The Plain Version First
An AI agent loop is a cycle. The model receives information, decides what to do, takes an action, checks the result, and then starts again with whatever new information that action produced.
That's it. The loop is what makes a single LLM call into a system that can do multi-step work without a human approving each step.
Regular LLM use: you send a message, the model generates a response, done. One input, one output. No loop. Agent loop: the model's output from step one becomes the input to step two, which feeds step three, and so on until the task is complete or the agent hits a termination condition it recognizes as done.
The concept is not complicated. The terminology around it is often deliberately obscured by people selling products that depend on the term sounding more technical than it is. The gap between what the words mean and how they're marketed is where most of the confusion lives. This article closes that gap.
The Four Steps, Concretely
Perceive, Plan, Act, Reflect. Four steps, repeated. Each one matters differently and fails in different ways.
Perceive is the input step. Reading a file. Fetching a URL. Checking a database. Receiving a user message. Reading the output of the previous action. The agent takes in information from whatever environment it has access to. This isn't passive. The agent often decides what to perceive, which means even the perception step involves judgment about what's worth looking at.
Plan is where most of the reasoning lives. The model takes its current goal, its current state, and its available tools, and decides what to do next. This is not a lookup table. It's inference. What does the situation call for? Which tool should I call? What do I still not know that I need to know before I can proceed? The planning step is where the model is functioning most like a reasoner rather than a text generator.
Act is tool execution. Not text generation. Writing a file. Running a code block. Sending an API request. Opening a browser tab. Querying a database. The action changes something in the environment, which is what distinguishes an agent from a chatbot. Chatbots generate text. Agents do things, and those things have effects that persist beyond the conversation.
Reflect is the step most simple agents skip, and the reason most simple agents fail at step four of a ten-step task. Did the action work? Did it produce the expected result? Does the plan need to change based on what just happened? Without a reflection step, the agent just proceeds regardless of whether the previous action succeeded, failed, or produced something unexpected. The failure mode is predictable: it gets to step six and the entire plan is built on a step three that silently went wrong.
What Makes It "Agentic" vs. Just a Prompt
The word "agentic" gets applied to many things that aren't actually agent loops. A single well-constructed prompt that produces a multi-part output is not an agent loop. A prompt chain where each response feeds the next manually, with a human reviewing each step, is not an agent loop either.
The defining characteristic is autonomy across steps. The model takes action, observes the result, and decides the next action without a human reviewing each transition. The loop runs. You watch it run. You intervene if it goes off the rails, but you don't approve each step. That autonomy is what makes the system "agentic" in any meaningful sense.
A travel booking agent that finds flights, checks prices, compares options against your stated criteria, and books the one that meets your requirements is running an agent loop. Each action produces information that feeds the next decision. No single prompt could do that work because each step depends on the specific result of the previous step, which is unknown at the time you start.
A code generation prompt that produces a function is not an agent loop. A system that generates a function, runs its tests, reads the test output, diagnoses failures, revises the code, and re-runs the tests until they pass is running an agent loop. Same final output type. Completely different architecture. The loop version handles cases the single-prompt version cannot, because it adapts to actual outcomes rather than anticipated ones.
Why Loops Fail
Agent loops fail in predictable ways. Understanding the failure modes is more useful than understanding the happy path, because the happy path is simple and the failure modes are where real projects get stuck.
Indefinite looping. No termination condition. The agent keeps acting, checking, acting again, and never decides it's done. This happens when the goal is never fully satisfiable, or when the reflection step never produces a "task complete" verdict. You watch the token count climb. Every agent system needs an explicit stopping condition specified before the loop starts.
Hallucinated tool calls. The agent plans to use a tool that doesn't exist in its available tool set, or calls a real tool with parameters it invented. The action step fails. Without a reflection step that handles failures gracefully, the agent either loops on the failure, trying the same nonexistent tool repeatedly, or proceeds as if the failed action succeeded. Both outcomes produce broken results that are sometimes hard to distinguish from successful ones.
Goal drift. The agent succeeds at each individual step but fails at the overall objective because the goal was underspecified at the start. You asked it to "improve the landing page." It makes changes. The changes are individually reasonable. The page is worse for what you actually needed. The agent didn't fail. The goal specification did. This is the most common failure in production agent systems and the hardest to debug because the logs show a successful run.
Context overflow. On long tasks, the agent's context window fills with the accumulated history of everything it has done. Later steps have less space available for the current task. Output quality degrades progressively. The agent starts making decisions based on a compressed or partially lost understanding of what happened earlier in the same run.
When Agent Loops Are the Right Tool
The loop is the right architecture for tasks that are genuinely multi-step and where each step depends on the actual result of the previous one, not on an anticipated result you could have specified upfront.
Research tasks that require following links, reading pages, synthesizing across sources, and iterating based on what each source says are natural fits. The agent can't know in advance what it will find, so it needs to observe and adjust. The number of steps is indeterminate at the start.
Code debugging is another clear match. Write a fix. Run the tests. Read the output. Determine whether the fix worked. If not, diagnose and revise. Each step depends on the actual result of the previous step. The agent can't short-circuit this by guessing what the test output will be.
Data extraction from variable-format sources works well with loops. Fetch a page. Parse the structure. If the structure doesn't match the expected format, adjust the parsing approach based on what you actually got. Move to the next source. Repeat. The loop handles variability that a static prompt can't anticipate because it doesn't know in advance what formats it will encounter.
The common thread: tasks where the next action cannot be determined without knowing the outcome of the current action. If you can write out all the steps in advance, you probably don't need a loop.
When Agent Loops Are Overkill
The loop is overkill when a single well-designed prompt would handle the task better. This covers more ground than agent enthusiasts acknowledge.
Summarization. Single prompt. The entire source material fits in context. There is no multi-step dependency. Each step does not depend on the outcome of the previous step. Running a loop over a document you could simply pass to a single prompt adds latency, adds cost, and introduces failure modes that don't exist in the direct approach.
Translation. Classification. Simple extraction from a structured document. Format conversion. All prompt tasks. The fact that you could technically structure them as agent loops doesn't mean you should. Loops add complexity. Complexity adds failure modes. Add complexity only when the task genuinely requires it.
The question to ask before building any agent loop: does this task require taking an action and then deciding the next step based on the actual outcome, where that outcome was not predictable in advance? If yes, a loop may be right. If the steps are predictable and don't depend on intermediate results, a well-structured prompt handles it more cleanly.
Most tasks described as "requiring an agent" in product pitches do not actually require an agent. They require a better prompt or a smarter prompt chain. Loops are harder to debug, more expensive to run, and more likely to fail silently than simpler alternatives.
The loop is not magic.
It's a cycle that runs until something says stop.
Design the termination condition first.