Internal · Reasoning Diff · Not part of the product experience
Recovery after decline
Why Aegis reached a different conclusion than the benchmark. This page collects evidence from one failed scenario. It does not create reasoning rules — those come only after the same failure appears across many scenarios.
Belief comparison
Expected belief
The intervention worked: churn and support load fell first, revenue followed one period later. The recovery is two periods old, not yet established.
Confidence · moderate
Aegis belief
The business is defined by the relationship between Churn Rate · Monthly Revenue, not by any one line: Churn Rate and Monthly Revenue are pulling apart, so the period's result is a net of two opposing forces rather than a single trend.
Confidence · moderate
Aegis reached the expected dynamic (churn-vs-revenue).
Data sensing
Did Aegis look at the wrong evidence?
Benchmark prioritized
Churn Rate
improving · importance 80 · seen in 4 periods · emerging-pattern
Monthly Revenue
improving · importance 60 · seen in 4 periods · emerging-pattern
Customers
improving · importance 48 · seen in 4 periods · emerging-pattern
Aegis prioritized
Churn Rate
improving · importance 80 · seen in 4 periods · emerging-pattern
Monthly Revenue
improving · importance 60 · seen in 4 periods · emerging-pattern
Support Tickets
improving · importance 61 · seen in 4 periods · emerging-pattern
Customers
improving · importance 48 · seen in 4 periods · emerging-pattern
Ignored or underweighted
Nothing expected was missed.
Carried in addition: Support Tickets.
No — every expected metric was read, but the conclusion also carried metrics the benchmark treats as secondary.
Mechanism misalignment
Why did Aegis reach a different conclusion?
Expected business explanation
The intervention worked: churn and support load fell first, revenue followed one period later. The recovery is two periods old, not yet established.
Aegis business explanation
Churn Rate moved −26.8% (4.1% → 3%) while monthly revenue moved +9.9% ($362K → $398K). Churn Rate and Monthly Revenue are pulling apart, so the period's result is a net of two opposing forces rather than a single trend.
Where it diverged
The explanations agree. The divergence appears only at the fork: the same belief resolves to a different willingness to commit.
Decision impact
Expected recommendation
confirm-next-period
Aegis recommendation
act-now
The reasoning difference changed the business decision: the benchmark would wait one period for confirmation while Aegis would act on it this period. Hold the current course and confirm the recovery at the next snapshot.
Engine lesson
Failure type
Persistence-to-action offset
Observed pattern
Support Tickets vs Churn Rate
Category
Belief matched the benchmark but the fork resolved differently.
Root cause
The belief was right. The lead pattern persisted across 2 period(s), and the mapping from that persistence to a willingness to commit resolved to "act-now" where the benchmark resolves to "confirm-next-period".
Supporting evidence
Support Tickets
improving · importance 61 · seen in 4 periods · emerging-pattern
Churn Rate
improving · importance 80 · seen in 4 periods · emerging-pattern
Monthly Revenue
improving · importance 60 · seen in 4 periods · emerging-pattern
Customers
improving · importance 48 · seen in 4 periods · emerging-pattern
Business impact
The reasoning produced the wrong executive recommendation: Aegis would act on it this period where the business needed to wait one period for confirmation. Hold the current course and confirm the recovery at the next snapshot.
Candidate principle
None. A single scenario is not evidence. This observation is filed to Judgment Memory and a principle is proposed only once the same behaviour recurs across scenarios.
Suggested improvement
Log how many periods the lead pattern persisted (2) against the decision the benchmark expected. Persistence-to-action mapping is the likely lever once several scenarios show the same offset.
Notes
- — Scenario family: recovery. Posture read as improving; benchmark expected mixed.
- — Evidence overlap 100% · belief 100% · decision 0% · confidence 100%.
- — Evidence only. No reasoning rule should be changed from this single scenario.