I spent a weekend chasing what I thought would be a tiny “memory” feature for my agent—and ended up tangled in a full-blown context engine. At first I believed I could just log conversations and call it a day. Instead I faced questions about what to store (preferences, task history, deleted items), how long to keep it, who gets access, and what “deleted” even means in various contexts. Memory immediately split into multiple categories, each demanding its own expiry rules, searchability, permissions and privacy settings. Then came data retrieval. I assumed connecting to existing databases or APIs would be straightforward, but I ran into schema changes, permission checks, expensive queries, stale records and cross-system joins. Structured tables, PDFs, emails and support tickets all live in different silos. I’d need sync pipelines, search indexes, fallback logic and permission layers. If the agent got too much data it cost more and confused the model. Too little and it hallucinated. If it saw data it shouldn’t, I risked a security breach. By the time I built caches and query guards, I’d wept twice. What I needed was an intermediary layer to shape and serve context at runtime. That’s where Redis Iris enters. Built on Redis’s low-latency core, it combines data integration for fresh operational data, a context retriever, both short- and long-term memory modules, Redis Search and a semantic caching tool called LangCache. Instead of custom glue across half a dozen services, Iris offers a single, battle-tested platform close to the runtime. It handles syncing, search, state and caching so I can focus on building agent logic rather than reinventing infrastructure.