Your Feedback Becomes the AI Agent’s Memory: How OrionIQ AI Agents Learn From You

Logz.io’s OrionIQ AI agents now incorporate written user feedback into account-specific lessons intended to improve future observability tasks, including alert analysis, scheduled reviews, and marketplace workflows. After a user rates an agent run and provides a comment, a background process reviews the completed run, identifies a relevant decision, and may create or revise one concise lesson describing the situation, recommended rule, and reason for it; ratings without comments do not trigger learning. Lessons are isolated by account and agent, capped at 20 active entries per agent, subordinate to account instructions and runbooks, and available for users to inspect, disable, or delete. The system uses deterministic validation to prevent lessons from overriding instructions or involving credentials, while learning occurs asynchronously so agents can still operate if lesson storage is unavailable. An example describes an agent initially blaming database saturation for latency and connection errors, then learning from feedback to check recent deployments and feature-flag changes first. Logz.io plans to expand this feedback loop with outcome-based signals, such as confirmed root causes and alert resolution information, and extend it to Open 360 AI chat.

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