Attribution Failure · Primary
We know what happened. Nobody can explain why.
How this principle feeds it
the system records changes and outcomes but cannot say which caused which.
Principles Library / Closing the Learning Loop
Closing the Learning Loop
Continuous System Learning Sustains Performance
Diagnose
AI signs are on by default. Use the AI switch to exclude them.
1. Which of these early warning signs are you seeing?
- The same problems come back despite repeated discussions and retrospectives.
- Improvement actions get identified but are rarely finished or checked.
- Change initiatives start strong and then quietly fade.
- Process changes go in without evidence that they helped.
- Old practices continue out of habit after the conditions behind them have changed.
- Delivery gets faster while outcomes stay flat, and nobody revisits old practices.
- Lessons from one team rarely reach other teams or standard practice.
- AI recommendations and agent self-optimization get accepted without checking what they optimize for. (AI)
- Retros and incident reviews change human processes but never examine agent influence or configuration. (AI)
- Nothing connects observed outcomes back to the agent behaviors that produced them. (AI)
- Model drift or bias is found only after it has affected customers. (AI)
2. Over the last few months, have you noticed…
For each consequence: Not seen / Sometimes / Persistently / Not sure.
- Known problems become fixtures that teams work around instead of removing.
- Nobody can tell which changes helped, so improvement work repeats and loses credibility.
- Outdated practices stay embedded, and AI now repeats them faster.
- Agent behavior drifts and degrades, and nobody notices until it compounds. (AI)
- Nobody can trace AI-shaped outcomes to a decision, so responsibility blurs and trust drops. (AI)
- Lessons stay local and leave when the people who learned them leave.
3. The condition it becomes
Based on your answers, recognize at most two conditions: No / Partly / Yes, that is us.
Attribution Failure: We know what happened. Nobody can explain why.
Intent Drift: The team built exactly what was asked. It is no longer what is needed.
Implementation Drift: The agents are building. Nobody is sure it still matches the plan.
Oversight Erosion: The review process still runs. It stopped meaning anything.
Take your Diagnosis to the next level by invoking AI Diagnostic Coaching tool.
What else have you observed? Optional. Shared only if you choose to start coaching.
Start a Diagnosis session: review and edit a draft containing your note, conditions you recognized in Step 3 (including whether they match partly), warning signs and consequence ratings before sending it. No scores or principle files are sent.
Overview
Principle intent
Continuously learn from system behavior, human and AI alike, and use that learning to improve how work is designed, delivered, governed, and supported.
Read the full intent
Delivery systems accumulate friction, outdated practices, and new failure modes as conditions change. Sustained performance depends on the system's ability to observe, learn, adapt, and retain what works. AI-influenced decisions have to be part of that learning loop just as much as human ones. Nothing exempts them from it by default.
When it holds: what it does for you
Problems stay fixed
Recurring issues get tested with small experiments and removed, so teams stop working around them.
Changes prove themselves
Only changes that move the intended outcome make it into standard ways of working.
AI inside the loop
Agent behavior gets reviewed and corrected alongside human decisions, so drift shows up before customers feel it.
When it is ignored: from early signs to a lasting condition
Early warning signs · Visible now, cheap to fix
- Improvement actions get identified but are rarely finished or checked.
- Process changes go in without evidence that they helped.
- Old practices continue out of habit after the conditions behind them have changed.
What happens over time · Accumulates, costly to reverse
- Known problems become fixtures that teams work around instead of removing.
- Outdated practices stay embedded, and AI now repeats them faster.
- Lessons stay local and leave when the people who learned them leave.
The condition it becomes
Intent Drift · Primary
The team built exactly what was asked. It is no longer what is needed.
How this principle feeds it
the system keeps executing against assumptions that no longer match conditions.
Implementation Drift · Primary
The agents are building. Nobody is sure it still matches the plan.
How this principle feeds it
unexamined deviations in agent behavior become the new operating baseline.
Oversight Erosion · Secondary
The review process still runs. It stopped meaning anything.
How this principle feeds it
oversight set up at deployment stops matching an agent whose behavior has drifted.
Act
Act
Showing all 6 practices. Answer the Diagnose questions and the most relevant come first.
Run small experiments on recurring problems
Treat recurring problems and performance gaps as chances to learn about the system. Frame each improvement as a small experiment you can test.
Start by asking your team: How quickly can we test and learn from an idea meant to fix a recurring problem?
Avoid · You’ll know it’s working when
Avoid
- Treating retrospectives or lessons-learned sessions as improvement by themselves
- Launching large transformation programs without testable hypotheses
You’ll know it’s working when
Problems raised in retros get fixed and stay fixed, and improvement actions get finished and checked.
Prove a change works before standardizing
Measure whether a change improves the outcome it was meant to before you standardize it. Close the loop by tracking outcomes instead of counting actions.
Start by asking your team: What have we learned about how the system is actually behaving, and what did we change based on that evidence?
Avoid · You’ll know it’s working when
Avoid
- Introducing frequent change without measuring whether outcomes actually improve
You’ll know it’s working when
Every process change comes with evidence that it moved the outcome it targeted.
Retire what stopped working, keep what works
Stop or revise practices that no longer produce useful results. Build what does work into your standard ways of working.
Start by asking your team: Which practices should we stop, revise, or standardize based on what we've learned?
Avoid · You’ll know it’s working when
Avoid
- Preserving outdated practices because they worked in the past
You’ll know it’s working when
Practices get dropped once the conditions behind them change, and lessons from one team show up in shared standards.
Bring AI decisions into retros and reviews
Put AI-influenced decisions on the table in retrospectives and reviews. Link outcome metrics back to the AI behavior or recommendations that shaped them.
Start by asking your team: How did AI influence this decision or outcome, and what patterns show up across AI-supported decisions?
Avoid · You’ll know it’s working when
Avoid
- Treating AI as a passive tool rather than an active contributor to outcomes
- Assuming AI self-optimization means the delivery system is learning
You’ll know it’s working when
Retros examine agent influence, and outcomes can be traced to the agent behavior behind them.
Monitor agent behavior and name an owner
Track agent behavior patterns over time with dedicated evaluation and monitoring loops. Give someone clear ownership for acting on what they surface.
Start by asking your team: What feedback actually reaches the model or agent configuration, not just the team?
Avoid · You’ll know it’s working when
Avoid
- Reviewing individual AI outputs without examining behavioral patterns over time
You’ll know it’s working when
Drift and bias show up in monitoring before customers feel them, and someone acts on it.
Revisit the system when conditions change
Check whether changes in technology, teams, customers or dependencies mean the system has to evolve, including agent configurations.
Start by asking your team: As AI becomes part of delivery, how are lessons changing agent behavior, configuration, and governance?
Avoid · You’ll know it’s working when
Avoid
- Improving human workflows while leaving automated failure patterns untouched
You’ll know it’s working when
Old practices and agent setups get revisited when the conditions behind them change, and faster delivery shows up in outcomes.