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Agent vs Workflow vs a Single Call

Three architectures sit under the word agent, and they differ by who decides what happens next. A workflow has its steps written in advance; an agent chooses its next action from what it just observed. Picking the wrong one is the most expensive early decision in an AI deployment, and picking the boring one is usually correct.

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RELATED CONCEPTS

AI Agents and Tool Use Tool / Function Calling Multi-Agent Orchestration Guardrails AI Cost and Unit Economics

LESSONS THAT TEACH THIS

Production AI Agents for FDEsBuild a support agent around one permitted outcome18 min→Production AI Agents for FDEsLab: build a baseline you can reproduce45 min→The FDE EngagementBound agent actions by authority, consequences and shared limits30 min→

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks→ML Infrastructure & GPUsExplain how the CUDA execution model maps to hardware, grids, blocks, warps, SMs.→LLM & GenAI FundamentalsWhat is a token, and why should an FDE care about tokenization?→ML Infrastructure & GPUsWalk me through the GPU memory hierarchy, registers, shared memory, L2, HBM. What lives where and why?→ML Infrastructure & GPUsWhat is warp divergence and why does it hurt performance?→RAG & Agent System DesignWhen is an agent the wrong answer? Argue against building one.→