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This paper presents a computational framework for automatically compiling collected commentaries on classical Chinese texts, preserving contextual dependencies of inline notes via prompt chaining and cross-source clustering.
A case study exploring prompt chaining for automated scholarly report generation, presented as an arXiv paper.
Google released a free 421-page playbook on building AI agents, covering prompt chaining, memory, routing, MCP, multi-agent systems, and more, serving as an AI engineering curriculum.
A technical teardown of how multi-agent frameworks like CrewAI and AutoGen actually route information under the hood, revealing that they are essentially automated prompt-chaining loops. The article explains why agents get stuck in infinite loops due to context window inflation and missing deterministic stop conditions, offering practical advice for builders to treat agents as functional programming functions instead of human collaborators.
The article describes five key workflow patterns for building agentic AI systems in enterprise settings, as summarized by Anthropic: prompt chaining, routing, parallelization, orchestrator, and evaluator-optimizer, with tips to prefer simpler workflows before using full agents.