What Is an AI Agent: The Autonomous Systems Reshaping Work Right Now

Picture handing an AI system a goal, “research our top competitors, identify market gaps, and prepare a strategic brief”, and receiving a finished report an hour later, without touching a keyboard. The system searched the web, analysed the data, wrote code to process it, drafted the document, and delivered it to you autonomously. This is not science fiction: it is what AI agents do today.

AI agents represent the most significant leap in AI since Large Language Models: they do not just answer questions, they plan, act, and iterate to reach complex multi-step goals. At H-FARM College, students learn to design and build systems based on AI agents, applying them to real-world challenges. One example is Diary Ally, developed as part of a collaboration with Donna Moderna magazine and Fondazione Libellula. In this article you will understand what an AI agent is, how it works technically, where companies are already deploying them, and what you need to learn to work with this technology. And if you already sense this is your world, reach out to the H-FARM College team or join the next Open Day.

The new AI paradigm: from assistants to autonomous agents

Over the past few years, AI has moved from answering questions to acting in the world. This is a fundamental paradigm shift.

Why chatbots are no longer enough

A traditional chatbot follows a predefined flow: it answers questions, follows scripts, handles FAQs. It is useful but limited: it cannot take initiative, cannot use external tools, and cannot complete tasks that require multiple sequential steps. First-generation chatbots are like a customer service representative who can only follow a written script, useful for what is already anticipated, stuck on everything else.

What makes a system a true AI agent

An AI agent is distinguished by four fundamental characteristics:

• Autonomy: operates without continuous human intervention, making decisions about how to proceed.
• Tool use: can call external APIs, execute code, search the web, read and write files.
• Planning: decomposes complex goals into sub-tasks and executes them in sequence.
• Iteration: observes the result of each action, adapts, and corrects its plan based on what it finds.

What is an AI agent: definition and components

An AI agent is a software system that perceives the context of its environment, plans a sequence of actions to reach an objective, and executes them using available tools, iterating until the task is complete.

The agentic cycle: perceive, plan, act, iterate

• Perception: the system receives the goal and gathers contextual information, documents, data, results of prior searches.
• Planning: the LLM breaks the goal into sequential steps and selects the tools to use.
• Action: it executes the planned step, searches the web, writes code, calls an API, updates a document.
• Observation and iteration: it analyses the result and decides whether to continue, correct the plan, or stop.

This cycle repeats until the goal is reached or the system determines it cannot be completed with available tools.

The difference between a chatbot, an assistant, and an autonomous agent

• Chatbot: answers following predefined flows, no real autonomy.
• AI Assistant (like base ChatGPT): answers contextually within a conversation, but does not act in the external world.
• AI Agent: perceives context, plans, uses real tools, and acts autonomously to achieve a multi-step goal.

How an AI agent works: technical architecture

Under the hood, an AI agent combines a language model with tools, memory, and an orchestration mechanism.

The LLM as the reasoning engine

At the core of every AI agent is a Large Language Model, GPT-4, Claude, Gemini, or an open-source model, acting as the “reasoning brain”. The LLM receives context, plans the next steps, selects tools, and interprets results. The quality of the agent’s reasoning depends directly on the quality of the underlying LLM and the system prompts that orchestrate its behaviour.

Tool use: APIs, memory, and external systems

Tools are what separates an agent from a chatbot. An AI agent can have access to:

• Web search: finding real-time information on Google, Bing, or specialised databases.
• Code execution: writing and running Python or JavaScript scripts to process data.
• API calls: interacting with CRMs, e-commerce platforms, or enterprise systems.
• File management: reading, creating, and editing documents, spreadsheets, and emails.
• Memory: accessing a persistent knowledge base to recall information across sessions.

Multi-agent systems: when agents work in teams

The most advanced systems orchestrate multiple specialised agents that collaborate on complex goals. In a multi-agent system, an “orchestrator” agent assigns sub-tasks to specialist agents, one for research, one for data analysis, one for writing, and integrates the results. Frameworks like CrewAI and Microsoft AutoGen simplify building these multi-agent architectures.

Types of AI agents and enterprise use cases

AI agents are rapidly expanding across multiple business functions.

Customer service, coding, data analysis, competitive research

• Customer service: agents that handle complex tickets, including escalations, by analysing customer history, consulting the knowledge base, and writing personalised responses.
• Coding: agents that write, test, and debug code autonomously on specific tasks.
• Data analysis: agents that download datasets, clean them, run statistical analyses, and produce reports automatically.
• Competitive research: agents that continuously monitor competitors, aggregate market signals, and generate weekly briefings.

Case studies: Salesforce Agentforce, GitHub Copilot Workspace, Microsoft 365 Agents

• Salesforce Agentforce: AI agents embedded in the CRM that autonomously manage sales opportunities, update customer records, and suggest next actions to sales representatives.
• GitHub Copilot Workspace: a development environment where an AI agent receives an issue, plans the code changes needed, implements them, and automatically opens a pull request.
• Microsoft 365 Copilot Agents: customisable agents integrated into Word, Excel, Teams, and Outlook that automate productivity workflows, drafting reports, analysing spreadsheets, summarising meetings.

