Enterprise agentic AI systems inherently require distributed system architectures to handle production challenges like distributed memory, streaming I/O with LLMs, stateful orchestration, semantic search, and resilient deployment across clusters. Key requirements include managing conversation history across nodes, handling LLM streaming failures and network partitions, orchestrating multi-agent workflows, integrating with vector databases for RAG, and ensuring fault tolerance across regions. Building production-ready agentic systems demands robust distributed systems frameworks rather than simple demo-style monolithic approaches.
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Distributed memoryStreaming I/O with LLMsStateful orchestrationSemantic search and context generationRunning resilient components in clusters and regionsSummarySort: