Nagent vs Dust: the room and the ladder

Nagent is Multiplayer AI for end to end growth: a team of AI coworkers and your own people, working together in one workspace across marketing, sales and customer experience. Three things make it different. You approve the AI coworkers' work until they earn the right to act on their own. They carry the work all the way to pipeline and customers, not just content. And where your plan includes it, a Nagent marketer joins your team and owns the number with you.

Most companies that bought AI seats over the last two years now live with a peculiar kind of mess. Everyone has an assistant. Nobody has a colleague.

The head of SEO asked a model to diagnose a crawl problem on Thursday. The content lead asked a different model to rewrite category pages on Friday. On Monday the two answers contradict each other, neither conversation can be found, and the person who has to decide what ships is reading a forwarded screenshot of a chat window. The work happened. The organisation learned nothing from it, and nobody can say who approved what.

Dust saw this problem earlier and named it more clearly than almost anyone. Its homepage now describes the company as multiplayer AI for collaboration between humans and agents, and its Series B announcement in May 2026 makes the case that the people who change companies with AI will be the operators closest to the work, building and sharing agents in the open rather than hoarding them in private windows. Dust raised USD 40 million on that argument from Abstract, Sequoia, Snowflake Ventures and Datadog, and Sifted reported that it had crossed USD 20 million in annual recurring revenue earlier in the year.

Nagent arrived at the same Monday morning from a different door. We spent 2025 running agentic growth programmes for brands, and one of them, a campaign for a leading Indian FMCG brand that drew more than 24.5 million views across eight audience personas, taught us something uncomfortable. Producing the work was no longer the hard part. Our agents could write, design and draft faster than any team could review. The hard part was deciding: which of hundreds of assets should ship, who was allowed to publish, and who would answer when the numbers moved. Agents did not fail at generating work. They failed at being accountable for it.

So both companies put people and AI agents in the same room. Dust built a room where any team can build agents for any job. Nagent built a room for one job, growth, and filled it with a team that is already hired. If growth is your problem, that difference decides everything.

Start with the scale, because it is real. By Dust's own account, more than 3,000 organisations use it and the people closest to the work have deployed more than 300,000 agents. Clay uses it as knowledge infrastructure for a fast growing GTM team. Persona has agents running across eleven departments. The customer list reads like a roll call of the companies other software companies copy: Vanta, Doctolib, Profound, Datadog, 1Password.

The product underneath that adoption has three ideas worth understanding.

The first is the Pod. Launched at the end of May 2026, a Pod is a shared workspace where a team of humans and agents works on one initiative, with conversations, files and tasks in one place and everything continuously indexed so the next conversation starts where the last one ended. Dust's documentation states the principle directly: everything a human can do in a Pod, an agent can do. Agents start conversations, pick up and complete tasks, save files and post into the Pod, with or without a person in the loop. Since then Dust has let triggered agents run inside Pods, let editors set a default agent and default skills per Pod, and let almost any file become a Pod tab.

The second is skills that get better on their own. Every night, Dust's self improving skills feature reads the conversations that used a skill, looks at explicit signals such as thumbs down and implicit ones such as a person correcting the agent, and proposes a revised version of the skill. An editor approves or declines the change. It is a thoughtful design: learning happens, but a human signs off on what is learned.

The third is reach. On its website, Dust says it connects to a long list of company systems, runs more than twenty frontier models, and meets people where they already work: Slack, Microsoft Teams, Zendesk, a browser extension, email and any MCP client. An agent built once in Dust can be called from wherever the conversation happens to be.

Around all of this sits the enterprise machinery that large buyers ask for first: SOC 2 Type II, data residency in the EU or the US, single sign on and SCIM provisioning, and a permissions model built on Spaces.

If you wanted to describe Dust in a sentence, it would be this: a horizontal platform on which any team can build, share and improve agents for any job, with the context those agents need already indexed. That breadth is the point. Dust does not want to decide what your agents do. It wants every operator in your company to be able to build the agents they need, and it has become very good at making that easy.

In Dust, an administrator sets a stake level for every action of every tool. Reading email never asks. Drafting asks once. Sending asks every time. It is clean and sensible for a platform that does not know what agents you will build.

But the stake belongs to the tool, not to the agent. An agent that has done flawless work for three months and an agent built this morning are trusted exactly the same way. And building the agents, deciding what they are for and answering for what they produce is still your job. Dust's own growth marketer describes building his outbound agents himself, one by one. That works brilliantly if you have that person. Most growing companies do not.

