Technical guide demonstrates practical engineering frameworks for LLM agents and RAG systems, highlighting methods to deploy dependable action-oriented production workflows.
This handbook exists because of a gap we kept running into ourselves. Most material on agentic AI andretrieval-augmented generation sits at one of two extremes: either a breathless case for how agentschange everything, with no working code attached to the claim, or a narrow tool tutorial that teaches aframework's own syntax and says nothing about the engineering judgement underneath it. Neitherextreme is much use to someone who actually has to ship a system, with real users depending on itsanswers, that a human eventually has to trust with an action rather than only a sentence
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Harry Patria (2026) studied this question.
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