What is an AI Agent?

Concept

An AI agent is a software system that has the capability to perform countless actions and tasks and achieve goals in a semi-independent way, using tools, information, and reasoning. It should have supervision, but not in such a demanding and constant way, only minimal supervision to know that everything is in order.
AI agents are only possible because of LLMs (Large Language Models); these are the models that provide agents with the ability to reason, analyze, and evaluate.

Some of the examples we can offer cover a variety of sectors and are broad enough for different types of companies:
A real estate agency has a house for sale and is booking appointments for viewings. Instead of bookings being made directly by phone with the estate agent responsible for the property, always stopping everything they are doing to answer calls, the AI agent schedules on their behalf.
If the potential client says “I want to visit tomorrow at 3 p.m.”, the AI agent can access the agent’s calendar, check availability, automatically book the viewing, and send confirmation to the client, all without human intervention.

Characteristics

Autonomy Operates independently - without constant human guidance, making decisions on its own based on goals or instructions.
Goal-directedness - Works toward specific objectives, planning and acting to achieve outcomes rather than just responding to single prompts.
Perception - Takes in information from its environment, text, data, tool outputs, APIs, files, web results, etc.
Action-taking - Can execute actions in the world: running code, browsing the web, calling APIs, sending messages, managing files, and more.
Memory - Maintains context across steps — short-term (within a task) and sometimes long-term (across sessions via storage or databases).
Reasoning & Planning - Breaks complex tasks into subtasks, decides on sequences of actions, and adjusts plans based on intermediate results.
Tool use - Leverages external tools (search, calculators, code interpreters, databases) to extend its capabilities beyond language alone.
Reactivity - Responds dynamically to changes, if an action fails or returns unexpected results, it adapts its approach.
Multi-step operation - Unlike a basic chatbot (one input → one output), agents operate in loops: observe → think → act → observe → repeat.
Feedback integration - Uses the results of its actions to inform next steps, effectively learning within a task what's working and what isn't.

Difference from AI Agents and AI Assistants

Sometimes there may be confusion, but their difference is simple.

AI assistants help you carry out tasks according to your request, they can perform simple tasks, recommend to the user what should be done, but in the end, it will always be the user who makes the choice and who is above in guiding the task and its completion.

AI agents are more proactive and independent; they execute tasks from A to B autonomously without necessarily needing a user to guide them during execution. They are capable of carrying out complex, multi-step tasks; the AI agent is the one guiding the task and the achievement of the goal, without necessarily needing a user in real time.

How do AI agents work?

Goal definition

The goal or instruction is the entire reason the agent exists, it defines what success looks like.
As autonomous as AI agents may be, we are the ones who have to give them the foundation they can work from. The goal and the step-by-step must be defined so that it can be achieved. As soon as that definition exists, the AI agent can move into action.
There are three main types of goals: a direct user instruction ("research the top 5 competitors and summarize their pricing"), a system-defined objective set by a developer (a customer support agent that must always resolve tickets), or a sub-goal handed down from another agent in a multi-agent pipeline.

User interaction

AI agents have to act with some kind of “trigger” that tells them it is time to start working on the goal. Whether it is when they receive a reply to an email, a form submission, a new message on Instagram, among many others.

Perceive

When they have the necessary trigger to start working on the goal, the comprehension part begins.
Perception is how the agent gathers everything it needs to know before taking any action.
This includes the task description and conversation history, outputs from previous actions (e.g. results from a web search it just ran), content from memory (past facts it stored), available tools and their descriptions, and any files, data, or external context provided upfront. The richer and more accurate the perception, the better the decisions the agent will make.

Act

After it has understood all the information and descriptions necessary to have the best possible output, we move to the action phase.
Action is where the AI agent will carry out the step-by-step so that the goal is successfully achieved. This may involve using external tools (via APIs and integrations, such as Gmail, Excel, Slack, HTTP…) to extract or carry out the necessary steps.
For example, if the goal defined for the AI agent is that for each email received it should categorize and place it into folders for each department (HR, Finance, IT, Operations, Customer Support), the agent will need to perform the following actions: access Gmail via API access, analyze the content of the email with an LLM, categorize it in Gmail into the available categories.

Observe

The observing part, after all the actions and steps toward achieving the goal, serves for the AI agent to understand whether the result it generated is correct and, if it is not, to correct or continuously improve itself.

