Dust AI vs Runbear (2026): Internal AI Workspace or Customer-Facing Agents?
Dust governs AI inside the company; Runbear deploys bounded agents into the customer channel. A 2026 comparison that routes by operating boundary, not feature count.
Dust and Runbear both connect AI agents to workplace tools and trigger automated work. Teams lose a week comparing model lists and integration counts without answering the question that actually changes the implementation: who uses the agent, where do they meet it, and who owns the boundary when it acts?
TL;DR
Dust governs AI inside the company. Start here if your first workflow is internal knowledge work.
Runbear puts a named agent in the Slack channel you already share with customers, who need no account. It can also deploy an existing Claude Code project as-is.
Both cover both cases. The difference is the default path each product optimizes, not a feature count.
The short answer: choose by operating boundary
Start with
When your first workflow is
What the pilot must prove
Dust
Internal knowledge work across employees and teams
The right employees reach the right agents without exposing restricted knowledge
Runbear
A customer-facing workflow in a shared messaging channel
One request goes from channel message to grounded answer or human handoff without leaving the thread
This is a routing recommendation, not a feature score. Both products reach Slack, Microsoft Teams, schedules, and webhooks. What differs is the default implementation path each product emphasizes, not the existence of a single integration.
Where Dust is strong: internal AI governance
Dust is built around a company workspace, and its control surfaces are the product rather than overhead:
Spaces separate access to agents, data, and tools, and restricted Spaces can be scoped to selected groups or individuals.
Workspace roles distinguish administrators, builders, and regular users.
Workspace analytics let an administrator review adoption across the workspace.
Builders connect company knowledge and tools, and Sidekick refines an agent through conversation.
That matters when the rollout question is organizational: who may discover an agent, who owns its instructions, and whether an administrator can tell if it is being used. Dust's setup boundaries are visible too. Its Slack Workflow documentation describes a product-side enablement step and asks you to name the relevant Space when a restricted agent is used. That is a governance decision showing its work, and it belongs in the deployment plan rather than in a complaint column.
One observation from setting up a workspace ourselves: nothing in Dust's onboarding pointed toward Slack, and the Slack connection took several minutes to locate. Read that as a statement about where the product's center of gravity sits rather than as a defect. Dust would rather host the conversation on its own dashboard, which is coherent with the rest of its design.
Where Runbear is direct: the customer channel
Runbear's default path starts closer to the external conversation. You create an agent, connect approved knowledge and actions, and deploy it into the Slack, Microsoft Teams, or Discord channel where customers or partners already talk. End users do not need a Runbear account, which is what makes candidate-facing and other external surfaces possible at all.
Two mechanics make that practical. Agents are named, so a customer mentions @Support or @Onboarding the way they would mention a colleague, with no interface to learn and no dropdown to pick from. And an agent that already exists as a Claude Code project can be deployed as it stands: its CLAUDE.md, skills, and subagents ship to a hosted agent you then connect to Slack, instead of being re-expressed by hand in a builder.
For a CSM, that changes the first design questions:
Deployment step
Dust-first internal workspace
Runbear-first customer workflow
1. Access
Workspace, Space, group, and agent visibility
Which channel, which conversation, and who may invoke the agent
2. Grounding
Internal knowledge and tools for permitted members
Only the sources and actions approved for this workflow
3. Exposure
Available through Dust or a configured integration
Added to the shared Slack, Teams, or Discord surface
4. Uncertainty
Internal review and ownership conventions
Refuse unsupported claims, hand the thread to a named owner
5. Observation
Workspace adoption and usage
Whether real customer threads were answered, escalated, or dropped
The distinction that matters is completion. "The agent ran" is a receipt. The operational result is that the customer holds a grounded answer in the shared thread, or a person holds the request with enough context to continue.
What a bounded pilot looks like
Take one narrow onboarding workflow. A customer asks in the shared channel whether their annual subscription is active, and two accounts share a similar name. The right behavior is not a confident guess. It is to say so, ask for the one detail that disambiguates, and offer the handoff.
