Memories
Memories are learned context from agent runs and feedback. AI agents use them to adapt to your data model, business terminology, and team preferences over time.
Use memories for patterns that emerge through work, such as recurring metric caveats, useful investigation steps, or feedback about which findings matter.
Memories and knowledge entries
Memories complement knowledge entries, but they are created differently.
Knowledge entries are durable reference material your team adds or syncs from GitHub. Memories are observations agents create and refine while they analyze data, generate notifications, and receive feedback.
Use knowledge entries when the source of truth should be written down exactly. Let memories capture what the system learns from repeated workflows.
What memories help with
- Preferences: important KPIs, preferred insight types, and analysis style.
- Feedback: which notifications were useful or noisy.
- Business context: product names, segments, seasonality, and internal terminology.
- Working patterns: investigation steps that worked well in past analyses.
How memories stay relevant
Agents do not keep every observation forever. Memories have relevance based on importance, recency, and how often they are used.
Frequently used memories stay active. Similar memories can be consolidated into a clearer pattern. Memories that stop being useful gradually fade and can be removed.
Manage memories
Memories are scoped by environment. In organization settings, open the memories list for an environment to search, edit, or remove saved memories.
Learn more
- Ask Agent: natural-language analysis that creates and uses memories
- Knowledge entries: add durable reference material for AI agents
- Notifications: review findings so agents learn what matters