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Definitive answer

What is agentic memory and context management?

Agentic memory is the layer that gives an AI agent short-term scratchpads, long-term episodic memory, and semantic recall across sessions. Context management is the discipline of curating what the LLM sees on every step — tools, retrievals, prior turns, and instructions — so the agent stays grounded, accurate, and within its context budget.

Three types of agent memory

A production agent rarely uses one memory store. It uses a stack tuned to the workflow.

  • Working memory — the active scratchpad and tool results inside one task.
  • Episodic memory — what happened in past sessions with this user/account.
  • Semantic memory — durable facts, policies, embeddings, and knowledge graphs.

Context as a finite resource

Context windows are large but not free. Implement Agentic treats context as a budget: ≤10 tools per agent, sub-agent decomposition with compaction, hybrid retrieval (BM25 + dense + RRF + reranker, ~91% recall@10) instead of naive vector search, and aggressive summarization between turns.

Frequently asked

Do I need a vector database?+

Often, yes — but not always. pgvector inside an existing Postgres is usually enough to start. Switch to a dedicated vector DB only when scale, hybrid search, or multi-tenant ACLs justify it.

How do you prevent memory poisoning?+

Permission-aware writes, provenance metadata on every entry, expiration policies, and review queues for any memory written from untrusted user input.

Related reading

Enterprise AI Memory & Context Systems (canonical guide)Enterprise AI Context & Memory SystemsRAG & Knowledge SystemsWhat is closed-loop AI?

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