Expert Coordinator Agent
The Expert Coordinator Agent (the expert-asking agent) is the bridge between your AI agents and your human experts. When a question can't be answered automatically, this agent posts it to a designated expert channel on Microsoft Teams or Slack, waits for a person to reply, checks whether the reply actually answers the question (asking follow-ups if not), and then saves the answer into organization memory so it can be reused automatically next time.
It is usually not something end users chat with directly. Instead it works behind the scenes — most often invoked by the Company Knowledge Agent when that agent runs out of documented answers. Its value is knowledge capture: every expert consultation turns a one-off human answer into reusable organizational knowledge.
What it does
- Post to the expert channel. The agent sends the question to the configured Teams or Slack channel where your experts are.
- Wait for a human reply. A person answers in that channel, in their normal workflow — no special tool to learn.
- Check the answer. A language model judges whether the reply genuinely answers the question or whether the expert declined / answered only partially.
- Ask a follow-up if needed. If the answer falls short, the agent composes a targeted follow-up question and posts it back to the channel — repeating up to a configurable number of rounds.
- Save and return. Once the answer is sufficient, the agent stores the question-and-answer pair in organization memory and returns the answer to whoever asked (typically the Company Knowledge Agent, which relays it to the user).
This is "bot-in-the-loop"
Where other agents pause for the end user (human-in-the-loop), the Expert Coordinator pauses for a different human — a subject-matter expert reached through a chat channel. The original user doesn't wait in the chat; they're told the question was forwarded and will be answered when an expert replies.
Knowledge capture
The reason to use this agent rather than just emailing an expert is that each answer is captured. When an expert answers once, the Q&A is written to organization memory and becomes searchable for everyone. The next time the same topic comes up, the Company Knowledge Agent can answer it from that stored knowledge without bothering the expert again — so the expert's effort compounds over time instead of being lost in a chat thread.
Typical scenarios
- Engineering escalation. A Company Knowledge Agent can't find a documented answer about an internal system, so it consults the engineering team's Teams channel; the answer is captured for future questions.
- Specialist desk. A small group of subject-matter experts fields the genuinely novel questions while the AI handles everything already documented.
- Building the knowledge base from conversations. Over time, expert answers accumulate in organization memory, steadily reducing how often a human needs to be involved.
Before you start: prerequisites
- A connected Teams or Slack bot. The agent reaches experts through a bot registered on your collaboration platform. That bot connection must be set up first — see Slack & Teams integration setup. You'll need the channel and bot identifiers from that setup to fill in the configuration below.
- An expert channel. A Teams or Slack channel where your experts are present and willing to answer questions.
- A chat model for checking answers and composing follow-ups, available through your 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:
- Open the blueprint under Admin > Agents > Blueprints and select Expert Coordinator Agent.
- Create a profile with an Agent ID, Name, Description, and Icon.
- Choose the channel (Teams or Slack) and fill in its identifiers (see the configuration reference).
- Set the organization-memory target so captured answers are written where your knowledge agents can read them.
- Choose the chat model and, if you like, adjust how many follow-up rounds are allowed.
- Save. The profile can now be targeted by a Company Knowledge Agent (or invoked directly).
Channel configuration lives in the form, not in environment variables
In earlier versions the channel was set through environment variables (EXPERT_ASKING_CHANNEL_TYPE, TEAMS_CHANNEL_ID, SLACK_CHANNEL_ID, …). That is no longer the case — all channel settings are now part of the agent's configuration form and are edited per profile in the Admin UI. If you find references to those environment variables elsewhere, treat them as outdated.
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. |
| Description | Text (per language) | Yes | Short explanation of what this expert profile is for. |
| Icon | Icon picker | No | Visual identifier. |
Expert channel
Pick the platform, then fill in that platform's fields. The identifiers come from your bot integration setup.
| Field | Type | Default | Description |
|---|---|---|---|
| Channel Type | Choice | teams | Which platform to use: Microsoft Teams or Slack. |
When Channel Type is Microsoft Teams:
| Field | Type | Required | Description |
|---|---|---|---|
| Channel ID | Text | Yes | The Teams channel ID (format like 19:…@thread.tacv2). |
| Tenant ID | Text | Yes | Your Azure AD tenant ID (a UUID). |
| Bot ID | Text | Yes | The Teams bot's application ID (a UUID). |
When Channel Type is Slack:
| Field | Type | Default | Description |
|---|---|---|---|
| Channel ID | Text | — | The Slack channel ID (starts with C). |
| Service URL | Text | https://slack.botframework.com | Bot Framework service URL. Use the EU endpoint (https://europe.slack.botframework.com) for EU data residency. |
Follow-up behaviour
| Field | Type | Default | Description |
|---|---|---|---|
| Max loops | Number | 3 | How many times the agent may ask a follow-up when the expert's answer is incomplete. Range 1–10. |
Language model
| Field | Type | Default | Description |
|---|---|---|---|
| Model | Model picker | — | The chat model used to judge answer sufficiency and write follow-up questions. Required. |
| Temperature | Number | 0.0 | Keep low — this is a judgement/extraction task, not a creative one. Range 0.0–2.0. |
| Return Log Probabilities / Top Log Probabilities / Timeout | — | — | Standard language-model options, as on the Document Intelligence Assistant page. |
Organization memory
Where captured expert answers are written. Read by knowledge agents (such as the Company Knowledge Agent) so answers are reused automatically.
| Field | Type | Default | Description |
|---|---|---|---|
| Tenant ID | Text | platform default | Which tenant's shared memory to write to. |
| Allowed namespaces | List | (empty) | Allow-list of namespaces. Empty means unrestricted. Also validates the write target. |
| Default namespace | Text | platform default | The namespace answers are written to when a request doesn't specify one. Must be in the allow-list if one is set. |
| Organization Memory Format | Long text | Question: {question}\n\nExpert Answer: {answer} | Template for the stored snippet. Use {question} and {answer} placeholders. |
Best practices
Put real experts in the channel. The agent is only as good as the people answering. Choose a channel where knowledgeable colleagues are present and willing to help.
Keep the namespace consistent with your knowledge agents. For captured answers to be reused, write them to a namespace that the Company Knowledge Agent (or a RAG agent) actually reads from.
Tune the follow-up limit to your experts' patience. Two or three rounds is usually enough; too many can feel like an interrogation to the human answering.
Pair it with a Company Knowledge Agent. On its own this agent just relays questions. Its real power comes from being the escalation target of a Company Knowledge Agent, which only consults it when its own documents fall short.