Definition
Agent Memory
Agent memory is how an AI agent retains and reuses information across turns and sessions, beyond what fits in a single context window. It spans short-term working memory within a task and long-term memory persisted in a store and recalled later, letting an agent remember past interactions, decisions, and learned facts instead of starting fresh every time.
Key takeaways
- Agent memory lets an AI agent retain and reuse information across turns and sessions, beyond what fits in one context window.
- It spans short-term working memory within a task and long-term memory persisted to a store and recalled later.
- Long-term memory typically reuses retrieval machinery — embedding and semantically recalling past facts into the prompt when relevant.
- In multi-tenant systems memory must be strictly scoped, since stale or leaked memory is worse than none.
A model's context window is finite and resets between sessions, so an agent with no memory is amnesiac — it cannot recall what happened earlier or what it learned last week. Agent memory is the architecture that overcomes this. Short-term memory keeps the current task's working state inside the context window; long-term memory writes salient information to an external store and retrieves the relevant pieces back into context when they are needed.
Long-term memory is typically built on the same retrieval machinery as grounding: facts, summaries, and past interactions are embedded and stored, then semantically recalled and injected into the prompt at the right moment. This keeps the context window focused on what is relevant now rather than dragging an ever-growing transcript along, which would be slow, costly, and eventually impossible.
Memory introduces its own discipline. What to remember, when to forget, and how to keep stored memories accurate are genuine design questions, and in a multi-tenant system memory must be strictly scoped so one workspace's recalled context can never surface in another's. Stale or leaked memory is worse than none.
Planoda persists agent context as tenant-scoped embeddings retrieved on demand, so an agent recalls relevant history inside its workspace boundary without carrying an unbounded transcript.
Related terms
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.
- Context WindowA context window is the maximum amount of text — measured in tokens — that a language model can consider at once, spanning its input and its output. Everything the model 'sees' for a given request must fit inside it: the prompt, retrieved data, conversation history, and the answer. When content exceeds the window, the oldest or least relevant parts must be dropped.
- EmbeddingAn embedding is a numeric vector that represents a piece of content — text, image, or other data — as a point in high-dimensional space, positioned so that similar items land near each other. Produced by a model trained to capture meaning, embeddings let software compare content by semantic similarity rather than by matching exact words.
- Retrieval-Augmented Generation (RAG)Retrieval-augmented generation (RAG) grounds a language model's answers in your own data by retrieving the most relevant documents at query time and feeding them into the model's context before it responds. Instead of relying solely on what the model memorized in training, RAG lets it answer from current, authoritative, private sources — sharply reducing hallucination.
- Row-Level Security (RLS)Row-level security (RLS) is a database feature that restricts which rows a query can read or modify based on the current user or context. Instead of relying solely on application code to filter data, the database itself enforces access policies on every query — a strong defense for multi-tenant systems where one workspace's data must never leak to another.