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How do you optimize a RAG or agent system for cost and latency in production?

RAG and agents get expensive and slow fast: retrieval plus reranking plus big-model calls, multiplied across agent steps. The signal is naming the dominant cost first, then the levers that actually move it. Here is the playbook.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

RAG and agents get expensive and slow fast: retrieval plus reranking plus big-model calls, multiplied across agent steps. The signal is naming the dominant cost first, then the levers that actually move it. Here is the playbook.

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UP NEXT ON YOUR JOURNEY
Next in this trackHow do you implement guardrails for an autonomous agent to prevent harmful or irreversible actions?Next in this trackWhat is reranking in a RAG pipeline, and why does a cross-encoder reranker improve results?Next in this trackHow do you combine lexical (BM25) and semantic (vector) retrieval, and what is Reciprocal Rank Fusion?
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