# How Do AI Agents Work? > How AI agents work under the hood: the perceive, reason, act and observe loop, how they use tools, and why they sometimes get it wrong. - Canonical: https://arkatai.com/en/how-do-ai-agents-work/ - Site: Arkatai (https://arkatai.com) — agentic operations as a service - Language: en - Published: 2026-07-19 --- An AI agent works by running a loop: it perceives the state of the case, reasons about what to do, executes an action against a real system, and observes the result to decide the next step. It runs that loop as many times as needed until the work is done or it hits a limit that forces it to call a person. Everything else is detail on that mechanic. I will explain it without gratuitous jargon, because an executive does not need to know neural networks to make a sound call on an agent. What they need is to understand how it decides, why it sometimes gets it wrong, and how that error is contained. If you first want the definition and the components, they are in [what is an AI agent](/en/what-is-an-ai-agent/), and the business level is in [AI agents for business](/en/ai-agents-for-business/). ## The loop: perceive, reason, act, observe An agent does not solve a case in one shot. It solves it in steps, and at each step it repeats four moments. **It perceives.** It gathers the current state: the case that reached it, what it has done so far, the information in front of it. That is its snapshot of the moment. **It reasons.** The model interprets that snapshot and decides what to do next: query a piece of data, apply a rule, write a result, or stop and escalate. This is where the agent chooses, and it is why it handles cases no one programmed one by one. **It acts.** It executes the chosen action against a real system. It does not "think about" querying the ERP: it queries it. This is the part that separates an agent from an assistant that only talks. **It observes.** It looks at the result of its action — the data that came back, the error that fired — and that feeds the next perception. If the result is not what it expected, it adjusts. If it moves closer to the objective, it continues. Loop after loop, the agent chains these four moments until the case is closed. An example: to answer an order status query, it perceives the question, reasons that it needs the order number, asks for it or extracts it, queries the logistics system, observes the delivery date, and drafts the reply. Four moments, several loops, one result. ## How it uses tools to act Reasoning on its own changes nothing in your company. What turns a decision into an effect is **tool use**: the agent has a set of concrete actions it can invoke, each wired to one of your systems. It works like this: as it reasons, the model does not only produce text but can emit a tool call — "query order number X". The system runs that call against the real ERP, collects the response, and hands it back to the model, which folds it into its next round of reasoning. The model decides *which* tool to use and *with what data*, and the tool does the real work and bounds what the agent can touch. That tool layer is also where security lives: each tool carries explicit permissions for what it may read and what it may write. The standardization of this connection through a common protocol for linking agents to systems and data I explain in [MCP, the Model Context Protocol](/en/mcp-model-context-protocol/). And the detail of how each action is bounded is in [AI agent permissions and controls](/en/ai-agent-permissions-controls/). ## Why it sometimes gets it wrong Here is the part no vendor will underline for you, and the one I always put on the table: an agent is **probabilistic, not deterministic**. The model doing the reasoning does not run a formula with a single correct output, but generates the most plausible continuation given what it has seen. Most of the time that continuation is right. Sometimes it is not, and it does not fail the way a classic program fails — with an error that fires — but by producing something that looks reasonable and is not. Where those errors come from, in practice: - **Case ambiguity.** If the input allows two readings, the agent picks one, and not always the one you meant. - **Incomplete context.** If it lacks a piece of data or a rule that lived in someone's head and was never written down, it fills the gap with a guess. - **Made-up statements.** A model can produce a fact with full confidence even when it has no basis. That is why a serious agent queries the source instead of trusting what it "remembers". - **Chained steps.** In a long loop, a small drift in an early step carries forward and amplifies in the later ones. None of this is a defect that a better model "fixes". It is the nature of the tool, and working with it sensibly means designing the system to contain the error, not to deny it. ## How that error is contained That an agent can get things wrong does not make it useless. It makes it a system that needs brakes. A car engine can fail too, which is why it has brakes, warning lights and service intervals. With agents it is the same, and the brakes are three. **Evaluations.** Before an agent gets real work, you measure how it performs: test cases with known answers, quality thresholds, and systematic review of failures. It is the difference between "it seems to work" and "it works on 98% of these cases and these are the ones it misses". And evaluations are not a one-off exam at the start: they are re-run every time the model or the process changes, because any change can degrade something that used to work. **Escalation to humans.** Cases above a threshold — high amount, agent uncertainty, unforeseen situation — are routed to a person with the context already prepared. Where that threshold sits is a business decision, and one of the ones that most governs the system's real risk. **Traceability.** Every loop of the cycle is recorded: what it perceived, what it reasoned, which tool it invoked, what result it got. Without a trace you cannot debug a failure or defend a decision to an auditor. It is what turns "the system got it wrong" into "the system applied this rule with this data, here is the trail". The full framework is in [AI agent governance](/en/ai-agent-governance/). With these three brakes, the probabilistic nature stops being a problem and becomes a property that is measured and governed. It is precisely by skipping this design that so many pilots shine in a demo and die in production, a pattern I analyze in [why enterprise AI pilots fail](/en/why-enterprise-ai-pilots-fail/). ## The complete system The loop, the tools and the brakes are the moving parts, but they fit into a larger architecture — orchestration, memory, observability, controls — that gives the whole picture. That view is in [AI agent architecture](/en/ai-agent-architecture/). And if you want to move from understanding how one works to knowing how to build one that survives production, continue with [how to build an AI agent](/en/how-to-build-an-ai-agent/). ## Frequently Asked Questions ### How do AI agents work in simple terms? They run a loop: look at the state of the case, decide what to do, do it against a real system, and observe the result to decide the next step. They repeat until the work is done or until they hit a limit that forces them to hand the case to a person. ### What is tool use in an AI agent? It is the agent's ability to invoke concrete actions against your systems — query the ERP, write to the CRM, read an email — instead of merely generating text. The model decides which tool to use and with what data, and each tool carries permissions that bound what it can touch. ### Why does an AI agent sometimes get it wrong? Because the reasoning model is probabilistic: it generates the most plausible answer, not a single guaranteed output. Faced with ambiguity, incomplete context or long chains of steps, it can be right most of the time and wrong on some, sometimes with a result that looks correct and is not. ### Can you trust an agent if it can get things wrong? Yes, if the system is designed to contain the error: evaluations that measure its quality before and during operation, escalation to a person on the risky cases, and a trace of every decision. You do not trust that the model "is very good". You trust the brakes around the model.