How to build user-controlled persistent context for an AI agent

The stateless problem

Many conversational interfaces do not carry user-approved context into a new session by default. That protects some privacy boundaries but can make repeated work cumbersome.

Continuity is useful only when the user understands what persists, why it was retrieved, and how to correct or remove it.

The Samsarix approach: per-agent persistent memory

Memory Architecture

Samsarix can store approved, agent-scoped context with attribution and retrieve it on a later run. Storage does not make an inference true, permanent, or eligible for every future task.

Memory Types

• Episodic context — Attributable records from eligible conversations or runs
• Semantic context — User-approved facts or references with provenance
• Procedural context — Approved reusable instructions for specific tasks

Memory Retrieval

When a new task arrives, the system can retrieve eligible context and attach its provenance. This is a retrieval-augmented context pattern; the interface should show enough evidence for the user to inspect why an item appeared.

Building it yourself

If you wanted to implement persistent agent memory:

1. Per-agent storage — Each agent gets its own database table or namespace
2. Memory indexing — Use embeddings for semantic search, timestamps for recency
3. Consent and authorization — Define which sources may persist and which agents may retrieve them
4. Retention and correction — Support expiry, stale-context review, export, correction, and deletion

Samsarix exposes persistent-context controls across its agent roster, but availability and behavior still depend on the user's consent, plan, workspace policy, and the evidence attached to each item.

Build continuity with controls. Try Samsarix free → Review what persists before relying on it in later work.