CyberMemory AI: an incident-response agent that learns from past incidents via Hindsight

charantejabikkasani · reddit · 2026-09-30

The author built CyberMemory AI, tackling a continuity problem: every alert starts from a blank context window, so the model gives generic checklists instead of using what the team already learned.

Hindsight serves as the memory layer instead of prompt stuffing. The flow:

The alert is normalized into a query (alerts are evidence, not memory)
recall pulls related past incidents via semantic, keyword, graph, and temporal retrieval
reflect reasons over the memory bank to produce a grounded report
After resolution, root cause, actions and outcome are written back via retain

Key lessons:

Recall and reflect solve different problems: recall gives raw evidence to show users; reflect gives synthesis. Separating them makes debugging easier — you can tell if a bad answer came from retrieval or reasoning
Memory before reasoning: generating the report first and attaching memory afterward performed worse; history should shape which hypotheses get explored
Retaining everything is the wrong default: log noise and temp IDs pollute retrieval; store each resolved incident as a structured record (context, cause, actions, outcome) plus tracing metadata
Similar wording ≠ similar incidents: relations like same user, IP range, or host don't show up in pure embedding similarity, so single-pass vector search is too thin
Write-back is the feedback loop: the resolution is more valuable than the original alert

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CyberMemory AI: an incident-response agent that learns from past incidents via Hindsight

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