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Want smarter agents that stay consistent, learn over time, and stop falling for confident prompts? Start with memory.

That’s the focus of this piece from our very own Manvinder Singh, VP of Product Management for AI. He breaks down why the next leap in AI isn’t about bigger models, but about retaining context, recalling the right data at the right time, and applying learned patterns across sessions.

When agents lack memory, reasoning stays fragile and easy to derail. When memory is built in from the start, agents become more stable, more adaptive, and far more trustworthy in high-stakes decisions.

AI coding agents can generate working Redis code in seconds. But working isn’t the same as production-ready. Too often, they reach for outdated patterns, miss newer data structures, and skip the performance guardrails that Redis experts rely on.

Our new agent skill changes that. With a single command, you inject up-to-date, opinionated Redis expertise directly into your agent’s context. It knows when to use hashes instead of large JSON blobs, avoids blocking calls like KEYS *, chooses the right data structures for caching, rate limiting, vector search, and agent memory, and defaults to patterns that scale.

AI still writes the code. It just writes Redis the way experts would.