From Chatbots to Systems
An LLM is just a text predictor. An Agent is a system that uses an LLM to reason, plan, and execute. Explore the five architectural patterns that bridge this gap.
Before we build complex systems, we must understand the fundamental shift. A standard Chatbot is a direct pipe. An Agent introduces a feedback loop with the world.
The output of one step becomes the input of the next. This allows for checkpoints where you (or the system) can inspect and modify intermediate states.
Edit the list below to see how it changes the final result.
The LLM acts as a traffic controller, classifying intent to send the request to the specialized downstream workflow.
Running tasks concurrently reduces latency. Useful for processing large documents or generating multiple distinct perspectives at once.
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A central "Brain" breaks down a high-level goal into tasks and assigns them to workers. It can adapt the plan if a worker fails.
The "Quality Loop". One LLM generates, another grades. If the grade is below the threshold, it sends feedback to the generator to try again.
while (score < threshold) {
feedback = evaluate(draft);
draft = improve(draft, feedback);
}