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Atlas/Dust
Vendor dossier / Horizontal AI

Dust

Collaborative enterprise AI workspace for teams to build and use connected agents.

REVIEWED 23 SEPTEMBER 2026Marketing / Sales / CXNO HANDS-ON BENCHMARK

What it is

Official-source notesHorizontal AIscaling

Dust explicitly markets multiplayer human–agent collaboration. Pro and Max differ in included credits; Max is €150 monthly or €120 on annual billing, with 40,000 credits per seat monthly. Enterprise adds pooled credits, SCIM and audit-log features. [V1] [V2]

Documented means the statement is present in the cited material. Capability effectiveness, customer outcomes and security assurances have not been independently audited here.

Promoted use cases

  • Research accounts and prepare proposals
  • Create campaign briefs and performance narratives
  • Triage support and synthesize company knowledge

Horizontal platforms compete on how people connect company knowledge, delegate work and keep control across tools. The central question is whether a reusable workspace can become an operating process, with a named owner and a reliable path from request to approved action. A visually appealing agent builder is only one part of that decision.

Pricing and buying model

Published commercial evidence

€30 / seat / month

Pro monthly: 8,000 credits. Annual: €24/seat/month equivalent. Excludes VAT; Enterprise custom.

Check current commercial source ↗

Budget for implementation, model and tool usage, data, human review and ongoing support. Credit units and outcome definitions differ between vendors. A missing numeric rate is marked unverified rather than replaced with an old third-party estimate.

Collaboration and governance

Dust describes separate permissions for agent data access and agent use, plus enterprise retention and deployment options. Validate the selected plan and connector behaviour.

The relevant unit of control is permission to update the CRM and release approved follow-up. Inspect who can propose, approve, execute, interrupt and audit that action. Shared seats, shared content and a shared live agent session are different capabilities; require a demonstration of the one your process needs.

Evaluate memory correction, permission revocation and release control for updated instructions. An improvement loop should preserve the original evidence, proposed change, test results and named approver. A safety or security badge alone cannot establish those workflow properties.

Review and customer evidence

G2’s September 2026 seller-invited review by Thibault G. praises model choice, connected knowledge and scheduled automation. A separate 2025 review sample praises reusable agents and support while raising onboarding, documentation and editing concerns. Those historical complaints require re-testing against today’s product. These are attributed experiences, not a representative benchmark or a verified guarantee of results.

Before relying on a testimonial, confirm the exact product, version, package, workload and baseline. Ask a relevant customer about setup effort, failed cases, ongoing manual work and support after launch. This dossier does not convert customer logos or vendor-hosted awards into independent proof.

Focused feature documentation

Focused documentation check · Sep 2026

Pods: what shared context actually contains

Dust documents a Pod as a workspace with conversations, tasks and files. Members can contribute to the same conversations, and those conversations are indexed for later agent context. Agents can post, create or complete tasks, and save files. This supplies concrete evidence for multi-person and human–agent participation in a persistent shared container.

Pods can be open within a workspace or restricted to invited members. A Pod Editor has additional control over members and settings. The documentation does not by itself establish how an external connector reacts when a participant loses access; request a revocation demonstration. Compare that behaviour with Nagent’s actual workspace once its administrative evidence is supplied.

Primary sources: Official Pods overview ↗ · Official conversations documentation ↗

Published documentation and public illustrations; account behaviour was not independently tested.

Visual source

Public Dust source page captured 22 September 2026
Dust · Public page captured 22 Sept 2026. Vendor presentation, not an authenticated product test. Open source ↗

Fit, limitations and proof requests

Editorial assessment

Dust is a direct horizontal workspace comparator. Its self-service, published seat economics are easier to inspect than a custom managed engagement. Whether that workspace covers a particular end-to-end growth process still depends on configuration, connectors and accountable operators.

Priority question: Show permission propagation and revocation across a shared conversation, a connected source and a scheduled agent.

The evaluation owner should be a growth operations lead. Use accepted work completed across tools as the business target. Judge the solution in its own stack role: a cloud runtime, a specialist production tool and a managed AI team can be complementary purchases.

Use-case evaluation plan

Account research to approved follow-up

Give the team an account record, a current product brief and a small set of approved customer references. Ask it to identify a relevant problem, distinguish observed facts from inferred needs, and prepare a follow-up for review. Then change one source and require a second person to correct the draft without restarting the research.

Measure: source accuracy, reviewer minutes, approved follow-ups and CRM consistency.

Campaign brief to coordinated delivery

Start with one campaign objective, audience, budget constraint and brand guide. Require research, a brief, assets and a release checklist with distinct human owners. Introduce an unresolved claim midway through the process. The system should retain useful work while preventing that claim from silently propagating into every asset.

Measure: accepted assets, revision burden, approval latency and missing handoffs.

Customer insight to shared learning

Provide anonymized support themes and sales objections. Ask for a synthesized insight, a proposed campaign adjustment and a knowledge update. A marketing owner and a CX owner should review the same underlying evidence. The exercise tests cross-functional context and accountability, not merely summarization quality.

Measure: evidence coverage, disagreement resolution and reuse of approved learning.

Detailed comparison

Read the detailed Nagent vs Dust guide for operating models, shared context, governance, cost, evidence gaps and a staged pilot.

Nagent vs Dust

Sources and evidence register

Nagent · Multiplayer AI for growth teamsResearch: 21–29 September 2026 · Public-source analysis, no hands-on benchmark · Sources & method