AI Agents Explained: How LLMs Coordinate Multi-Step Tasks Beyond Simple Prompts

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A technical explainer published on DEV Community outlines what truly distinguishes an AI agent from a standard LLM-powered application. Unlike fixed pipelines, an AI agent uses a language model to dynamically decide which action to take next, executing tools and evaluating results in an iterative loop until a goal is reached. The article uses a practical example — finding a failed deployment, diagnosing its cause, filing a GitHub issue, and sending a Slack alert — to illustrate why some tasks cannot be handled by a single API call. It draws a clear line between predictable, deterministic workflows, which do not require agents, and open-ended tasks where the required steps are unknown in advance. The guide advises developers to default to simpler deterministic approaches and adopt agents only when dynamic, multi-step decision-making is genuinely necessary.

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