Code, LLM Calls, or AI Agents: A Developer Breaks Down When to Use Each

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A developer set out to demystify what an AI agent actually is after finding job postings full of the term but lacking clear explanation. Through a hands-on Go project, they built four endpoints of increasing complexity — from a stateless LLM call to a multi-tool agent loop — without using any framework. The core finding: an agent is simply a loop where the model can request tool executions, with the developer's code running those tools and feeding results back until a final answer is reached. Most agent challenges, such as runaway loops, partial failures, and context overflow, map directly to well-known engineering problems like circuit breakers, sagas, and bounded caches. The project also raised a pointed question: if plain code can reliably solve a problem, there may be little reason to route it through a language model at all.

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