Understanding different memory architectures for AI agents and when persistent, episodic, or vector memory actually pays off in production.
AI Agent Memory in 2026: How It Works and When to Use It
Most agent demos forget everything between calls. That works for toy scripts. It breaks the moment you want an agent that improves over a week of work.
Memory is not one thing. It is several different stores that solve different failure modes.
Short-term context vs long-term recall
The context window is your agent's working memory. It is fast and expensive. Keep it for the current task only.
Learn how long-term memory pipelines, retrieval strategies, and consolidation tradeoffs help AI agents retain context across…
Most AI Agent Memory discussions start from the same assumption: If the agent forgets, give it more...
Vector recall bolted onto a prompt isn't memory. A temporal knowledge graph, self-editing blocks, and consolidation are what let…
Agent memory explained: the 7 types — working, semantic, episodic, procedural, retrieval, parametric, prospective — and when to…
Most AI agent systems start with a simple idea: "Let's give the Agent Memory". At first, this...
A practical guide for builders to design agent memory stores with working memory, episodic logs, semantic facts, decay rules,…
