What is an AI agent and what is it really used for?

An AI agent is a system capable of making decisions and executing actions autonomously, using language models, data and tools to achieve specific goals. Unlike traditional chatbots and rule-based automations, AI agents can plan tasks, interact with enterprise systems, learn from outcomes and adapt to context. Their real value emerges in business use cases such as process orchestration, advanced support, operational analytics or the automation of complex workflows.

What is an AI agent?

An AI agent is an artificial intelligence system designed to achieve objectives autonomously, not just to generate responses. Unlike more passive AI solutions, an agent perceives its environment, reasons, plans and executes actions, typically interacting with data, tools and other systems without requiring a person to supervise every step.

In practice, an AI agent does not simply “say smart things”, but makes decisions and acts: it queries databases, calls APIs, executes workflows, evaluates results and adjusts its behaviour based on what happens. This is why we talk about action-oriented AI, not just content generation.

How is it different from traditional AI?

Traditional AI usually works in a reactive and limited way: it receives an input, applies a predefined model or set of rules and returns an output. It is highly effective for specific tasks (classification, prediction, recommendation), but it lacks real autonomy and the ability to coordinate complex, multi-step tasks.

An AI agent goes one step further because it:

• Works with goals, not just isolated instructions
• Plans how to achieve those goals by breaking them into steps
• Decides which actions to execute and in what order
• Learns from the outcome of its actions and improves over time

In short, while traditional AI responds, an AI agent acts. This makes it a key component for advanced business process automation and multi-step workflows.

Differences between a chatbot and an AI agent

Although they are often confused, a chatbot and an AI agent are not the same.

A chatbot is mainly designed to hold conversations. It answers questions, guides users and, in some cases, executes simple and very limited actions. Its behaviour is usually reactive: it waits for the user to input something before responding.

An AI agent, by contrast:

• Does not always depend on a conversation to act
• Can operate in the background, without a visible interface
• Executes real actions in systems (CRM, ERP, databases, internal tools)
• Makes autonomous decisions, even initiating tasks on its own

Put simply:

• The chatbot talks
• The AI agent thinks, decides and acts

That is why, while a chatbot can resolve questions or support users, an AI agent can manage entire processes, coordinate systems and automate operations end to end.

How do artificial intelligence agents work?

AI agents operate as goal-oriented cyclical systems. They do not follow a single linear path, but a continuous process in which they perceive, decide, act and learn. This cycle allows them to operate autonomously and adapt to changing contexts, which is essential when working with real data and enterprise systems.

Environment perception and data collection

Every AI agent starts by collecting information from its environment. This information can come from multiple sources: user interactions, internal databases, corporate documents, sensors, system logs, external APIs or real-time events.

The key point is that the agent does not operate blindly. Its ability to correctly perceive context directly determines the quality of its decisions. The better and more up-to-date the data, the more reliable the agent’s behaviour.

Reasoning and decision-making with LLMs

Once data has been collected, the agent uses one or more large language models (LLMs) as its “brain” to interpret information, understand context and reason about what to do next.

At this stage, the agent:

• Analyses the current situation
• Evaluates different possible options
• Selects the most appropriate action according to its objectives and rules

This is where an agent differs from simple automation: it does not execute fixed steps, but dynamically decides based on the situation.

Task and goal planning

After making a decision, the agent moves into planning. This involves breaking down a complex goal into smaller, manageable tasks, defining priorities and establishing a logical execution order.

For example, if the goal is to resolve an incident, the agent may plan to:

• Review the customer’s history
• Consult internal documentation
• Test possible solutions
• Escalate the case if it cannot be resolved

This planning capability allows agents to manage multi-step workflows without constant human intervention.

Autonomous action execution

After planning, the agent executes the required actions. These may include:

• Calling APIs
• Updating records in a CRM or ERP
• Sending emails or notifications
• Generating reports or responses
• Triggering other systems or agents

Execution is not just about “doing”, but about doing so with control, respecting permissions, limits and defined policies. A well-designed agent knows what it can and cannot do.

Learning and continuous improvement

The process does not end with execution. AI agents include feedback mechanisms to evaluate whether the outcome was correct.

Based on this evaluation, the agent:

• Adjusts future decisions
• Improves plans and priorities
• Learns from both mistakes and successes

This learning can be automatic, human-supervised or a combination of both. Thanks to this continuous cycle, AI agents improve with use and become more effective over time, rather than becoming obsolete after initial deployment.

Key components of an AI agent

An AI agent is not a single model or technology, but a system composed of several layers working together to enable autonomy, control and scalability.

Language model and LLM routing

The language model (LLM) acts as the agent’s cognitive core. In real environments, different models are often used depending on the task.

LLM routing allows the agent to:

• Select different models for reasoning, summarisation or extraction
• Combine specialised LLMs to improve accuracy and cost efficiency
• Apply rules governing when and how each model is used

This makes the agent more efficient, flexible and reliable at scale.

Identity, objectives and instructions

Every AI agent requires a clear identity: who it is, what role it plays and why it exists. Along with this, the following are defined:

• Core objectives
• Operational instructions
• Action boundaries

These are strategic rules, not simple prompts. Well-defined objectives lead to coherent behaviour aligned with business goals.

Tools, APIs and integrations

AI agents deliver real value when they can act on real systems. This requires access to:

• Internal and external APIs
• CRMs, ERPs and corporate systems
• Search engines, databases and cloud services
• Code execution and automation tools

These integrations turn the agent into a process orchestrator.

Memory and knowledge management

AI agents can remember and reuse information through:

• Short-term memory for context
• Long-term memory for persistent knowledge

Many agents also use RAG (Retrieval-Augmented Generation) to access up-to-date documentation and data, ensuring consistency and accuracy.