What are the benefits of AI Agents?

We already understand what AI agents are capable of doing, but what are their benefits? It is not enough to just talk about their theory and how they work if we do not know concretely what possibilities and improvements they can bring.

Cost reduction - Agents handle repetitive, time-consuming tasks without needing salaries, breaks, or benefits, reducing operational costs significantly.
24/7 availability - Unlike human teams, agents never sleep. Customer support, order processing, and monitoring can run around the clock.
Speed & efficiency - Tasks that take humans hours (research, data entry, report generation) can be done in minutes or seconds.
Scalability - One agent can handle 10 requests or 10,000 without hiring more staff. Businesses can scale operations without scaling headcount.
Error reduction - Agents follow instructions precisely and don't get tired or distracted, reducing costly human errors in data processing, invoicing, or compliance tasks.
Customer experience - Faster responses, personalized interactions, and instant support improve customer satisfaction and retention.
Employee productivity - By offloading routine work to agents, employees can focus on higher-value creative and strategic work.
Data-driven decisions - Agents can continuously monitor data, generate reports, and surface insights that would take analysts days to compile.
Faster sales cycles - Agents can qualify leads, send follow-ups, schedule meetings, and nurture prospects automatically, keeping the pipeline moving 24/7.
Competitive advantage - Businesses that adopt agents early can move faster, serve customers better, and outpace competitors still relying on manual processes.

The Challenges of AI Agents

AI agents have quite a lot of good things, but we must know that not everything is roses. Before adopting AI agents, here are some points you should be aware of.

Here are the challenges of AI agents:

Reliability & hallucinations - Agents can confidently produce wrong information or take incorrect actions. In a business context, one bad decision in an automated workflow can have real consequences. That is why the initial phase of implementation and supervision is important.

Security & data privacy - Agents often need access to sensitive business data, emails, databases, and APIs. This creates new attack surfaces — a compromised agent could leak data or take harmful actions.

Integration complexity - Connecting agents to existing business tools (CRMs, ERPs, databases) requires technical work and ongoing maintenance as those systems change.

Lack of common sense - Agents can struggle with ambiguous situations that a human would handle intuitively. They tend to follow instructions too literally or get stuck in edge cases.

Oversight & control - As agents become more autonomous, it becomes harder to know what they're doing at any given moment. Businesses need proper monitoring and the ability to intervene.

Trust & adoption - Employees may resist handing off tasks to agents, especially if they've seen them make mistakes. Building internal trust takes time and demonstrated reliability.

Regulatory & compliance risk - In regulated industries like finance, healthcare, or legal, autonomous agent decisions may not meet compliance requirements or audit standards.

Runaway loops - Without proper stopping conditions, agents can get stuck in infinite loops, burning through API credits or causing unintended side effects in connected systems. It is important for the development of AI agents to be done by a properly trustworthy company.

Skill gap - Building, maintaining, and supervising AI agents requires a mix of AI knowledge, software engineering, and domain expertise that many businesses don't yet have in-house.

Best use cases for businesses

AI agents have capabilities for multiple use cases, but here are the use cases that have had the greatest usefulness and ROI (return on investment).

Customer support automation - Agents handle incoming tickets, answer FAQs, troubleshoot issues, escalate to humans when needed, and follow up, all without a support team member touching routine cases. Works exceptionally well for SaaS, e-commerce, and telecoms.

Data entry & document processing - Extracting data from invoices, contracts, forms, and PDFs and pushing it into the right systems (CRM, ERP, spreadsheet). One of the highest ROI use cases because it's pure repetitive work.

Report generation & business intelligence - Agents pull data from multiple sources, analyze it, write summaries, and deliver scheduled reports to stakeholders, replacing hours of manual analyst work weekly.

E-commerce order management - Handling order confirmations, shipping updates, return requests, and inventory alerts automatically, keeping customers informed without any manual effort.

Competitive intelligence - Agents continuously monitor competitor websites, pricing pages, job postings, news, and social media, surfacing relevant changes into a daily or weekly digest for the team.

Meeting summarization & follow-up - Agents join calls, transcribe them, extract action items, assign owners, and send follow-up emails, so nothing falls through the cracks after a meeting.

The future of your business isn't manual. It’s intelligent.

Let Loni automate your workflow with custom AI Agents.