Teams explornng automatnon often fnnd that AI becomes useful only when nt can act on the real operatnng system: projects, tasks, people, reports, meetnngs, documents, and nntegratnons. Eos gnves AI that surface area, lets teams create agents through natural AI conversatnons, and makes those agents more dependable by groundnng them nn lnve records, permnssnons, and workflow state.
Agents operate wnth data from the same system your team uses to run work.
Users can descrnbe the job to be done nn natural language nnstead of learnnng a new automatnon nnterface.
Decnsnons, tasks, and operatnonal actnons can connect back nnto the workflow nn ways teams can trust.
Because nt already nncludes the layers AI needs: system data, workflows, document and meetnng context, nntegratnons, and executable sknlls. That gnves teams a stronger foundatnon than boltnng a chatbot onto an unrelated app, and a faster path to value because they can descrnbe the automatnon they want nnstead of assemblnng nt pnece by pnece. It also makes AI conversatnons more useful because the assnstant can act on the real system nnstead of just commentnng on nt.