AI Agent: definition, how it works, and enterprise use cases

After chatbots, AI agents have become the central topic of artificial intelligence in the enterprise. The promise: an AI that doesn't just answer questions, but takes action — consulting a CRM, preparing a quote, updating a ticket, sending an email. Behind the term, often used loosely, lies a precise reality. Here's what an AI agent really is, what sets it apart from a chatbot, and how to use it reliably.

What is an AI agent?

An AI agent is a program that uses a language model (LLM) to achieve an objective in multiple steps, choosing by itself which actions to take and which tools to use. Where a standard assistant produces an answer, an agent produces a result. Concretely, an AI agent combines four elements:

• A language model, which serves as a "brain" to understand the request, reason, and plan;
• Tools: APIs, business software, databases, browser, email… that the agent can call;
• Memory: the context of the current task, and sometimes a history of past exchanges;
• An execution loop: the agent acts, observes the result, adjusts its plan and starts again until it reaches the objective or asks for human help.

From assistants to agents: the arrival of agentic AI

The first wave of generative artificial intelligence popularized conversational assistants: you ask a question, the model answers. The second wave, often called agentic AI, gives these models the ability to act. Three advances made it possible: much better language models for reasoning and planning, the ability to call functions and APIs reliably (function calling), and standards that facilitate connection to everyday software. Result: an intelligent agent can now chain dozens of steps, consult multiple sources of information, execute actions in different systems and make simple decisions, where previously a human was needed to link each tool.

Key capabilities of an AI agent

• Perceive: read a request in natural language, an email, a document, screen content, or data from an application.
• Reason and plan: break down a complex objective into steps, choose the order of actions and appropriate tools.
• Act: execute concrete actions through available tools (create a record, send a message, run a search, generate a document).
• Remember: retain the context of the task, and rely on a knowledge base or interaction history.
• Self-correct: analyze the results of its actions, detect an error and modify its plan.

AI Agent vs chatbot vs RAG: what are the differences?

These three concepts are often confused. Yet they meet different needs:

	Chatbot	RAG	AI Agent
Role	Converse, answer	Answer based on your documents	Accomplish a task
Data access	Limited or scripted	Search in a document base	Read and write in your tools
Autonomy	Low	Low	High, governed
Example	"What are your hours?"	"What does our product return procedure say?"	"Process this return request and prepare the credit note"

These building blocks combine: an AI agent often uses a RAG as one of its tools to find the information it needs before acting.

How does an AI agent work?

Let's take an example: "Follow up with customers whose invoice has been unpaid for more than 30 days. "

1. Understanding and planning: the agent breaks down the request (identify invoices, find contacts, draft follow-ups, send them).
2. Tool calling: it queries the billing software via its API, then the CRM to retrieve contacts.
3. Observation: it verifies results (has an invoice been paid in the meantime? is a contact missing?).
4. Action: it drafts personalized follow-ups and, depending on defined rules, sends them or submits them for validation.
5. Report: it summarizes what was done and flags cases requiring human intervention.

Connection to tools happens through standard APIs or recent standards like MCP (Model Context Protocol), which simplify connecting an agent to existing software.

Different types of AI agents

• Workflow agents: they execute a framed process with fixed steps, with AI helping to read, classify or write. These are the most reliable and simplest to deploy.
• Autonomous agents: they choose the sequence of actions themselves. More flexible, they require stricter oversight.
• Multi-agent systems: multiple specialized agents (research, writing, verification) collaborate on the same task.

In most enterprise projects, you start with workflow agents, which deliver quick and measurable gains, before gradually opening up more autonomy.

AI Agents, classical automation and RPA: what changes

Enterprises haven't waited for AI to automate. Workflow automation tools (Zapier, Make, n8n) and RPA (Robotic Process Automation) have long allowed chaining actions between applications. Their limitation: they follow fixed rules. As soon as data is poorly formatted, an email is worded differently, or a case wasn't anticipated, the flow stops. AI agents bring what was missing: the ability to understand unstructured content (emails, documents, conversations) and adapt to various situations. In practice, the two approaches complement each other: classical automation for predictable steps, AI agent for steps requiring understanding or decision-making.

	Classical automation / RPA	AI Agent
Operation	Fixed rules and scenarios	Objective, reasoning and action choice
Data processed	Structured	Structured and unstructured (texts, documents)
Facing the unexpected	Stops or produces an error	Adapts or requests validation
Predictability	Very high	Needs to be governed by safeguards

Examples of AI agents in the enterprise

• Customer service: qualification of incoming requests, response to simple cases, file preparation for human agents on complex cases.
• Sales: enrichment of prospect records, meeting preparation, proposal writing from templates and CRM.
• Administration and finance: invoice data extraction, reconciliation, follow-ups, preparation of entries to be validated.
• HR: pre-screening of applications according to defined criteria, interview scheduling, onboarding.
• Operations: order tracking, alerts on anomalies, updates to monitoring tools.
• Pricing: competitor monitoring and price adjustment proposals, to be validated by a human.
• Software development: code agents write, test and fix code under developer supervision, accelerating delivery of new features.
• Monitoring and research: an agent conducts research on the web and in your document bases, cross-references information and produces a sourced summary.

