How LangChain grew self-serve analytics questions 40x by giving its AI agent layered context
LangChain gave its Hex agent layered context, turning self-serve analytics questions from occasional to routine.
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LangChain gave its Hex agent layered context, turning self-serve analytics questions from occasional to routine.
Every time an agent takes a step, it re-reads its entire context before acting. Here is how prompt caching for OpenAI models on Amazon Bedrock lets your agent remember, cutting input cost and latency without resending the same tools, instructions, and history every turn.
Long-running agents degrade as context piles up. Learn how offloading, summarization, skills, subagents, and memory keep your agent's context window focused.