AI agents explained: how an agent plans, builds and tests software

From chatbot to agent: what's the difference?

Ask a chatbot 'how do I build a sign-in page?' and it gives you an explanation and maybe a code snippet, and then its job is done. Running it, testing it and finding the bugs are all on you. Give an AI agent the same goal and it does something very different: it creates the files, wires up the database, runs the page, tries signing in, and if an error appears, it reads it and fixes it.

Both rely on a large language model (LLM) underneath. The difference is less about how smart the model is and more about the system around it: an agent has tools it can use, a memory of what it has done, and a work loop that lets it try and correct. If you want to understand the models themselves first, read LLMs for business owners.

Think of the difference between asking an engineer friend how to mount a shelf and hiring a technician who brings tools, mounts it and checks it holds. The first gives you information; the second hands you a result. That is exactly what has changed in software: the question is no longer only what the model knows, but what it can do, and how it verifies that what it did is right.

The agent loop: understand, plan, act, observe

Whatever an agent is called and however its interface looks, most of them run a repeating loop, sometimes called an agentic workflow. Understanding this loop helps you predict what the agent will do and write better requests:

1. 1
   Understand the goal

   The agent reads your request and extracts what's needed: who the users are, which screens, which constraints. When something is unclear, a good agent asks before it starts instead of guessing.

2. 2
   Plan

   It splits the goal into smaller tasks: create the database tables, then an API for orders, then the display pages, then the tests. A good plan is shown to you so you can correct it.

3. 3
   Act with tools

   For each task, the agent picks the right tool: write a file, run a command, query the database, open the page in a browser. This is called tool calling.

4. 4
   Observe

   After each step the agent reads the outcome: did the build pass? Did an error appear? Did the tests pass? This observation is what separates an agent from a text generator.

5. 5
   Correct or continue

   If a step failed, it analyzes why and tries another approach. If it succeeded, it moves to the next task in the plan, until the goal is met or it reaches a point that needs your decision.

A good agent isn't measured by never being wrong, but by noticing its mistakes and fixing them.

Notice that this loop runs dozens of times in a single project. That's why an agent may spend minutes on a task that looks simple to you: it isn't only writing; it writes, runs, reads and revises.

Tools: what can an agent actually touch?

A language model on its own can't create a file or call a server. An agent's real capability comes from the tools the system gives it. In software building, these are the main tools and what they enable:

Some systems use open standards such as MCP to connect an agent to extra tools and data sources in a consistent way. The principle is the same: the clearer and more precise the tools, the more reliable the agent. In Fanann, the agent works with a managed database, hosting and ready integrations, so it doesn't have to invent infrastructure from scratch.

How an agent builds an app end to end

Take a realistic example: a café owner in Jeddah writes 'I want a pre-order app: the customer picks a drink, pays, and picks it up without waiting.' Here's what typically happens inside a full-stack building agent:

1. Clarify and plan

The agent asks a few short questions: multiple branches? Does the customer need an account, or is a phone number enough? Which payment methods? Then it presents a plan: products, orders and customers tables; a menu screen; a cart; checkout; a barista screen showing new orders; and a notification when an order is ready.

2. Build layer by layer

It starts with the database because everything rests on it, then the server and APIs, then the right-to-left Arabic interface, then local payments. After each layer it runs what it built and confirms it works before moving on.

3. Test

The agent writes tests for the important paths: a new order shows up on the barista screen, a failed payment never creates a confirmed order, a customer never sees anyone else's orders. Then it opens the app in a browser and walks the flow as a customer would.

4. Deploy and iterate

After your approval, it deploys the app and connects the domain, and it can build iOS and Android apps from the same conversation. When you ask for a change later, the loop starts again on a smaller scale. For similar examples, see restaurant and café solutions.

Where agents go wrong

Agents are powerful, but not infallible. Knowing the common failure patterns helps you catch them early:

• Vague goals: Say 'make it better' and the agent will pick an interpretation that may not be what you meant.
• Hallucination: It may assume a library or function exists when it doesn't, or report something as done when it isn't.
• Lost context on long tasks: In a big project it may forget an early decision and break something that used to work.
• Loops: It keeps trying to fix the same error the same way without making progress.
• Shallow tests: It may write tests that always pass because they don't check the right thing.
• Scope creep: It edits files you never asked it to touch while fixing something else.

Most of these errors are less a flaw in the model's intelligence than the result of missing information or missing checkpoints. So most of the fix is in your hands: a clearer goal, smaller tasks, and hands-on testing after every important step. The longer the project, the more it helps to keep a short note of key decisions (table names, pricing rules, permissions) and remind the agent of it when needed.

For a full pre-launch checklist, see Can you trust AI-generated code?.

Guardrails and human checkpoints

The answer isn't to watch every line the agent writes. It's to have clear guardrails in the system, plus points where the agent stops and waits for your decision. These are the guardrails worth asking about in any tool:

Teams that build agents also rely on evals: sets of known tasks and cases the agent is measured against repeatedly, to catch quality regressions before they reach users. As a user you can apply the same idea at small scale: keep a list of five to ten scenarios you run after every big change.

How to work with an agent effectively

Working with an agent is like working with a talented developer who joined your team today: smart and fast, but new to your business. The clearer your instructions, the closer the result. Three habits make the difference:

1. State acceptance criteria: Don't just say 'add payments'; say 'add payments, confirm the order only after payment succeeds, and notify the customer.'
2. Ask for the plan first on big tasks: Review it, cut what you don't need, then approve.
3. Split the work: One feature per request, and test it before moving on.

Another useful habit is explaining the why, not just the what. When the agent knows a screen exists to cut the customer's wait at the counter, it makes better calls on the small details you never mentioned, like button order or what appears first on the menu.

For more examples and templates, read How to write prompts that build better apps, and if the whole idea is new to you, start with What is vibe coding?.

Questions to ask before choosing an agent-based tool

Every tool today says it uses agents. Before committing to one for your project, ask these questions and test the answers yourself rather than relying on marketing pages:

• Does the agent show its plan and wait for my approval before big changes?
• Does it actually test what it builds, or only write code?
• Can I roll back to any earlier version easily?
• Do I own the full code and can I export it to GitHub?
• Does it understand requests in Arabic and build a correct RTL interface?
• Does it support local payments and phone sign-in without complex integrations?
• Where is my data hosted, and are there in-region hosting options?

For a fair look at the options, see AI app builders compared, or try it yourself with the demo. To dig into the terms used here, visit the glossary.

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