McKinsey estimates that by 2030, AI agents will handle between 30 and 50 percent of cognitive work tasks in the most advanced organisations. Curious how these technologies are taught on our campus? Get in touch with the H-FARM College team or join the next Open Day.

AI agent risks and how to manage them

With growing autonomy come risks that designers and users of these systems must understand.

Hallucinations, uncontrolled access, and governance

• LLM hallucinations: the underlying model may produce false information with confident tone. An agent acting on hallucinated information can cause real harm.
• Uncontrolled access: an agent with overly broad permissions can modify or delete critical data.
• Unexpected behaviours: in complex environments, agents may execute sequences of actions that produce unforeseen side effects.
• Privacy and compliance: an agent accessing sensitive data must comply with applicable data protection regulations.

Human-in-the-loop: when autonomy needs a guardrail

The established best practice is the human-in-the-loop model: for high-impact actions, sending external communications, modifying critical systems, initiating financial transactions, the system requests human approval before proceeding. Autonomy levels should be calibrated to risk: reversible low-risk actions can run autonomously. Irreversible or high-impact actions must pass through human review.

Frameworks and skills for building with AI agents

The market has already produced an ecosystem of tools and frameworks for building agentic systems.

LangChain, AutoGen, CrewAI, OpenAI Agents SDK

• LangChain: the most widely used framework for building LLM-powered applications with tool use and memory.
• Microsoft AutoGen: specialised in multi-agent systems, enabling networks of collaborating agents with defined roles.
• CrewAI: oriented toward creating “crews” of specialised agents with defined roles and workflows.
• OpenAI Agents SDK: OpenAI’s official framework for building GPT-4-based agents with advanced tool use.

What to learn today to stay ahead tomorrow

• Advanced prompt engineering: writing system prompts that reliably guide agent behaviour.
• Python: for building pipelines, integrating APIs, and using agentic frameworks.
• LLM architecture fundamentals: understanding model limitations to design reliable systems.
• Workflow design: for business roles, mapping processes and identifying automatable tasks.

To explore the technology underlying AI agents, read our articles on what a Large Language Model is and on what generative AI is and who works with it.

Build your future in the agentic AI ecosystem at H-FARM College

At H-FARM College, we believe agentic AI is the most important frontier of the next decade, and that those who can build and govern it will have an extraordinary competitive advantage. At the campus in Roncade, you will work with LangChain, the OpenAI and Anthropic APIs, and multi-agent frameworks from the first year, on real challenges brought by partner companies. Three programmes prepare you:

• The Bachelor’s Degree in AI & Data Science builds the technical foundations for AI agents: LLMs, tool use, software architecture.
• The Master’s in AI for Business Transformation prepares you to integrate agentic systems into business processes, from design to governance.
• The Master’s in Entrepreneurship & Startups is for those who want to build products and startups in the agentic ecosystem.

A campus with a startup ecosystem and technology partners where entrepreneurship is in the DNA. Want to see how H-FARM supports the builders of tomorrow? Discover our Entrepreneurship & Startup Center or contact the H-FARM team.

FAQ

How is an AI agent different from a chatbot?

A chatbot answers questions following a predefined and often rigid flow. An AI agent perceives context, plans a sequence of actions, uses external tools, web search, APIs, code execution, and acts autonomously to reach a goal. The key difference is autonomy: an agent can complete complex multi-step tasks without continuous human input.

How does an AI agent work technically?

An AI agent is built around an LLM like GPT-4 or Claude that acts as the reasoning engine. Around the LLM are tools: web search, code execution, file writing, API calls. The cycle is context perception, planning, action, observation of the result, and iteration. Frameworks like LangChain, AutoGen, and CrewAI make it easier to build these systems.

Which companies are already using AI agents?

Many are. Salesforce uses AI agents to automate CRM workflows. GitHub Copilot Workspace is an agent for software development. The most common enterprise use cases are customer support, email management, competitive research, data analysis, and report generation. McKinsey estimates that by 2030, AI agents will handle between 30 and 50 percent of cognitive work tasks.

Are AI agents safe?

It depends on how they are designed. The main risks are hallucinations from the underlying LLM, uncontrolled access to critical systems, unexpected behaviour in complex environments, and data privacy concerns. Best practices include human-in-the-loop oversight for critical decisions, sandbox testing environments, and full action logging.

What skills do you need to work with AI agents?

Advanced prompt engineering, LLM fundamentals, Python for pipeline development, and familiarity with agentic frameworks like LangChain, AutoGen, and CrewAI. For business roles, the ability to design automatable workflows and evaluate output reliability is essential. H-FARM College programmes in AI for Business Transformation and AI and Data Science prepare you to build and manage agentic systems in real company environments.