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Last week, you asked your team’s AI assistant to put the regional sales summary first. This week, you have to explain it again.

Now imagine carrying that preference into the next conversation. The agent could also reuse the steps that solved a similar question or apply a correction you made last time. Over a week of recurring work, that could mean less time explaining and fewer mistakes repeated.

That is the promise of Agent Memory, announced by Databricks on October 20. It gives developers a way to add lasting memory to the custom AI agents they build for your business, so selected preferences, corrections and useful past work can carry into later conversations.

Think of a conversation as a meeting transcript, and memory as the notes you carry into the next meeting. The transcript records what happened. The notes keep the details that may help next time. An agent can save a useful detail during a conversation, while a background process can review earlier conversations and organize what to keep.

A preferred report format may be useful for months. A live sales figure needs to come from the latest data. The aim is to keep lasting lessons while looking up fresh figures when needed. Your team needs to decide which details the agent should carry forward.

The early research offers a reason to test the idea. Databricks evaluated MemCurrent, the research system behind Agent Memory, on tasks built from real conversations. Here is how it compared with agents that had no memory.

Databricks tested MemCurrent on 114 selected tasks derived from internal Genie conversations. Pass rates are averages across three runs, with answers scored by an AI model. These are research results, not a forecast for your rollout. Tokens are small units of text processed by the model; fewer tokens do not show the full cost of running memory.

For a pilot, choose one recurring task where you can see whether the saved context helps. Databricks stores both conversations and memories in Lakebase, a database it manages in your workspace. Databricks says developers can read, export and delete the saved entries. It also needs to manage the running costs and decide how those entries are handled.

Measure the full cost. During Beta, there is no separate charge for the memory feature itself, but the Lakebase database is billed, and Databricks says pricing may change. Background updates also incur processing and model-use charges. The research does not clearly say whether its token saving includes that preparation. Test total cost per successful task, alongside answer quality and time saved.

Plan for deletion. Keeping useful lessons also changes what it means to delete a chat. Removing stored conversation history does not automatically delete the memories derived from it. Your app needs a process for both, including a decision about how long each is kept.

Check who can access it. Deletion is only one part of controlling those saved lessons. User labels organize memories but do not enforce permissions. A person or application granted access to a memory store can read and change every entry in it. For strict separation between client companies, Databricks recommends a separate store for each client.

Databricks’ background process is designed to keep lasting preferences and workflows and leave out secrets. That is a design goal, not a guarantee that sensitive information cannot be stored. Developers can request an update or enable automatic updates roughly once a day; directly saved details can be available sooner.

Give memory an owner. As those updates revise or delete saved entries, someone needs to review incorrect or outdated lessons. Important business rules should still come from an approved source. Memory can help an agent reuse a lesson; your team still decides whether it is a good one.

Agent Memory is still in Beta, which Databricks classifies as not for production use. Use a controlled pilot to work through these choices with your data or IT team.

Agree three things before you start:

1. Set the rules. Choose which preferences and lessons the agent may keep, what it must exclude and who reviews the saved entries.

2. Test privacy and deletion. Check that each user sees the right information and that a deletion request reaches both conversation history and memory. Use separate stores where clients need strict isolation.

3. Prove the value. Compare the same tasks with and without memory. Measure answer quality, time saved and total running cost, including the database and background updates.

This article covers memory for custom agents. The ready-made Genie One assistant has separate, personal memory controls.

Memory is one part of running agents well. This week’s other updates cover model training, AI spending, marketing and two dates to put on your calendar.

We also have a new place to talk. Join r/brickster_ai to ask Databricks questions, share what you build and tell us what brickster.ai should cover next.

Planning a memory pilot? Ask the Brickster Assistant: “What should we check before giving a Databricks agent long-term memory?” It searches our archive and links to its sources.