SCENARIO ACTIVE2 Active Scenarios — $355K total exposure
$355,200 SLA exposure
CRITICAL

Context Coverage Impact

How each layer of context improves decision quality

ℹ

Illustrative data. Metrics on this page are derived from this demo deployment's seeded Decision Object corpus. They are intended to illustrate the type and directionof improvements AgentForge produces — not to represent any specific production deployment. In a live deployment, all figures are computed in real time from that customer's actual decision history.

Quality: ERP Only
?
54/100
Quality: Full Forge
?
89/100
Quality Lift
?
+35pts
Context Sources
?
4 layers
Decision Quality by Context Layer?
Structured Data Only
54/100
+ Unstructured Docs
68/100
+14
+ Supplier Signals
74/100
+6
+ Pattern Memory
81/100
+7
+ Institutional Memory
89/100
+8

Pattern Memory (+7pts) and Institutional Memory (+8pts) are the two biggest differentiators — neither is available in a conventional planning system.

Outcome Distribution by Context?
Good
Acceptable
Bad
Case Study: DO-2023-SC-0031 — The $31K Lesson
Without Pattern Memory (what happened)
  • • Agent had no record of SUP-A Q4 capacity constraint (pattern P-002 didn't exist yet)
  • • Full network expedite ordered — 1,000u from SUP-A at 35% premium
  • • SUP-A hit capacity wall, delayed 6 days post window
  • • RetailerX SLA breach + margin destruction: -$31,200
With Pattern Memory (what Forge does now)
  • • P-002 flags SUP-A Q4 capacity risk before hypothesis is even generated
  • • P-006 eliminates full-expedite approach at adversarial validation
  • • P-003 routes to MW-02 → NE-01 Werner transfer instead
  • • Result: SLA met, net outcome: +$8,640
This single bad decision generated patterns P-002, P-003, and P-006. The $31K loss has now been repaid ~3× in prevented failures.