Long-horizon tasks: building agents that work over hours & days

Long-horizon AI agents are evolving to handle complex, multi-step tasks that span hours or even days, necessitating robust memory infrastructure beyond larger models. These agents are employed across domains like coding, research, and enterprise operations, where they maintain state across extended sessions to perform tasks such as refactoring codebases, conducting deep research, and managing enterprise workflows. Despite their potential, these agents often encounter challenges like context rot, memory drift, goal coherence loss, and error compounding, which stem from limitations in memory management rather than the models themselves. To address these issues, a comprehensive memory system comprising working, episodic, semantic, and procedural memory is crucial, along with patterns like checkpoint-and-resume, plan-then-execute, and context isolation. Redis Iris offers a real-time context engine that provides durable state, fast retrieval, and data freshness, enabling agents to maintain continuity and relevance over long tasks without the compounding latency and cost issues associated with traditional memory approaches.

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