AI Agents Explained: How They Plan and Act
You have probably heard that AI agents can complete tasks on their own, but if you have ever wondered what is actually happening under the surface, this article is for you. The behaviour can look like magic from the outside, an instruction goes in and a finished result comes out, yet the mechanism is surprisingly understandable. An agent is not improvising wildly. It is following a clear loop, repeated over and over, that lets it work through a problem the way a methodical person might.
Understanding that loop is genuinely useful for a business decision-maker. It tells you what agents are good at, where they can go wrong, and how to set them up so they help rather than surprise you. We will walk through how an agent plans, how it acts, and how it checks itself, then look at what all of this means when you put an agent to work on real tasks.
The core idea: a loop, not a single answer
A chatbot produces one response and stops. An agent does something fundamentally different: it runs a cycle. At each turn of the cycle it thinks about what to do next, takes an action, looks at the result, and then decides whether the goal is met or whether another turn is needed. This repeating pattern is often described as a think-act-observe loop, and it is the heart of how every agent operates.
The power of the loop is that the agent does not need to know the entire path in advance. It only needs to work out the next sensible step, take it, and learn from what comes back. Just as you would not plan every footstep before crossing an unfamiliar city, an agent navigates a task one informed step at a time, adjusting as it discovers more. This is what lets agents handle messy, real-world goals where the route cannot be fully mapped beforehand.
Step one: planning
When an agent receives a goal, its first job is to make sense of it. A goal like "resolve this customer's billing question" is not a single action; it is a small project. The agent breaks it down into a sequence of smaller, achievable steps: identify the customer, find their account, locate the relevant charge, check it against the records, and prepare a clear explanation or correction.
Good planning is what separates a useful agent from a confused one. By decomposing a large goal into ordered sub-tasks, the agent gives itself a manageable next move at every point instead of trying to solve everything at once. Crucially, the plan is not set in stone. If the agent discovers partway through that its initial plan was wrong, perhaps the customer it identified turns out to be the wrong one, it can revise the plan and carry on. Planning in an agent is a living process, not a one-time blueprint.
Step two: acting
A plan is worthless without the ability to carry it out, and this is where tools come in. An agent acts on the world by calling tools: looking something up, reading a record, running a calculation, sending a message, updating a system. Each action is the agent reaching out beyond its own text to make something happen or to gather a fact it does not yet have.
This is the difference between an agent and a model that merely talks. A language model on its own can describe how to look up an order; an agent can actually look it up, because it has been given a tool that performs that lookup and the judgment to know when to use it. The richer and more reliable the agent's toolset, the more it can accomplish. We cover this in detail in our guide to how AI agents use tools to get work done, and it is one of the most important factors in whether an agent is genuinely useful.
Step three: observing and adjusting
After every action, the agent looks at what came back. Did the lookup return the record it expected? Did the calculation produce a sensible number? Did the action succeed or fail? This observation step is what makes an agent robust. Rather than barrelling ahead regardless of results, it reads the outcome of each step and feeds that understanding into its next decision.
When something goes wrong, and in real work something often does, the observe step is what lets the agent recover. If a tool returns an error or an empty result, a well-built agent notices, reconsiders, and tries a different approach: searching with different terms, asking a clarifying question, or escalating to a human. This capacity for self-correction, repeated turn after turn, is what allows an agent to push through obstacles that would stop a rigid, scripted system in its tracks.
Putting the three steps together
Seeing the loop laid out as a sequence makes the rhythm clear. The same three moves repeat until the agent decides the goal is complete, with each cycle building on what the last one revealed.
| Phase | What happens |
|---|---|
| Plan | Decide the next sensible step toward the goal |
| Act | Use a tool to take that step |
| Observe | Read the result and decide what is next |
That loop continues, plan, act, observe, plan again, until the goal is reached or the agent decides it needs help. The simplicity of the cycle is deceptive; repeated enough times with good tools and good judgment, it can accomplish remarkably complex work.
What this means for using agents well
Once you understand the loop, several practical lessons follow naturally. First, an agent is only as good as the tools and information you give it. If the agent cannot access the record it needs, no amount of clever planning will help; it is acting blind. Giving an agent reliable tools and accurate, current data is the single highest-leverage thing you can do to make it perform.
Second, clear goals matter enormously. Because the agent plans from the goal you set, a vague or ambiguous goal leads to a vague or wandering process. The more precisely you define what success looks like, the more focused the agent's loop becomes. This is quite different from a rule-based system, where you script every branch in advance; with an agent you describe the destination and let it find the path.
Third, oversight should match the stakes. Because the agent takes real actions, you want checkpoints for anything consequential. The observe step gives you a natural place to insert a human: the agent can pause and ask for approval before an irreversible action, then continue once cleared. This keeps the speed of automation while preserving human control where it counts.
Where agents fit in a business
The loop is well suited to tasks that are multi-step, somewhat variable, and built on information the agent can reach through tools. Customer support is a natural fit, which is why agentic assistants on channels like our WhatsApp AI chatbot can resolve requests end to end rather than just answering questions. Back-office processes that involve gathering information from several systems and acting on it are another strong fit. For a fuller picture of where this technology is heading, our overview of agentic AI sets the broader context, and our explainer on what artificial intelligence is covers the fundamentals beneath it all.
Frequently asked questions
How is an AI agent different from a chatbot?+
What happens when an agent makes a mistake?+
Do I need to write code to use an agent?+
What makes one agent more capable than another?+
References
- Stanford HAI, AI Index report on agent capabilities, hai.stanford.edu
- Anthropic, guidance on building effective agents, anthropic.com/news/model-context-protocol
Want to see the agent loop applied to your customer conversations? Explore our WhatsApp AI chatbot, or get in touch to talk through the right starting point for your team.