What happened
The developer behind HACH, an agent that lives inside Slack / Teams / Chatwork / LINE WORKS, laid out a retention design for chat logs, attachments and summarized data. Raw conversation logs get a short default, such as 30〜90日, while summaries are kept longer.
Why it matters
If retention is left as one block, conversations about departed staff or closed clients stay searchable past their purpose, and a deletion request can leave the same content alive in vector stores or summary caches. Splitting retention by data type and routing long-term memory into summaries is presented as making deletions easier to trace.
What to watch
Immediate erasure is often not technically or operationally possible for backups and snapshots, so the article treats those as deferred deletions and logs them in an audit trail. Whether a team can honestly say what is gone and what is still pending is the test.
WHO IT HITSTeams running always-on chat agents that accumulate contracts, HR details and client names — most directly the platform and security engineers who own log schemas and deletion workflows. Marking a record deleted is not enough once copies sit in embedding indexes and backups.
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The article comes from the developer of HACH, an AI agent that resides in Slack, Teams, Chatwork and LINE WORKS and runs with confidential-information masking at inference time inside AWS. That operating context is why the piece treats retention as a schema decision rather than a later cleanup task: an agent whose value is remembering past conversations is also an agent that accumulates contract amounts, HR information and client names in raw logs.
The four practices it lays out are related rather than independent. Splitting retention by data type keeps raw logs short and pushes long-term reference into summaries, but that only helps if deletion requests do not stop at a logical-delete flag — the same content may still sit in vector indexes and summary caches. The embedding problem is the sharpest version of this: even if the original text is removed, similar search results can surface near-copies of deleted content, so the embedding lifecycle must be tied to the source text. Finally, the article argues that a time-to-live value belongs in the storage schema from day one, because retrofitting bulk deletion means first hunting down every target.
The stakes hinge on whether teams can distinguish, at the moment a deletion request arrives, what is already gone from what is still queued. That transparency is what the article presents as making later accountability lighter.
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