**Redis Iris** is a context and memory platform designed for agentic pull architectures. It replaces static RAG with dynamic, live-synced data, semantic tool access, and session management to handle high-frequency AI agent requests at scale.
* Delivers petabyte-scale retrieval with sub-millisecond latency by optimizing costs (99% flash/SSD, 1% RAM).
* Auto-generates MCP tools via Pydantic models, enabling agents to query business data directly with row-level access controls.
* Uses CDC pipelines for continuous synchronization with sources like Snowflake, Databricks, and Postgres.
* Delivers petabyte-scale retrieval with sub-millisecond latency by optimizing costs (99% flash/SSD, 1% RAM).
* Auto-generates MCP tools via Pydantic models, enabling agents to query business data directly with row-level access controls.
* Uses CDC pipelines for continuous synchronization with sources like Snowflake, Databricks, and Postgres.
