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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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