tylers-blogger-blog

Project management doesn't fail because people don't care. It fails because coordination is expensive: status updates, handoffs, "who owns this?", outdated docs, and the slow drift between what we planned and what actually happened. This manifesto is simple: stop using AI as a fancy autocomplete. Start using AI agents as teammates-purpose-built, role-based assistants that move work forward across your tools, with guardrails. 1) What "AI agents" mean in project management (and what they don't) An AI agent isn't "a bot that chats." It's a system that can: Understand a goal (e.g., "keep this sprint on track") Take actions in your workflow tools (create tickets, update fields, draft messages) Watch signals (PR activity, calendar changes, blockers in comments) Report outcomes and ask for approval when stakes are high Think of agents as specialists , not a single all-knowing manager. A practical stack might include: Intake Agent : t...

It started as a joke: "Let's see if the bots can survive a Friday night in our office." We'd been testing AI agents in small, polite ways-summarize a doc, draft an email, pull a quick report. Useful, sure, but not exactly game-changing. Then we looked at the calendar: a Monday exec update, a customer renewal call, a backlog of support tickets, and a spreadsheet that always seemed to multiply after 5 p.m. So we did the thing you're not supposed to do: we let a handful of AI agents run while we went home. The Setup: A Few Agents, Clear Boundaries, Real Work We weren't trying to build "Skynet for spreadsheets." We created three agents with narrow jobs, strict permissions, and obvious stop signs. 1) Inbox Triage Agent (read-only + draft-only): It scanned a shared inbox, labeled threads (billing, bug, onboarding), extracted key details (customer name, urgency, due date), and prepared draft replies using our saved tone guidelines. Nothing sent automatical...

It started as a "tiny experiment" we promised wouldn't disrupt anything. Two weeks later, we realized our AI agents weren't just helping-they were quietly becoming the most reliable teammates in the building . The surprising part wasn't that the agents could write copy or summarize notes. It was how they stitched together dozens of small, annoying tasks that normally fracture a day: the follow-up emails, the "can you pull that data again?" requests, the meeting prep, the status updates. Suddenly, our best people were doing more of the work only humans can do. The moment it clicked: less busywork, more momentum Our first win was embarrassingly simple: meeting-to-action automation. Before: someone took notes, someone else "cleaned them up," tasks got lost, and we'd spend the first 10 minutes of the next meeting re-litigating decisions. After: we used an AI agent that listened to the meeting transcript, produced: A 5-bullet executive summary D...