AI agent
An AI agent is an AI system pursuing goals by itself: it perceives its environment, plans steps of action, uses external tools and APIs and learns from the results. An AI agent is mostly based on a large language model (LLM) taking control. Unlike a plain chatbot, an AI agent acts autonomously across several steps rather than only answering individual inputs.
Also known as: AI agent, autonomous AI agent, intelligent agent, AI assistant
What is an AI agent?
An AI agent is software that pursues a goal independently and across several steps. Unlike a classic program following rigidly prescribed logic, an AI agent decides dynamically which steps are needed to reach the goal. At its core is generally a large language model (LLM) that acts as the planning and steering instance.
The decisive difference from a simple chatbot is the ability to act. While a chatbot merely generates answers, an AI agent can operate tools itself, fetch data, start calculations or carry out tasks in other systems. It therefore acts, not just speaks.
How does an AI agent work?
How an AI agent works can be described as a cyclical process, often called the agent loop. The agent first perceives a task or a state, then plans how to proceed, carries out an action and assesses the result. On the basis of that assessment it plans the next step, until the goal is reached.
Tool use is a central building block. Through defined interfaces the agent can run searches, query databases, send emails or execute code. In addition, many agents use a memory (Memory), to keep intermediate results and context available across several steps.
AI agent vs. chatbot: what is the difference?
Chatbots react to individual input and finish their work with each answer. An AI agent, by contrast, pursues an overarching goal proactively and breaks it into sub-tasks itself. That autonomy across several steps is the essential distinguishing feature.
Another difference concerns the connection to the real working environment. A chatbot generally stays confined to dialogue, while an AI agent acts in systems through tools and triggers processes. The value thus shifts from mere information to actually getting work done.
Where are AI agents used?
AI agents are used wherever recurring, multi-step tasks are to be automated. Typical examples are researching and preparing information, handling customer enquiries, maintaining records or coordinating workflows across several applications.
In a business context, AI agents can be connected to internal knowledge sources, for instance via RAGmethods (retrieval-augmented generation). That lets them give well-founded, context-aware answers and at the same time carry out documented processes on their own. Often several specialised agents work in a Multi-agent system together.
What matters during development?
For an AI agent to work reliably, clear goal definitions, carefully chosen tools and well-considered guardrails are decisive. Without sensible limits there is a risk of an agent choosing inefficient routes or carrying out actions that were not intended. Safety, traceability and control are therefore central requirements.
In practice an iterative approach is advisable: the agent is first developed for a clearly defined use case, tested and extended step by step. A clean connection to existing systems and well-considered context and prompt engineering significantly determine the quality of the results. Elisabit develops such bespoke AI agents for concrete business requirements.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot reacts to individual input and gives answers. An AI agent pursues a goal autonomously across several steps, plans its approach and uses tools to act in systems. The AI agent acts, while the chatbot mostly answers.
Does an AI agent always need a large language model?
In today's practice, most AI agents are based on an LLM handling planning and control. Other control logic is conceivable in principle, but LLMs have become the standard thanks to their flexibility and language understanding.
What does agent loop mean?
The agent loop describes an AI agent's cyclical process: it perceives a situation, plans a step, carries out an action and assesses the result. This cycle repeats until the set goal is reached.
Are AI agents safe for enterprise use?
AI agents can be used safely when clear guardrails, permissions and controls are defined. Traceability of actions, limited tool rights and iterative testing matter before an agent takes on critical tasks in production.
Related terms
An AI paradigm that acts autonomously, with a goal in mind and across several steps, instead of merely answering single prompts.
A platform for building, configuring and deploying AI agents, often no-code or low-code.
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
RAG combines a language model with the retrieval of relevant information from external knowledge sources before answering.
AI automation uses LLMs and agents to automate even unstructured business processes end to end.
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