Quick answerSet a default retention window by data category, not a single blanket number, since raw transcripts, extracted account data, and aggregated analytics all carry different risk and different business value the longer they sit around. Automate the purge as a scheduled job tied to conversation timestamp, not a manual quarterly cleanup someone has to remember to run, and log every purge event itself so you can prove the schedule actually executed if asked later. Build in a documented exception path for data under legal hold, so the routine purge job does not silently delete something a litigation hold requires you to keep. ---
This is routine housekeeping, not a deletion event
Our post on what happens when a customer wants their AI agent history deleted covers a specific, customer-initiated event, one account, one request, handled as it comes in. A retention schedule is the opposite kind of process: it runs continuously in the background against every conversation your agent has ever had, aging data out on a fixed timeline whether or not any customer ever asks. Both matter, but conflating them means the routine schedule often never gets built, since teams treat the reactive deletion process as if it covers the whole problem.
Different data categories need different clocks
A raw transcript, a structured record of what account changes were made, and an aggregated usage metric all have different reasons to exist and different risk profiles if kept too long. A reasonable starting split keeps raw transcripts on the shortest clock, since they carry the most re-identifiable detail and the least ongoing business value once a conversation is resolved, keeps structured account-action records longer where you have a genuine operational or compliance reason, and treats fully aggregated, de-identified analytics as effectively exempt from the same schedule.
The training-data question is separate and needs its own gate
Even data inside its normal retention window should not automatically flow into model training or fine-tuning; that is a separate consent question covered in whether customer AI agent conversations should be reused as training data. Keep the retention schedule and the training-data consent gate as two independent controls, since a conversation can be fine to retain for operational reasons while still being off-limits for training, or the reverse.
Do not let the purge job override a legal hold
A scheduled purge running on autopilot is exactly the kind of process that can accidentally delete something a litigation hold or regulatory request requires you to preserve, since the automation does not know about a hold unless you explicitly wire it in. Build a hold flag that the purge job checks before deleting anything, sourced from whatever process handles the residency and jurisdictional questions covered in how to handle data residency and sovereignty for your AI agent, since holds and residency requirements often originate from the same legal and compliance team.
FAQ
Should the retention window be the same across every jurisdiction you operate in?
No, some jurisdictions set their own minimums or maximums; build the schedule to support per-region overrides rather than one global number from the start.
How do you prove the schedule actually ran if asked during an audit?
Log every purge job's execution and record count, kept separately from the data it deleted, so the audit trail survives even though the underlying conversations no longer do.