AI-Assisted Work Retrospective

Triage AI-driven wins and friction—deliberate controls from disposable waste.

Use this template in TeamRetro

Went well

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

The agent scaffolded our test suite in minutes—saved 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Agent kept hallucinating an outdated API—cost ~2 hours of rework.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—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—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?

As teams weave AI agents and assistants into their daily work, the line between genuine acceleration and hidden cost gets blurry fast. The AI-Assisted Work Retrospective gives your team a disciplined, evidence-first way to review a cycle of AI-assisted delivery—celebrating the wins worth protecting, naming friction with its root cause, and deciding which friction is actually a control point you chose on purpose. The guiding idea is simple but powerful: not all friction is waste. Some of it—review gates, approval steps, verification loops—is a deliberate control the team put in place. Triage sorts the intentional from the disposable before anyone rushes to "fix" it. Running through five focused prompts, the team moves from evidence-cited wins, to friction with a named root cause, to the quiet assumptions the agent (or the team) let stand, then to a keep-or-kill decision on each friction, and finally to a short, prioritized action list. Each action lands at a specific altitude—documentation, environment, process, material, or upstream—so fixes are aimed at the right level rather than treated as generic to-dos. This altitude framing draws on the "keep the control you chose" thinking popularized in Armin Ronacher's AIE Europe talk on working deliberately with agents. The benefit is a retrospective that resists both hype and cynicism about AI tooling. By demanding evidence for wins, root causes for friction, and honesty about unverified assumptions, teams build a shared, fixed vocabulary for talking about AI-assisted work. In TeamRetro, everyone contributes cards in parallel, groups shared themes, votes on what matters most, and walks away with owned actions—turning a messy cycle into a clear map of what to protect, what to remove, and what to leave exactly as it is.

AI-Assisted Work retrospective format

Went well

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

This column captures evidence-cited wins only—no praise-padding. Ask each contributor to point to something concrete that AI assistance improved: a task that shipped faster, a bug caught earlier, a draft that saved hours. The goal is to identify what to protect and repeat, so encourage people to attach the evidence or moment behind the win rather than a general good feeling.

Friction, with the cause

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

Each card should name the moment, the rough cost, and one root-cause label from the team's fixed vocabulary (for example: docs, environment, process, material, upstream). Avoid vague grumbling—push for the specific instance and an estimate of what it cost. Naming the cause up front makes the later keep-or-kill and prioritization steps far sharper.

Guesses we let stand

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

This is latent friction—assumptions that haven't caused harm yet but remain unverified. Ask the team to flag anything the agent inferred or the team accepted on faith: an untested edge case, an unconfirmed requirement, a 'probably fine' shortcut. Surfacing these now prevents 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 deliberate control points the team chose (a nod to Ronacher's AIE Europe talk). Go through the friction cards and mark each as KEEP (an intentional control worth protecting) or KILL (disposable waste to remove). Removing a control should be an explicit team decision, not an accidental side effect of 'cleaning up.'

Do this first

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

Prioritize ruthlessly—top three fixes maximum. Each must have a named owner and land at a specific altitude: docs, environment, process, material, or upstream. This keeps actions aimed at the right level instead of becoming generic tasks. Use voting in TeamRetro to converge on the highest-payoff items before assigning owners.

When to use this retrospective

  • At the end of a sprint or delivery cycle where AI agents or coding assistants played a meaningful role in the work.
  • When your team is scaling up AI-assisted workflows and wants a disciplined way to separate genuine gains from hidden costs.
  • After a period of rapid AI adoption where friction and workarounds have started to accumulate without review.
  • When you suspect some friction is being 'fixed' away that actually serves as a deliberate control point worth keeping.
  • To build a shared team vocabulary for discussing AI-assisted work objectively rather than through 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?

Ideas and tips for your retrospective meeting

  • Agree on your fixed 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' is not a win; 'it saved ~half a day of boilerplate' is. This keeps the retro honest and actionable.
  • In the Keep-or-Kill step, treat removing a control as a deliberate decision. Never delete a review gate just because it's slow—confirm it isn't catching real problems first.
  • Cap the action list at three items with named owners and an altitude. Too many actions means none get done.
  • Give quieter or more skeptical voices explicit space—AI enthusiasm can drown out valid concerns about unverified assumptions.
  • Revisit the 'Guesses we let stand' column next cycle to check whether latent friction turned into real cost—it closes the loop on assumptions.

Frequently asked questions

How long does the AI-Assisted Work Retrospective take?
Most teams complete it in 45 to 60 minutes. The Keep-or-Kill and prioritization steps benefit from a few extra minutes of discussion since they involve real decisions about controls and ownership.
When should we use this format instead of a standard sprint retrospective?
Use it whenever AI agents or assistants materially shaped the cycle's work. A standard retro treats all friction as improvable, while this format explicitly distinguishes deliberate controls from disposable 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 ensures each action targets the right layer instead of becoming a vague to-do.
Why separate 'friction' from 'guesses we let stand'?
Friction is cost you already felt; guesses are latent, unverified assumptions that haven't hurt yet. Surfacing both lets you fix present pain and pre-empt future incidents.
What is the idea behind 'not all friction is waste'?
Some friction—like review gates and approval steps—is a control the team chose on purpose, an idea popularized in Armin Ronacher's AIE Europe talk. Triage sorts deliberate controls from waste before anything gets removed.
How many actions should come out of the retro?
Cap it at three fixes maximum, each with a named owner and an altitude. Fewer, well-owned actions are far more likely to actually get done.

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