[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-agent-memory::en":3,"gloss-cluster-agent-memory::en":26,"gloss-next-agent-memory::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"agent-memory","agents","Agent Memory","Agent memory is how an AI agent retains information beyond a single model call, so it can act coherently across many steps or sessions instead of forgetting everything each turn. It usually splits into short-term (working) memory — the running context of the current task, held in the context window — and long-term memory — facts, past interactions, and learned preferences stored outside the model, typically in a vector database or plain database and retrieved when relevant. People often further divide long-term memory into episodic (what happened before) and semantic (durable facts about the user or domain). For builders, memory is what turns a stateless chatbot into a product that remembers a customer's plan, past tickets, or brand voice. The practical challenge is curation: naively stuffing every past message into the prompt is expensive and degrades accuracy, so you summarize, score relevance, and retrieve only what each step actually needs.","Agent memory is how an agent retains information beyond one model call — short-term working context plus long-term stores it can search across sessions.",null,[11,14,17,20,23],{"slug":12,"name":13},"agent","Agent",{"slug":15,"name":16},"context-engineering","Context Engineering",{"slug":18,"name":19},"context-window","Context Window",{"slug":21,"name":22},"retrieval-augmented-generation","Retrieval-Augmented Generation (RAG)",{"slug":24,"name":25},"vector-database","Vector Database",[27,31,35,38,41,44,47,50,53,54,57,61],{"slug":28,"category":5,"name":29,"updated_at":30},"agent-budget","Agent Budget","2026-08-24T02:46:37+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"agent-checkpointing","Agent Checkpointing","2026-08-24T02:46:38+00:00",{"slug":36,"category":5,"name":37,"updated_at":30},"agent-handoff","Agent Handoff",{"slug":39,"category":5,"name":40,"updated_at":30},"agent-loop","Agent Loop",{"slug":42,"category":5,"name":43,"updated_at":34},"agent-sandbox","Agent Sandbox",{"slug":45,"category":5,"name":46,"updated_at":34},"agent-trajectory","Agent Trajectory",{"slug":48,"category":5,"name":49,"updated_at":30},"agentic-rag","Agentic RAG",{"slug":51,"category":5,"name":52,"updated_at":34},"computer-use","Computer Use",{"slug":15,"category":5,"name":16,"updated_at":30},{"slug":55,"category":5,"name":56,"updated_at":34},"dry-run","Dry Run",{"slug":58,"category":5,"name":59,"updated_at":60},"escalation-path","Escalation Path","2026-08-24T03:30:02+00:00",{"slug":62,"category":5,"name":63,"updated_at":30},"human-on-the-loop","Human-on-the-Loop"]