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Tech talk series

AI tech talk series: Agent context layer

97% of enterprise leaders say context is critical to their AI strategy, but only 4% have built for it (Redis, State of context engineering 2026), and that gap is where most agent projects stall. The model works; it's the layer underneath, memory that resets, retrieval that slows at scale, stale state, and a climbing LLM bill, that doesn't.

This five-part series builds a context-aware app on Redis Iris in 15-minute sessions, adding memory, data integration, retrieval, and caching step by step. By the end, you'll have a working app and a practical understanding of how the context layer runs in production.

Register for the series

Session highlights:

  • Session 1
    Search

    Build the base app for the series and get fast, sub-millisecond hybrid retrieval (vector plus filters) working with Redis Search, even as your data grows.

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  • Session 2
    Compounding memory

    Add Agent Memory so context carries across turns, sessions, and restarts, without depending on the context window to hold everything.

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  • Session 3
    Context retrieving at scale

    Add the Context Gateway so the agent can navigate multiple database tables in a single turn using one predefined schema, replacing a tool zoo.

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  • Session 4
    Semantic caching: Making cost part of the architecture

    Add Redis semantic caching to serve repeat and near-duplicate queries from cache instead of the model, the same approach that cut Mangoes.ai's LLM spend by 70%.

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  • Session 5
    Your context aware apps

    Wrap up with real use cases, like support agents that need memory and fresh order data, and fraud checks that need answers before a transaction clears.

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Speaker:

Redis

Redis

Samuel Agbede

Developer Advocate

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