AI-Assisted Work Retrospective

Sort deliberate AI controls from waste you can remove.

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Went well

What did AI-assisted work make faster or better this cycle?

The agent scaffolded our test suite in minutes, saving roughly half a day of boilerplate we can point to in the PR.
AI-generated first-draft docs meant reviewers only had to edit, not write from scratch. Turnaround dropped noticeably.
Pair-programming with the assistant caught two null-handling edge cases before they hit staging.
Friction, with the cause

Where did it drag, and which root cause was it?

Agent kept hallucinating an outdated API, which cost ~2 hours of rework. Root cause: docs (stale reference material).
Local env didn't match CI, so AI-generated code passed locally then failed the pipeline. Root cause: environment.
We re-explained the same context to the assistant across sessions. Root cause: process (no shared prompt/context doc).
Guesses we let stand

What did the agent (or we) assume without enough information?

We assumed the agent's rate-limit handling was correct but never load-tested it.
Accepted the AI's data-migration script without verifying it against production-scale volumes.
The agent assumed our users are all in one timezone. That's unverified and likely wrong.
Keep or kill

Which of these frictions did we choose on purpose?

KEEP: The human review gate on all AI-generated migrations. It's slower, but it caught a real bug. Deliberate control.
KILL: Re-explaining context every session. Pure waste; we'll add a shared context doc.
KEEP: Approval step before the agent touches production config. Chosen on purpose.
Do this first

Of everything here, which one fix pays off most, and at what altitude?

Update the API reference docs the agent relies on. Owner: Priya. Altitude: docs.
Align local dev env with CI via a container image. Owner: Marcus. Altitude: environment.
Create a shared context/prompt doc so we stop re-explaining. Owner: Lena. Altitude: process.

What is the AI-Assisted Work Retrospective?

Once AI agents and assistants are part of everyday work, it gets hard to tell real speed-ups from hidden costs. The AI-Assisted Work Retrospective gives your team an evidence-first way to review a cycle of AI-assisted delivery. You name the wins worth protecting, trace each piece of friction to a root cause, and then decide which friction you put there on purpose. That last step matters because not all friction is waste. Review gates, approval steps and verification loops are controls the team chose, and triage separates them from the waste before anyone rushes to "fix" them. The board has five columns. Went well takes evidence-cited wins. Friction, with the cause names each drag and labels its root cause. Guesses we let stand collects the assumptions the agent (or the team) accepted without checking. Keep or kill marks each piece of friction as a control to protect or waste to remove, and Do this first ends with up to three fixes. Each fix lands at a named altitude (docs, environment, process, material or upstream), so it's aimed at the right level instead of becoming a generic to-do. Asking for evidence behind wins, causes behind friction and honesty about unchecked assumptions keeps the retro clear of both hype and cynicism, and it gives the team a fixed vocabulary for AI-assisted work. In TeamRetro, everyone adds cards in parallel, groups shared themes and votes, and you leave with owned actions and a clear view of what to protect, what to remove and what to leave alone.

AI-Assisted Work retrospective format

Went well

What did AI-assisted work make faster or better this cycle?

Only wins with evidence go here, so skip the general praise. Ask each person to point to something AI assistance actually improved: a task that shipped faster, a bug caught earlier, a draft that saved hours. The aim is to find what to protect and repeat, so ask for the moment or the numbers behind each win.

Friction, with the cause

Where did it drag, and which root cause was it?

Each card names the moment, a rough cost and one root-cause label from the team's fixed vocabulary (docs, environment, process, material or upstream). Steer away from general grumbling and ask for the specific instance and what it cost. Naming the cause now makes the keep-or-kill and prioritization steps much sharper.

Guesses we let stand

What did the agent (or we) assume without enough information?

This is friction that hasn't hurt yet: assumptions nobody has checked. Ask the team to flag anything the agent inferred or the team took on faith, like an untested edge case, an unconfirmed requirement or a "probably fine" shortcut. Catching these now keeps them from becoming next cycle's incident.

Keep or kill

Which of these frictions did we choose on purpose?

Not all friction is waste. Review gates and approval steps can be control points the team chose on purpose. Go through the friction cards and mark each one KEEP (a control worth protecting) or KILL (waste to remove). Removing a control should be an explicit team decision, never a side effect of cleaning up.

Do this first

Of everything here, which one fix pays off most, and at what altitude?

Be strict: three fixes at most. Each needs a named owner and a specific altitude (docs, environment, process, material or upstream), which keeps it aimed at the right level. Vote in TeamRetro to settle on the fixes with the biggest payoff before you assign owners.

When to use this retrospective

  • At the end of a sprint or delivery cycle where AI agents or coding assistants did a real share of the work.
  • When your team is scaling up AI-assisted workflows and wants to separate real gains from hidden costs.
  • After a stretch of fast AI adoption, when friction and workarounds have piled up without review.
  • When you suspect someone is about to remove friction that's actually a control worth keeping.
  • When you want a shared team vocabulary for talking about AI-assisted work without hype or cynicism.

Suggested icebreaker questions

  • If your AI assistant had a personality this sprint, would it be an eager intern, a know-it-all, or a quiet genius, and why?
  • What's one task you'd never trust an AI with, and one you'd happily hand over tomorrow?

Want a warm-up your team plays, not just answers out loud? Browse free icebreakers →

Ideas and tips for your retrospective meeting

  • Agree on your root-cause vocabulary (docs, environment, process, material, upstream) before you start. Consistent labels make triage and prioritization much sharper.
  • Insist on evidence for wins. "It felt faster" isn't a win; "it saved half a day of boilerplate" is.
  • In Keep or kill, treat removing a control as a decision. Never delete a review gate just because it's slow. Check first that it isn't catching real problems.
  • Cap the action list at three, each with a named owner and an altitude. A longer list usually means nothing gets done.
  • Give quieter and more skeptical people room to speak. Enthusiasm for AI can drown out fair concerns about unchecked assumptions.
  • Revisit Guesses we let stand next cycle to see whether any of them turned into real cost.

Frequently asked questions

How long does the AI-Assisted Work Retrospective take?
Most teams finish in 45–60 minutes. Leave a few extra minutes for Keep or kill and the final prioritization, since those involve real decisions about controls and owners.
When should we use this format instead of a standard sprint retrospective?
Use it whenever AI agents or assistants shaped a large part of the cycle's work. A standard retro treats all friction as something to improve, while this format separates deliberate controls from waste.
What does 'altitude' mean in the action step?
Altitude is the level a fix lands at: docs, environment, process, material or upstream. Naming it points each action at the right layer, so it doesn't turn into a vague to-do.
Why separate 'friction' from 'guesses we let stand'?
Friction is a cost you've already paid. Guesses are unchecked assumptions that haven't hurt yet. Looking at both lets you fix today's problems and head off the next incident.
What is the idea behind 'not all friction is waste'?
Some friction, like a review gate or an approval step, is a control the team chose on purpose. Triage sorts those controls from waste before anything gets removed.
How many actions should come out of the retro?
Three at most, each with a named owner and an altitude. A short list of owned actions is far more likely to get done.

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