Agent Architecture
Single agent, agent + RAG, supervisor, workflow graphs — and the tradeoffs between them.
One model, one loop, a small set of tools, a hard step limit — the default architecture until measurements prove it insufficient.
An agent whose knowledge lives outside the model: retrieval is either a tool the agent chooses to call or a step that always runs before the model sees the question.
One coordinating agent delegates sub-tasks to specialised worker agents with isolated contexts — buying tool partitioning and parallelism at the price of coordination overhead and a single bottleneck.
A typed state object moved through a fixed graph of steps with deterministic and conditional edges, capped retries, and checkpoints — the most debuggable way to use LLMs on a task whose shape you already know.
Classify the intent once with a cheap model, then dispatch to a specialised handler — plain code, a single LLM call, a workflow, or an agent — so that each request pays only for the machinery it needs.
A side-by-side of single agent, agent + RAG, router, workflow, supervisor, and multi-agent on complexity, latency, cost, reliability, and debuggability — and the handful of questions that decide between them.