matching embedding model, and a chat model — see that agent's prerequisites. The Company Knowledge Agent shares all of the same retrieval configuration.
- A configured Expert Coordinator Agent profile. The escalation target must already exist and work, including its connected Teams/Slack bot. Set up and test the Expert Coordinator Agent first — the Company Knowledge Agent is useless without one to delegate to.
Setting it up
The agent is delivered as a blueprint from which you create configured profiles — see Blueprints & Profiles. With the prerequisites in place:
- Open the blueprint under Admin > Agents > Blueprints and select Company Knowledge Agent.
- Create a profile and configure it exactly like a Document Intelligence Assistant: profile identity, chat model, at least one knowledge source, and any reranking, guards, prompts, and memory you want.
- Turn on the context-sufficiency guard. Expert escalation is triggered by the sufficiency check deciding the context isn't enough — so this guard should be enabled, or the agent will not escalate.
- Select the Expert Agent. Choose the Expert Coordinator Agent profile to escalate to. This is the one setting unique to this agent.
- Save and test with a question your documents can answer (expect a normal cited answer) and one they can't (expect the consent prompt and escalation).
Configuration reference
The Company Knowledge Agent has the entire configuration of the Document Intelligence Assistant — profile identity, language model, knowledge sources, reranking, context-sufficiency guard, suitability guard, user memory, organization memory, prompts, and input budget. Refer to that page for all of those fields; they behave identically here.
On top of that, it adds a single new setting for escalation:
Expert escalation
| Field | Type | Required | Description |
|---|---|---|---|
| Expert Agent | Agent picker | Yes | The Expert Coordinator Agent profile to consult when the documents don't have a sufficient answer. The picker lists agents that can take expert questions. |
Enable the context-sufficiency guard
Escalation only happens when the context-sufficiency guard decides the retrieved context isn't enough. If that guard is left off, the agent will almost always answer from documents alone and never reach the expert path — defeating the purpose of this blueprint.
Best practices
Get both underlying agents working first. This is a composition of a RAG agent and an expert agent. Confirm your Document Intelligence setup answers well, and your Expert Coordinator reaches a real human, before combining them here.
Enable the sufficiency guard and tune it. It's the trigger for escalation. Too lenient and the agent guesses instead of asking; too strict and it escalates trivial questions. Test with real questions and adjust.
Point escalation and memory at the same namespace. For captured expert answers to come back as document-free answers later, the Expert Coordinator must write to a namespace this agent's organization memory reads from. Keep them aligned so the knowledge base genuinely grows from each consultation.
Set expectations in the system prompt. Since some answers will arrive only after a human replies, make sure the assistant's wording (and your users' expectations) account for the "I've forwarded this to an expert" path.