Set up proactive agents around a real project for intake, evidence, actions, deliverables and reporting, with approved connections, human review and a clear way to pause or retire them.
What you will be able to do
- Sort incoming requests into one consistent queue: Define a narrow intake agent with a named role, such as Project Follow-through, that keeps a record of what it has already handled. Repeated or reworded requests then don't create repeat tasks, and incomplete ones are flagged as open questions instead of being filled in with assumptions.
- Keep project evidence current without the noise: Give a research agent an approved source set and an update cycle. It produces a source digest that reports what changed, where, and which part of your assignment it affects. Weak or conflicting findings are labelled and shown side by side for you to judge.
- See commitments and blocked work clearly: Maintain an action register where every commitment shows its source, only approved actions get in, and missing owners or dates stay marked as not agreed. Follow-up drafts wait for your review before anything is sent.
- Draft the next deliverable from what was actually agreed: Build a deliverable-preparation agent that drafts from your accepted requirements and evidence, shows which requirement each section draws on, and flags gaps instead of filling them. A reviewed change produces a traceable revision with a change log.
- Run a project review you can check over time: Connect the agents through a shared project record. Produce a recurring progress summary and meeting pack, then test how the whole set behaves when it is paused and resumed.
Taught at learn.m-a-s-t-e-r.ai: lessons that adapt to your level, an AI tutor you can ask anything, quizzes and flashcards, and a certificate when you finish.