Go back to that Monday mess: two contradictory AI answers, no record of either conversation, and nobody sure who approved what.

In Nagent, the work never leaves the room. Your people and your AI coworkers share one workspace, organised into teams for marketing, content, sales and customer experience, and every piece of work is recorded where everyone can see it. The first screen each morning shows what the team did overnight and the few decisions that need a person.

Now the crawl problem from Thursday. In our demonstration workspace, the content lead tells the AI coworker for search that thousands of product pages have vanished from Google overnight. It finds the cause within minutes. But it does not fix it on its own, because changing the website's structure is a human decision. It sends one card to the lead: what it proposes, what is at stake, the risk, and whether the change can be undone.

She approves. A week later almost every page is back on Google and traffic is up. On Monday, nobody has to ask who approved what. It is on the record.

And here is the part Dust's stakes cannot express. Because the AI coworker handled that call well, it earns more freedom. Every AI coworker starts like a new hire, with everything it does waiting for a person. As it proves itself, Nagent recommends a promotion and a person signs it off. A promoted coworker needs you less. One that slips loses the freedom it had.

You also do not build the team. It arrives hired: AI coworkers for search, paid ads, social, content, outbound and customer experience, with an AI chief of staff called MIRA who plans the week and hands out the work. When a buyer shows interest, NORA, the AI coworker for outbound, researches the account and drafts a first message, and keeps the record in a CRM that comes with Nagent. Your sales lead reads it before anything is sent. And where your plan includes it, a Nagent marketer sits in the same workspace and owns the number with you.

Dust's chief executive has said that by 2027 companies will need to understand what their agents are doing, who is responsible for them and whether their decisions remain reliable. We agree. Multiplayer was the first question. Accountability is the next one, and it is the one Nagent was built to answer today.

Dust details are taken from Dust's website, documentation and a free Dust workspace, September 2026. Nagent examples come from Ridgeline, a demonstration workspace for a fictional manufacturer.

If your problem is growth and you want a team rather than a toolkit, see the plans on the pricing page, or book a walkthrough with the team. To see list prices at three team sizes, read the cost table in the Dust guide.

• Nagent vs Dust: the sourced comparison guide, with dated screenshots
• Dust alternatives: the other platforms in its category
• Dust dossier: pricing, governance and evidence in one place
• The Earned Autonomy Ladder: How much an AI agent may do alone, decided by its record
• Anatomy of an Agent: A Configuration, Not a Program: The ten declared parts every Nagent agent is built from
• How to evaluate an agentic AI platform: Six criteria, three grades of evidence and one bounded pilot
• Autonomy is not a setting: Nagent vs NoimosAI
• Nagent vs Jasper: a hundred agents is not a team
• Nagent vs Okara: the founder is not a queue

Facts about Dust are as its own pages and the reports below stated them, checked on 25 September 2026. Products change; the linked pages are the current word.

• Dust, Series B announcement, May 2026 https://dust.tt/blog/series-b-multiplayer-ai
• Sifted, Dust raises USD 40m, 2026 https://sifted.eu/articles/dust-series-b-40m
• Dust documentation, Pods overview https://docs.dust.tt/docs/user-documentation/pods/overview
• Dust documentation, Self improving skills https://docs.dust.tt/docs/user-documentation/agents/self-improving-skills.md
• Dust documentation, Tools management and stakes https://docs.dust.tt/docs/tools-management
• Dust documentation, Gmail tool https://docs.dust.tt/docs/gmail
• Dust blog, How I Built a Growth Outbound OS With Dust AI Agents, April 2026 https://dust.tt/blog/growth-outbound-os-with-dust-ai-agents
• Dust, Pricing https://dust.tt/home/pricing
• Dust, Changelog https://docs.dust.tt/docs/changelog
• Sifted, From chatbots to digital teammates, July 2026 https://sifted.eu/articles/from-chatbots-to-digital-teammates-brnd

Pratap Behera, Co-founder and chief executive, Nagent AI.

Pratap Behera is the co founder and chief executive of Nagent AI. Before Nagent he led growth at BigBasket, where he helped build BBdaily into a USD 200 million business.

Published 25 September 2026, last updated 4 October 2026. Part of the Nagent vs series.