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What is it?
An agent is software that receives a goal and then independently determines the steps needed to reach it. It takes in information from its environment, selects an action, and carries it out, using each result to inform the next decision.
In AI applications, an agent typically runs on a language model for reasoning, combined with memory and access to tools. That makes it more than a function that executes a single action: it is a component that continuously decides and acts until the task is complete.
Why it matters for SMEs
Many business processes are a chain of small decisions and actions that are currently handled manually or passed between people and systems in separate steps. An agent can pick up that entire chain without anyone needing to hand it off each time.
- Routine tasks are fully taken over: the agent completes the work, not just the first step, so it genuinely leaves your team's plate.
- Processes run without manual handoffs: as soon as a trigger arrives, the agent starts immediately, including outside working hours.
- Multiple agents working together scale processes without adding headcount: a collaboration of agents can handle complex workflows that would be too large for a single system.
For SMEs this means expanding capacity without hiring straight away: the agent handles the routine work, your people handle the work that requires judgment.
How it works
An agent works in a short cycle that repeats until the goal is reached. The mechanism is straightforward, but the combination of perceiving, deciding, and acting is what makes it powerful.
- Perceive: the agent receives a trigger or reads its environment, such as an email, a form submission, or a system event.
- Reason: based on the input and its instructions, the agent determines which step to take next.
- Act: the agent carries out the chosen action, for example looking something up, drafting a message, or sending a notification.
- Evaluate: the agent checks whether the goal is met, whether another step is needed, or whether a person should be brought in.
You decide in advance which actions the agent may take and at which point it must escalate. That keeps control with you, even when the agent is working autonomously.
Example in practice
Picture a staffing agency receiving dozens of applications each day by email and through a web form. An agent reads each application, compares the profile against open vacancies, adds the candidate to the CRM with the right tags, and sends a confirmation. Candidates who do not match any open role are flagged for the weekly talent pool review. The recruiter arrives in the morning to a shortlist of matched candidates, ready for a first conversation.
Comparison and misconceptions
An agent differs from standard automation in that it decides which step to take based on the situation rather than following a fixed sequence. Classical automation applies a set of pre-written rules; an agent adapts its approach based on what it observes.
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