Axel Blandeau

It's only for admin 🙃, it's pretty powerful

Hello Félix, if you’re an admin, you have access to the default “Analyst” agent, which can do that.

Yes they are 😉 I spoke to Youssef about that

Hello Hugo, It really depends on your Dust pricing plan. Which plan are you on? If you’re on the Business plan, each user gets 8,000 credits per month, which are replenished monthly and aren’t shared between users. You also have the option to increase that limit to 40,000 credits per month

Hi Alexis ! Adoption is indeed a great first step. The harder question is: what comes next? In our experience with our clients on Dust, the answer lies in combining clear governance with dedicated time to support employees over the long term. On the governance side, it is important to set up a clearly identified project team with distinct roles:

Builders represent their teams, identify relevant use cases, train their peers, and make sure agents are properly shared and adopted within the team. They also work with team managers to review and approve the agents that are ready to move into production. For this model to work, builders need dedicated time in their role to build, iterate, and maintain agents. This work should also be recognized and rewarded as part of their responsibilities. Another key role is the AI Ops technical lead. What we have found to work particularly well is continuous support through:

From a governance perspective, the project manager should also maintain a clear overview of the agents being used and adopt a product-oriented approach to their lifecycle:

It is also important to track the ROI of agents in production, taking into account their credit cost, usage, time saved, and day-to-day impact on the team. The next challenge is moving beyond isolated individual use cases and rethinking workflows at the team and company level. This is where AI adoption can evolve from experimentation into a sustainable operating model