Figs · AI agents that show their work

The convenience of having agents wake up on schedule and handle tasks like chasing invoices is appealing. The sign-off system lets them work independently, which saves time. However, the fact that it is still in public beta means some reliability might be untested. The ability to connect over a thousand apps is impressive, but verifying that all necessary tools integrate smoothly is crucial. The team seems to have created a useful workflow, though the lack of detailed pricing specifics for enterprise use is a minor gap.

From the information available, Figs presents a structured approach to AI agents that distinguish themselves by showing their work. The platform allows users to build agents through chat, assigning them specific roles like AR clerks or research analysts, each with their own connections and monthly caps. This budget model seems flexible, as hiring is free and costs are tied to work done rather than seats. The sign-off process appears designed to let agents continue other tasks while waiting for approval, which could improve efficiency. However, the public beta status means some features might still be evolving. The emphasis on logging every step and allowing send-backs with notes suggests a focus on transparency and iterative improvement. For someone considering this company, the balance between automation and human oversight seems well thought out, though the actual experience depends on how well the agents perform in real-world scenarios. It is a promising tool for those who want AI that does real work without constant babysitting.

The pricing structure is excellent. Paying for work done with hard monthly caps prevents costs from spiraling. Hiring is free, and the public beta offers a solid starting point.

This is fantastic! Paying only for work done with clear monthly caps is such a relief. I just need to know if read calls count toward the budget.

From the information available, Figs offers an intriguing model for AI agents that show their work. The concept of budget caps and paying for work done rather than seats is attractive. However, the advertised terms leave some questions. While hiring is free and reads run free, it is not entirely clear how write operations are counted against the monthly cap. The public beta status suggests that the platform is still evolving, and users might encounter limitations. The sign-off process is described as non-blocking, which is a positive feature. Yet, without more detailed pricing examples for different types of work, it is difficult to predict actual costs. For someone considering this company, the lack of clarity on how specific tasks are billed could be a concern. The team seems capable, but the transparency around pricing and scope needs to be clearer before committing to long-term use.