Context-Aware Agent Patterns

An evening for AI builders, hosted by Redis BOOKING ESSENTIAL - LIMITED SEATS *How do you keep an AI agent’s context fresh as it navigates complex data sources?* Agent answers depend on information spread across business systems, documents, tool results and past interactions. As tasks progress, that information accumulates and changes. Errors get carried forward, relevant details get missed and conflicting facts make decisions less reliable. Sending more context can introduce further distractions while increasing latency and token costs. The agent harness controls what the model retrieves, what it remembers and what gets included at each step. How we design those mechanisms shapes the agent’s ability to use information effectively. Join us for two practical talks on real-time retrieval and agent memory. Through demonstrations built with Redis, we’ll examine how context fails and explore engineering patterns for keeping it useful across tasks and conversations. Bring your hardest problem. Tell us what’s breaking in your agent when you attend and we’ll try to cover it. REGISTER HERE