Multi-step AI agents are only as capable as the context they can access.
Without persistent, shared memory, agents repeat themselves, lose the thread between steps, and can't improve from one interaction to the next, making production-grade workflows harder to trust and harder to scale. Give your agents a real-time context engine that persists across sessions, syncs across steps, and gets smarter over time, so your AI workflows don't just run, they compound.
Join to see a hands on demo and learn how to build:
Agents that retain memory across sessions, carrying context forward so each step builds on the last.
Agents in a pipeline share the same real-time state, eliminating context gaps in complex workflows.
Long-horizon workflows that run with the consistency and durability production environments demand.
Persistent context that allows agents to learn from prior interactions, growing more useful over time.
Speakers
Redis
Bhavana Giri
Developer Advocate
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