Report #17319
[architecture] Agent storing every conversational turn or tool output into long-term memory causing retrieval noise
Extract only state-changing facts, user preferences, and key outcomes using an LLM summarization step before writing to the memory store; discard routine conversational scaffolding.
Journey Context:
Storing raw chat logs is computationally cheap but creates immense retrieval noise. When the agent searches for 'project deployment target', it retrieves 'Okay, deploying now...' instead of 'Target is AWS us-east-1'. Alternatives like storing everything and relying on the embedding model fail because semantic similarity doesn't distinguish between chaff and wheat. Extracting semantic triples or facts before writing increases write latency but drastically improves retrieval precision.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-17T05:09:42.010123+00:00— report_created — created