npbuilds Adaptation Observer — Reading the Living Product
Before amplifying or pruning, observe. What is the product actually doing? How does the product-in-use differ from the product-as-seeded?
The gap between design and reality is not a bug — it's the most important data you have.
What to Observe
Behavioral Drift
How has the product's behavior changed from its initial conditions?
- Positive drift: The system does things better than designed, or does useful things nobody specified
- Negative drift: The system does things worse, or develops unhelpful patterns
- Neutral drift: Behavior changed but neither better nor worse — just different
Usage Patterns
How do actual usage patterns compare to expected ones?
- Frequency: More or less than expected?
- Duration: Longer or shorter sessions?
- Depth: Using surface-level or deep capabilities?
- Paths: Following designed flows or creating their own?
- Abandonment: Where do users stop? What triggers exit?
Feedback Loop Health
Are the feedback loops designed by seed/feedback-architect working?
- Active loops: Signals are being detected, responses are firing, effects are visible
- Dead loops: Signals exist but aren't being sensed, or responses aren't firing
- Runaway loops: Reinforcing loops without balancing — spiraling toward extremes
- Stale loops: Loops that were relevant but the context has changed
Emergence Events
New behaviors that emerged post-surface:
- Cross-domain capability combinations nobody designed
- User-discovered use cases beyond the original thesis
- System adaptations that improve quality without intervention
Observation Process
- Read the data — Usage logs, feedback signals, behavioral metrics (engage data-science-orchestrator if quantitative analysis needed)
- Compare to seed specification — What was expected? What's different?
- Classify the differences — Positive/negative/neutral drift per dimension
- Identify the signals — Which differences are actionable?
- Route — Positive patterns → amplifier. Negative patterns → pruning-engine. Surprising patterns → emergence-detector.
Output
# Adaptation Observation: {surface name} — {date}
## Behavioral Drift
Positive: {what's working better than designed}
Negative: {what's working worse}
Neutral: {what changed but isn't clearly better/worse}
## Usage vs. Expectation
{Key divergences between expected and actual usage}
## Feedback Loop Status
Active: {which loops are healthy}
Concerning: {which loops need attention — and why}
## Emergence
{New behaviors observed — cross-reference with emergence-detector}
## Recommendations
Amplify: {what to strengthen}
Prune: {what to cut or simplify}
Investigate: {what to watch more closely}
Leave alone: {what's fine and doesn't need intervention}