Blog
Notes from the Statewave project
How agent memory works under the hood, deployment patterns we land on, and the design choices behind a Postgres-only, self-hosted memory runtime. Subscribe via RSS.
- Concepts
AI Data Governance for AI Agents: Governing What the Model Actually Sees
Governance for agents lives on the retrieval path, not in the catalog. What your agent remembers, what gets selected into a prompt, who may see it, where it may be stored — and what artifact you hand an auditor afterward.
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- Engineering
Multi-Tenant Isolation in AI Memory: Which Surfaces Actually Need a Tenant Boundary
A user can be fully authenticated, correctly authorized, and still receive another customer's facts in a context bundle. Where the tenant boundary belongs, the seven surfaces that are not tenant-scoped, and the backfill step that silently erases memory if you skip it.
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- Concepts
Episodic vs Semantic Memory in AI Agents: What to Store and When
Episodic memory stores what happened; semantic memory stores what's true. Here's how AI agents use each, when to convert one to the other, and how to store it.
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- Concepts
Agent memory provenance: why your LLM app needs an audit trail
When an AI agent answers from memory, you need to know where it learned the answer. This post explains the provenance model in Statewave — what we store, what it costs, and what it enables for compliance, debugging, and trust.
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- Concepts
AI agent memory vs RAG — what's the difference?
Retrieval-augmented generation and an agent memory runtime overlap at the embedding store, but they answer different questions. Here's where the two diverge and which to reach for.
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- Guides
How to add persistent memory to your AI support agent
A practical walkthrough of giving a support agent durable memory of customers across sessions — what to record, how to compile it, how to retrieve it, and what to expect from the support workflow benchmark.
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- Engineering
Self-hosted AI memory with Postgres and pgvector
Why Statewave is Postgres-only by design, what pgvector buys you over a dedicated vector database, and what the deployment shape looks like in production.
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