A vector database gives agents memory — the ability to recall by meaning, not just exact words. Systems like Pinecone, Weaviate, and Chroma store semantic embeddings so agents can ground decisions in past experience.
Typical memory layers include:
Short-term context — Immediate recall from current tasks or chats.
Long-term memory — Persistent embeddings in vector stores.
Shared memory — Team-level recall for multi-agent systems.
By combining vector memory with real-time events, agents can reason over both what just happened and what has happened before — enabling contextual awareness across time.
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Agentic AI in Action: Why Real-Time Is Non-Negotiable
This post breaks down explains how EDA can help you use LLMs and RAG to build smarter applications that work with live, real-time data.
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Solace Aims to Address Complex, Multi-Agent Workflows with Agent Mesh Enterprise Edition
SAM Enterprise Edition leverages the vendor's event messaging and governance capabilities, integrating agents with enterprise systems and...