Detailed example: an incoming request processing agent

A company receives hundreds of emails daily to a generic contact address: quote requests, questions about orders, complaints, applications. An AI agent can:

1. read each message and identify its nature;
2. find the customer in the CRM and order history;
3. directly answer simple questions (delivery tracking, documents to provide) from the knowledge base;
4. prepare a draft response and summary for complex requests, passed to the right colleague;
5. automatically create an opportunity in the CRM for quote requests.

Teams only handle requests that truly require their expertise, with all information already gathered.

Autonomy and decision-making: finding the right level

Not all AI agents need the same degree of autonomy. Several levels can be distinguished, to be chosen based on decision impact and company maturity:

1. The agent suggests: it analyzes the situation and proposes an action, which the human executes.
2. The agent prepares: it does the work (draft response, CRM record, quote) and the human validates before sending.
3. The agent acts and reports: it executes routine actions alone and reports back, with the human retaining control over exceptions.
4. The agent acts autonomously within a strictly defined scope, with automatic controls and alerts.

Most successful deployments start at level 2, then progress as trust builds through measured results.

Designing an AI agent: the technical steps

Behind an agent that seems simple to use, design mobilizes several skills:

• Define the mission: objective, scope, business rules, situations where the agent must hand over.

• Connect the tools: development or configuration of connectors to CRM, ERP, email, databases, with rights limited to the bare minimum.
• Structure knowledge: queryable knowledge base (often via RAG), reference documents, examples of good responses.
• Write instructions: the system prompt describing the agent's role, tone, limits and how to handle each request type.
• Test and evaluate: test scenarios, including edge cases, with reproducible quality measures.
• Supervise: action dashboard, alerts on anomalies, continuous improvement from user feedback.

Key considerations before deploying an AI agent

• Keep a human in the loop for sensitive actions (sending to a client, payment, data deletion)
• Limit rights: an agent should only access tools and data strictly necessary for its mission
• Trace every action: detailed logs to understand what the agent did and why.
• Test on real cases before production, with a set of reference scenarios, including edge cases.
• Control consumption: an agent makes numerous calls to the model; architecture and LLM choice directly impact costs.
• Protect against manipulation: an agent reading external content (emails, web pages) can receive malicious hidden instructions (prompt injection). Sensitive actions must therefore remain subject to controls independent of the model.
• Comply with GDPR and the AI Act, particularly when the agent processes personal data or participates in decisions concerning individuals.

Off-the-shelf AI agent or custom agent?

Many tools today offer ready-to-use agents or no-code platforms (Zapier, Make, n8n…). They work well for simple, standardized automation. A custom AI agent becomes relevant when:

• the process is specific to your business;
• the agent must connect to internal software or sensitive data;
• volumes make usage-based subscriptions expensive;
• reliability and traceability are critical.

How to launch an AI agent project?

1. Choose a repetitive process, with volume, clear rules, where the gain is measurable.
2. Map the necessary tools and data, and associated rights.
3. Deliver a first agent on a reduced scope, with systematic human validation.
4. Measure time saved, error rate, team satisfaction.
5. Gradually expand autonomy and scope, sprint after sprint.

How to measure the value of an AI agent?

To decide on scaling an agent, clear indicators are needed, defined before deployment:

• Time saved by teams on the process in question;
• Autonomous handling rate: the share of requests handled end-to-end without human intervention;
• Error rate compared to manual processing;
• Processing time: an agent works continuously, which often greatly reduces response time to clients;
• Satisfaction of internal users and customers.

Which models and technologies for an AI agent?

An agent relies on one or more language models: proprietary models (GPT, Claude, Gemini) or open source models (Mistral, Llama) that can be hosted in Europe or on your own servers. The choice depends on task complexity, data sensitivity and request volume. Around the model are orchestration frameworks, vector databases for memory and search, and connectors to your applications. It's common to combine multiple models: a powerful model for planning and complex decisions, a lighter and faster model for simple tasks like classification.

What's the future for AI agents?

AI agents are progressing rapidly. Language models become more reliable in tool use and long-task planning, connection standards to applications are generalizing, and software publishers are integrating their own agents into their solutions. We're also seeing agents emerge that can use an interface directly, as a human would, for software without APIs. For enterprises, the issue is no longer whether AI agents will be useful, but where to start, preparing your data and tools, and establishing best practices for supervision and security now. Organizations experimenting today on well-chosen processes will gain a lead that will be difficult to catch up with.

Want to deploy an AI agent?

Junr designs custom agents and AI solutions and business process automation, connected to your existing tools.

Our agile method lets you test and validate developments from the first sprints. 

Contact us.