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An AI agent works in a loop: it receives a goal, reasons and plans, calls tools, observes the results, and repeats until the task is complete or it reaches a safety limit. Unlike a model that only generates text, an agent alternates between reasoning and action (the ReAct pattern) to self-correct and handle multiple steps on its own.
Key points
- The agent loop consists of 5 consecutive steps: receive a goal, reason/plan, call tools, observe the results, and repeat.
- The ReAct pattern (Reasoning + Acting) alternates natural-language reasoning with concrete actions, helping reduce hallucinations and self-correct errors.
- Five components make up an agent: the language model (brain), tools/APIs, memory, planner, and orchestration layer.
- The harness is the environment layer around the agent: it grants tool access, limits the number of loops, logs activity, and intervenes when the agent goes off track.
- Risks to note: error accumulation across multiple loops, hallucinations, cost and latency, and permission risks; start with a narrow scope, grant minimum permissions, and keep logs for review.
Understanding how an AI agent works is the first step that helps marketers and SME business owners confidently delegate tasks to artificial intelligence instead of using a chatbot to answer one question at a time. Unlike a conventional language model that only generates text, an AI agent is a system that can plan on its own, call external tools, observe results, and repeat until it achieves a goal. This article breaks down that operating mechanism in an easy-to-understand way, with technical accuracy and real marketing examples.
If you are not yet familiar with the basics, read What is an AI agent first for an overview before diving deeper into how an AI agent works.
The agent loop: receive a goal, reason, call tools, observe, and repeat
The heart of every AI agent is a loop (agent loop) that runs continuously until the goal is achieved or a safety limit is reached. How an AI agent works can be summarized in five consecutive steps:
- Receive a goal: The user gives a high-level request, for example, “analyze 50 customer comments and write a summary.”
- Reason and plan: The language model (LLM) breaks the goal into smaller steps and decides the next action.
- Call tools: The agent selects a suitable tool, such as querying a database, calling an API, or searching the web, with specific parameters.
- Observe the results: The results returned by the tool are fed back into the context for the model to “read” and evaluate.
- Repeat: Based on the new observation, the agent continues reasoning, calls more tools, or concludes the task is done and responds to the user.
This reasoning-and-action pattern is described in the ReAct (Reasoning + Acting) research by Google and Princeton University. The core idea: instead of only “thinking” or only “doing,” the agent alternates between natural-language reasoning and concrete actions, thereby reducing hallucinations and self-correcting when errors occur.
The components that make up an AI agent
To make the loop above work, a real agent needs multiple pieces working together. Understanding each component helps you see how an AI agent works at the architecture level:
- Language model (LLM): Acts as the reasoning and decision-making “brain.” This is where task decomposition and action selection happen.
- Tools: Functions or APIs that the agent is allowed to call, such as sending email, reading Google Sheets, or querying a CRM. Anthropic describes in detail how to define and pass tools to the model in the tool use documentation.
- Memory: Stores conversation context, previous step results, and sometimes long-term knowledge so the agent does not “forget” midway.
- Planner: The mechanism that breaks a large goal into a sequence of sub-tasks and orders their execution. Some agents plan ahead, while others plan step by step based on feedback.
- Orchestration layer: The software framework that keeps the loop running, manages the maximum number of iterations, and handles errors when tools fail.
OpenAI also provides guidance for building agents through function calling in the official documentation, where the model decides when to call a function and with which parameters. If you want to move from theory to practice, you can refer to how to build an AI agent step by step.
AI agent example in marketing
Imagine an agent supporting an email marketing campaign for an SME store. The goal given to the agent: “Find 20 customers who purchased within the last 90 days but have not returned, draft a promotional email, and schedule it.” The loop works like this:
- The agent reasons that it needs customer data first, so it calls a tool to query the CRM by purchase-date conditions.
- It observes the returned list, filters out those who already have a new order, then loops to the content-drafting step.
- The agent calls the LLM to write a personalized email draft for each customer group, then calls a scheduling tool to set the send time.
- If the email-sending API reports a formatting error, the agent reads the error message, fixes the parameters, and tries again instead of stopping midway.
The key point is that the human only gives the goal once, while the agent works through multiple steps on its own. This is the difference between a passive assistant and a proactive agent. You can learn more about use cases in AI agents in marketing automation and the full AI guide category for beginners.
The role of the environment and the “harness” around the agent
An agent does not run in a vacuum. It needs an environment layer around it to call tools, read results, and be constrained by safety limits. This layer is often called the harness, or the agent control framework. The harness is responsible for granting tool access, limiting the number of loops, logging activity, and intervening when the agent goes off track.
If this concept is still new to you, read What is harness AI to understand why the same language model can behave very differently when placed in different harnesses. Anthropic analyzes how to design effective agent systems in detail in Building effective agents, emphasizing that you should start with the simplest solution before adding the complexity of an agent.
Limitations and risks to keep in mind
Despite being powerful, AI agents are not a “silver bullet.” Understanding the limitations helps you deploy them safely:
- Error accumulation: Each step has a small chance of being wrong, but over many loops, mistakes can accumulate and drift away from the original goal.
- Hallucination: The LLM can still generate incorrect information, especially when it lacks tools to verify real data.
- Cost and latency: Every reasoning loop consumes tokens and time, so a meandering agent becomes expensive and slow.
- Permission risks: An agent granted access to real tools can cause consequences if there are no safeguards, such as accidentally sending mass emails.
- Human oversight is needed: For sensitive actions such as payments or data deletion, manual approval should remain in place.
Therefore, the practical advice for SMEs is to start with a narrow scope, grant minimum permissions, and always keep logs for review. Google Cloud also provides a useful overview of what AI agents are and how they operate at the enterprise level.
Frequently asked questions
How is an AI agent different from a chatbot? A chatbot mainly answers one question at a time based on text, while an agent can plan on its own, call tools, and repeat multiple steps to complete a complex goal.
Do you need to know programming to use an AI agent? Building a custom agent requires technical skills, but many no-code platforms such as n8n and automation tools already allow marketers to build simple agents without much code.
Can an agent learn and improve over time? Most agents today do not retrain themselves; they “remember” within the scope of session memory or a knowledge base, while their core capability comes from the underlying language model.
Are ReAct and the agent loop the same? The agent loop is the general operating framework of reasoning, acting, and observing; ReAct is a specific pattern that helps interleave reasoning and action effectively within that loop.
In short, once you understand how an AI agent works through the loop of reasoning, tool calling, and repetition, you will know how to delegate the right tasks, set safe guardrails, and harness the power of agents for marketing in a sustainable way.
