Greenlight Financial Says Luna's Memory Layer Pushes Autonomous Resolution Rate from 83% to Over 90%
Greenlight Financial reports that in its tiered multi-agent architecture, the memory layer is the key factor driving the autonomous resolution rate from 83% (Tier 1 alone) to over 90%. The core mechanism is a case-learning pipeline: when Luna resolves a case in the same way three times, it records this as a confirmed pattern. After summarization, this insight is injected into the supervisor’s reasoning context, creating a feedback loop for continuous improvement. All figures are self-reported by the vendor and lack independent verification.
A notable design choice for Greenlight Financial’s security operations agent, Luna, is that it does not rely on more powerful models or additional tools to improve accuracy; instead, it relies on memory.
Luna is a tiered multi-agent system where Tier 1 handles the triage of routine alerts, while Tier 2 manages complex cases requiring multi-step reasoning. With Tier 1 operating alone, the autonomous resolution rate plateaued at 83%. Greenlight reports that adding the memory layer pushed this figure above 90%.
The memory layer consists of two parts. The first is short-term memory with a 30-day rolling window: when a new alert arrives, Luna checks whether this pattern occurred in the past 30 days, how it was adjudicated, and whether it should be allowed through. Greenlight provided a specific example—an infrastructure role executes planned maintenance every Tuesday at 3 AM. A system without memory would re-flag this behavior weekly, whereas a system with memory learns after the first resolution that this is expected behavior and automatically adds it to the allowlist.
The second part is long-term memory, which stores semantic preferences and resolution templates beyond the 30-day window. For instance, cards originating from a specific dark web source have a 73% false-positive rate, so deduplication is prioritized; or resolution templates encoded for recurring case types.