Governance, security and human control

Autonomy without control is risky. AI agents must include:

• Permission and access management
• Action logging and auditing
• Limits on critical decisions
• Human-in-the-loop validation

These mechanisms ensure that the agent operates in a secure, ethical, and compliant manner, especially in business environments where the impact of an error can be significant.

Types of AI agents

Not all AI agents are the same. There are different types of agents, designed to solve problems with increasing levels of complexity, autonomy, and adaptability. Choosing the right type depends on the environment, the level of acceptable risk, and the objectives of the system.

Agent type	Level of autonomy	Learning capability	Typical use cases
Simple reactive agents	Low	No	Basic automations, condition–action rules
Model-based agents	Medium	Limited	State-based environments, simulations, control systems
Goal-oriented agents	Medium–high	None or limited	Task planning, process optimisation
Utility-based agents	High	Limited	Recommendation systems, complex decision-making
Learning agents	High	Yes	Adaptive systems, personalisation
Multi-agent systems	Very high	Yes (individual and collective)	Orchestration, distributed systems

Simple reactive and model-based agents

Simple reactive agents operate using direct rules such as “if X happens, do Y.” They have no memory or historical context, so they do not learn or plan. They are fast, predictable, and useful for very specific tasks, but limited in complex scenarios.

Model-based agents add an extra layer by maintaining an internal model of the environment. This allows them to make better decisions by considering the current state of the system, although they still do not learn in a deep way. They are commonly used in control systems, simulations, or partially observable environments.

Goal-oriented and utility-based agents

Goal-oriented agents do not just react; they act to achieve a specific goal. They evaluate different possible actions and choose the one that best helps them reach the defined objective. They are well suited for task planning and workflow management.

Utility-based agents go a step further: instead of simply achieving a goal, they aim to maximise a value or utility. They compare options based on expected benefits, costs, or risks. This approach is key in recommendation systems, optimisation, and strategic decision-making.

Learning agents and multi-agent systems

Learning agents can improve their behaviour over time through experience and feedback. They use techniques such as machine learning or reinforcement learning to adapt to changing environments. They are especially valuable when it is not possible to define all rules in advance.

Multi-agent systems are made up of several agents that collaborate or compete with each other to solve complex problems. Each agent can have a different role, and the system as a whole achieves objectives that would be unattainable for a single agent. They are used in process orchestration, logistics, advanced simulations, and distributed environments.

How to implement an AI agent step by step

Implementing an AI agent is not just “plugging in an LLM” and calling it a day. For it to work in a real environment (and not just as a polished demo), you need a well-defined use case, reliable data, well-integrated tools, and a system for control, testing, and monitoring. The safest way to start is with a measurable pilot and then scale your business with AI.

Step	What is done	Key deliverable
1	Select a pilot use case and define objectives	Use case + KPIs + success criteria
2	Prepare data and knowledge (RAG)	Validated sources + connected knowledge base
3	Define tools, integrations and permissions	Tool list + permissions + access policies
4	Design prompts, flows and limits	System prompt + flow + escalation / HITL rules
5	Test, evaluate and refine	Test suite + metrics + refined version
6	Deploy and monitor	Logs + dashboards + alerts + cost control
7	Iterate and improve with human control	Improvement roadmap + periodic reviews

Step 1: Choose a pilot use case and define objectives

Start with a use case where the agent can deliver clear and fast value. The key is to define what “working” actually means: objectives, scope, boundaries and KPIs (for example, time saved, resolution rate, ticket reduction, response accuracy or cost per task). Poor pilots usually fail due to overambition: scopes that are too broad, too many dependencies or unclear metrics.

Step 2: Prepare data and knowledge (internal sources, RAG)

An agent is only as good as the information it can access. At this stage, you must decide which sources it will use: internal documentation, FAQs, policies, tickets, databases, ERP/CRM systems, and so on. With RAG, these sources are connected so the agent can respond and decide using up-to-date information. This is also the moment to clean duplicates, define a single source of truth and control access by roles.

Step 3: Define tools, integrations and permissions

The agent needs “hands”: APIs and tools to execute real actions. Define which systems it will interact with (ERP, CRM, ITSM, email, calendar, BI, etc.) and what permissions it will have. Best practices include the principle of least privilege, test environments, service-based credentials and human approval for critical actions. Without proper permission control, risk increases significantly.

Step 4: Design prompts, workflows and action boundaries

This is where the agent’s “behaviour” is built: instructions, tone, rules, output formats and, most importantly, limits. Define when the agent should request more information, when it should escalate to a human and which actions are prohibited. In enterprise agents, it is essential to design workflows (not just prompts) and to establish a HITL (human-in-the-loop) system for sensitive decisions.

Step 5: Test, evaluate and refine (tests and metrics)

Before going into production, you need tests that simulate real scenarios: normal, ambiguous and adversarial cases. Measure both quality and safety: accuracy, coverage, escalation rate, errors, response time and cost per interaction. It is common to iterate on prompts, RAG sources, rules and tools until the agent becomes stable and predictable.

Step 6: Deploy and monitor (logs, quality, costs)

In production, observability is critical. This includes logs of inputs, decisions, tool calls and outcomes. Add dashboards and alerts to detect degradation, hallucinations, integration failures, cost spikes or unexpected behaviour. An agent without monitoring quickly becomes a “black box” and, sooner or later, a problem.

Step 7: Iterate with continuous improvement and human control

An agent is not a “set and forget” system. It must evolve with new data, process changes, user feedback and model improvements. Define a review cycle that includes conversation analysis, knowledge updates, policy adjustments and risk assessment. Always keep a layer of human control for critical actions and a clear continuous improvement plan with well-defined priorities.

Frequently asked questions