AI Agents: Loops, Architecture, and Production Patterns
An agent is an LLM running in a loop that decides its own next action — call a tool, look at the result, decide again — until the task is done or a budget says stop. The hard, valuable part is everything wrapped around that loop: context engineering, budgets, guardrails, and traces. This topic maps the loop, the workflows-vs-agents decision, and the 2025 production skeleton.
01.The Problem: Some Tasks Have No Scripted Path
Ask a plain LLM chatbot:
"Fix the failing tests in the payments module."
It cannot do this in one answer. Nobody can write the instruction list in advance, because the right steps depend on what each previous step revealed:
- Run the tests → 3 fail.
- Read the first failure → a schema changed.
- Search for its users → two files.
- Edit one → re-run → a new failure appears that nobody predicted.
- …
The number of steps, and their order, is only known while doing the task. You cannot hard-code that. A hard-coded pipeline (called a workflow, more below) is a train on rails — brilliant when you know the route, helpless when you do not.
So the question becomes
What if the model itself held the steering wheel — deciding, turn by turn, what to do next, looking at each result before choosing again?
That is an agent. And this topic is what agents really are, when to use one, and everything production wraps around them.
The Agent Loop, Annotated
The Agent Loop, Annotated
One LLM call per turn choosing among act/ask/answer; production value comes from budgets, gates, and traces wrapped around that loop.
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