1 link tagged with all of: agent-memory + redis-iris + data-retrieval
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The author recounts the pitfalls of building a custom context layer—handling memory, data retrieval, caching and permissions—only to face endless complexity. They found that Redis Iris bundles syncing, search, semantic caching and memory into a single context engine that sits close to the agent runtime.
- Building a "simple" memory feature for an agent inevitably explodes into a full context engine covering memory categories, expiry, permissions, and privacy.
- Connecting to real data sources creates a cascade of problems: schema drift, stale data, expensive queries, and cross-system joins across silos like databases, PDFs, emails, and tickets.
- Getting context volume wrong breaks the agent in both directions—too much data raises cost and confuses the model, too little causes hallucination, and improper access risks security breaches.
- Redis Iris packages data sync, retrieval, short/long-term memory, search, and semantic caching (LangCache) into one runtime-adjacent platform, replacing custom-built glue code across multiple services.