How Agentic AI and ClickUp Transformed My Engineering Management

For a long time, my one-on-ones had a second job running in the background, since I would sit with a direct report and try to get every important detail into a notebook before it slipped. I left those conversations with pages of notes, and I also left having spent part of the hour somewhere else entirely.

I touched on this shift when I wrote about relaunching the site. The short version was that AI workflows made the management side of my job at ClickUp more efficient, and the longer version is about attention. AI notetakers now sit in the meeting and do the capture work I used to do by hand, so the notes are still there afterward, usually more complete than anything I would have scribbled, and during the conversation I actually get to be in it.

With my attention on the person in front of me, I notice different things: the hesitation before someone names a problem, the project that looks fine in the tracker and is wearing on the person doing the work, and the moment when a follow-up needs to happen while we are still talking and the point is still alive, rather than later, reconstructed from a half-readable page.

The record of the meeting still matters, and it no longer has to be produced by the same brain that is trying to understand the person across from me. Taking that administrative load off the hour gave the human part of the job the room it had been sharing with logistics.

Work starts as a task

Every piece of work on the team starts as a task in ClickUp, and the part that changed delegation is what gets attached to that task before anyone is asked to pick it up.

Context used to scatter across a doc that was almost current, a thread that had already scrolled away, and whatever I could remember from the last sync. Handing work to an engineer meant a briefing, because the briefing was where the real information lived, and if ownership changed we briefed again.

The task can carry that context now, with decisions, constraints, notes, and links back into the rest of the workspace and the tools connected to it sitting alongside the work itself. An engineer can open the task and see why it exists, what has already been decided, and what done actually looks like, and if someone else picks it up next week they inherit the same picture. We still talk when the work is ambiguous, since the background now travels with the task and the conversation can start at the part that needs a person.

Showing up already oriented

Mentorship is where these workflows changed my week the most. Before each one-on-one, a custom agent reads back through past notes and surfaces what is worth paying attention to, whether that is an open thread from last time, a growth goal that has gone quiet, or friction that showed up in the work and never quite made it into the conversation. I read that brief, decide what we will actually spend the time on, and walk in already oriented.

The meeting can start on the discussion itself, so my direct reports do not have to spend the opening stretch catching me up on a month the system already remembers. We get to talk about priorities, growth, and the friction that does not fit cleanly in a task comment, and it feels more collaborative because we are looking at the same history and deciding what to do with it, rather than using the hour to extract a status report.

Review cycles are the same idea stretched over months. A fair account of someone’s growth is hard to write from memory alone, because the important things blur together with whatever happened most recently, and an agent can lay out the longer span: what shipped, where someone stretched, and which challenges kept showing up. I use that to build a case that is specific, with growth tied to work we both saw and challenges named clearly enough to be useful. The agent gathers the history, and I still decide what the story is, which is a better decision when it is grounded in the whole stretch of time.

A clearer story upward

The same pattern helps when I sit down with my own leadership. Across a few projects, progress and roadblocks rarely arrive as a neat narrative, because they live in tasks, updates, and the side conversations I meant to write down. Before those one-on-ones, an agent can synthesize what moved, what stalled, and where the team is carrying more than it should, so I can put real wins on the table with enough detail to mean something, and raise the places where I need support, a decision, or cover while they are still a conversation.

That used to mean reconstructing the week from memory and a pile of tabs. The synthesis gives me a draft of the situation, and I still have to know it well enough to stand behind it.

Treating agents like research assistants

None of this holds up if I treat the generated output as finished.

I use these agents the way I would use a sharp research assistant, since they can read across more history than I will reliably hold in my head and hand me a synthesis when I need one. Then I do the part that is actually the job: I read what comes back, cut what misses the point, and add the context that never made it into a note, because being in the room produces a kind of understanding a transcript only approximates. A paragraph does not go into a review, a task, or a note to my manager until I have put my own direction on it.

When a workflow earns its place, I pass it along, because the same idea that took the notebook out of my one-on-ones can take a chunk of administrative work off an engineer’s plate, whether that is status updates, grooming, or the small chores that fill a week and leave the real engineering squeezed to the edges. I would rather my team spend that attention on the work that needs them, and my job is to make that easier while keeping the judgment with the people doing the work.