Four things are worth capturing from a pilot like this: the reply stayed in the same conversation, the answer named its approved source, the unsupported claim was refused instead of guessed, and the named owner received the thread with its context. That is deliberately smaller than "deploy an AI support system," and it is what makes the boundary testable.
Seven checkpoints worth more than a demo
A polished demo hides the work that decides whether an agent is safe to operate. Run the same workflow through both products and write down:
Identity. Who creates, owns, invokes, and reviews the agent?
Authorization. Which people, channels, knowledge sources, and actions are allowed?
Authentication. Which account connects each external service, and what happens when that connection expires?
Trigger. What exact event starts the workflow: a mention, a message, a schedule, a webhook, or a workflow step?
Completion. What user-visible outcome counts as done?
Failure. Where is an unsupported answer refused, and who receives the handoff?
Observation. Can the team reconstruct what happened without treating an API receipt as success?
Frequently asked questions
Is Dust or Runbear better for customer-facing AI agents?
Runbear is the more direct path when the agent has to meet customers or partners in a shared Slack, Microsoft Teams, or Discord channel, because end users do not need an account and the agent is deployed into the conversation itself. Dust is built around an internal company workspace, so a customer-facing rollout has to be designed around workspace membership.
Can Runbear also handle internal workflows?
Yes. Runbear runs internal workflows in the same way it runs external ones, and Dust likewise supports Slack, Microsoft Teams, schedules, and webhooks. The comparison is about which path each product makes shortest by default, not about which capabilities exist.
Can I move an agent I already built in Claude Code?
Into Runbear, yes. One deploy command from the project directory ships your CLAUDE.md, skills, subagents, docs, and code to a hosted agent that you connect to Slack, so the setup one person tuned locally becomes a teammate the whole team can mention.
Dust is a different shape here, and it is worth being exact about it. Dust runs Claude models, selectable per agent across Opus, Sonnet, and Haiku, so you can absolutely
Dust is a different shape here, and it is worth being exact about it. Dust runs Claude models, selectable per agent across Opus, Sonnet, and Haiku, so you can absolutely build on Claude there. Its CLI also runs a local MCP server that exposes Dust agents to Claude Code and Cursor. What that CLI does is the reverse of an import: it brings Dust agents into your terminal, not your terminal project into Dust. There is no path that takes the Claude Code project you already have and runs it as a hosted Dust agent, so the instructions get rebuilt in the agent builder and Claude Skills get recreated as Dust Skills. If your working agent already lives in a repo, that rebuild is the cost to weigh.
What is a named agent, and why does it matter outside the company?
A named agent is a distinct teammate with its own name, instructions, knowledge base, and tools, addressed directly in a channel: @Support, @Onboarding, @Billing. It matters most with people outside your company, because a customer or vendor cannot be trained on your internal tooling. Mentioning a name in a thread they already use is the only interface they will reliably adopt.
How should we compare the two without running a long evaluation?
Pick one recurring request, run it through both products, and record the seven checkpoints above: identity, authorization, authentication, trigger, completion, failure, and observation. That comparison takes days rather than weeks and surfaces the operating boundaries a demo hides.
Which should you start with?
Start with Dust when the problem is coordinating internal AI across employees, shared knowledge, builders, administrators, and access-controlled Spaces. Its governance model is a central product strength, not overhead.
Start with Runbear when the problem is deploying a bounded workflow into customer or partner conversations, especially when a CSM owns the outcome and the agent has to answer or escalate in the same channel.
If both matter, do not force one pilot to prove both. Pick one recurring request that eats CSM time, and limit it to one channel, one approved source set, one escalation owner, and one definition of done.
Disclosure: this article was written by the Runbear team. Rather than taking the conclusion at face value, run the same deployment workflow in both products and record the operating boundaries you encounter.
Design in Claude Code. Keep It Running in Runbear.
Build and improve a Runbear agent from Claude Code, then keep the approved teammate connected to apps, triggers, Slack permissions, and run history for the whole team.
