AI Agents Retrospective

AI Agents Assemble Retrospective Format

Algorithms That Worked

What processes or behaviours delivered great results?

This topic is the team's equivalent of a well-trained model — the routines, habits, and decisions that consistently produced positive outcomes. Encourage participants to be specific about what worked and why, so the team can consciously repeat and reinforce these behaviours. Ask: 'What would we want to keep running in the next sprint?'

System Glitches

What errors or failures slowed the team down?

Just like a software bug, a system glitch is something that caused unexpected behaviour or a drop in performance. This topic surfaces the friction points, miscommunications, and process failures that got in the way. Encourage a blameless tone — focus on the system, not the individual. Ask: 'If we were debugging this, what would the error log say?'

Missing Gaps

What knowledge gaps or missing information held us back?

An AI agent is only as good as the data it's trained on. This topic explores the gaps in knowledge, documentation, onboarding, or shared understanding that limited the team's effectiveness. Facilitators should help the team distinguish between one-off knowledge gaps and systemic issues that need a longer-term fix. Ask: 'What information, if we'd had it earlier, would have changed our approach?'

Upgrade Requests

What improvements or experiments should we run next?

Every great AI system is continuously improved through iteration and feedback. This topic is where the team proposes upgrades — new processes, tools, experiments, or behaviours they want to try in the next cycle. Encourage bold ideas as well as small tweaks. Ask: 'If you could push one update to how we work, what would it be?'

When to use this retrospective

• When your team wants a fresh, creative spin on the standard retrospective format that still drives meaningful outcomes.
• Ideal for tech or engineering teams who will connect naturally with AI and systems-thinking metaphors.
• Use it after a sprint or project phase where there were notable successes and failures worth unpacking in a structured but engaging way.
• Great for teams experiencing recurring blockers or knowledge gaps that haven't been surfaced through traditional retrospective formats.
• When you want to energise a team that has grown fatigued with the same retrospective structure and needs a new lens to spark honest conversation.

Suggested icebreaker questions

• If you were an AI agent, what would your core function be — and what's your most common error message?
• If your team were an AI model, what dataset would you say you've been trained on, and what data are you still missing?

Ideas and tips for your retrospective meeting

• Set a blameless tone early — remind the team that just like debugging software, the goal is to fix the system, not point fingers at individuals.
• Timebox each topic to keep energy high. AI Agents Assemble works best as a fast-paced session; aim for 10–12 minutes per topic.
• Encourage specificity in 'System Glitches' — vague complaints like 'communication was bad' are hard to act on. Push for concrete examples and root causes.
• Don't let 'Upgrade Requests' become a wishlist. For each idea, ask the team to identify an owner and a first small step to make it actionable.
• If the team is new to the AI metaphor, spend 2–3 minutes at the start framing the analogy — it helps people engage more creatively with the prompts.
• Watch for patterns across topics. If the same theme appears in both 'System Glitches' and 'Missing Training Data,' it's likely a high-priority systemic issue worth addressing first.