What is an AI agent?
An AI agent is a software system that uses an AI model to pursue a goal on its own, planning its steps and acting inside other software to complete them.
A regular AI assistant answers the question it is asked and then stops. An agent is given an outcome, works out what has to happen to reach it, and keeps going until the job is done or a person needs to weigh in.
The term has spread because large language models made agents practical. Earlier agents ran on hand-written rules and broke as soon as the input changed shape. A model-driven AI agent can read an email or a contract, decide what it means, and choose the next action without a script for every case.
How does an AI agent work?
Every AI agent architecture has its own details, but the cycle underneath looks roughly like this:
Goal. A person or another system hands the agent a task with a clear end state, for instance “review this contract against our playbook and flag anything off-standard.”
Perception. The agent gathers what it needs: the document itself, records from a CRM or database, results of a web search, or prior context from its memory.
Reasoning and planning. The model breaks the goal into steps and decides which to take first. It may revise the plan after each step.
Action. The agent calls a tool: it runs a query, fills a form, drafts a message, or updates a record in an external system. Tool access is what separates an agent from a chat window.
Evaluation. The agent checks the result against the goal and either continues, retries, or stops and asks a human to approve before anything irreversible happens.
Types of AI agents
Agents are usually grouped by how much they reason and how they are organized:
- Simple reflex agents: React to the current input with fixed rules. A thermostat is the textbook example.
- Goal-based and utility-based agents: Compare possible actions against an objective and pick the one that brings them closer to it or the one with the best expected payoff.
- Learning agents: Improve their behavior from feedback over time.
- LLM-based agents: Use a large language model as the reasoning core, which lets them handle unstructured input and natural-language goals. Most agents built for business today fall here.
- Multi-agent systems: Several specialized agents that hand work to each other, often with an orchestrator agent coordinating the flow.
AI agents vs. chatbots
A chatbot holds a conversation. It retrieves information and answers questions, and the interaction ends there. Anything that has to happen next is the person’s job.
An AI agent acts. Given the same customer request, a chatbot explains the refund policy, while an agent checks the order, applies the policy, issues the refund, and logs the outcome. Chatbots are often the front end of an agent, but the ability to take action on other systems is what makes something an agent.
Where can you use AI agents?
AI agent use cases in business cluster around work that repeats often and needs judgment on each pass. Here are a few examples:
- Contract review: An agent takes a batch of agreements, finds the clauses that carry risk, compares each with the firm’s standard positions, and hands a lawyer a draft with the deviations already marked.
- Customer service: Agents resolve routine tickets end to end, from looking up the account to making the change, and escalate the rest with the context already gathered.
- Research and monitoring: An agent keeps watch on competitor announcements or regulator publications and writes them up as a briefing that updates itself on a set cadence.
- Compliance: When a rule changes, an agent works out which internal policies it touches and prepares the revised wording for the compliance team to sign off.
- Finance and underwriting: An agent pulls together an application, the external reports, and the loss history, quotes the ordinary cases from the rulebook, and sets the odd ones aside for an underwriter with everything in one place.
- Due diligence: Instead of an analyst opening several hundred documents, an agent goes through the data room, lifts out the terms that move the valuation, and produces a memo draft with the concerns highlighted.
- Sales operations: An agent fills in missing lead details, prepares follow-up messages, and keeps the CRM tidy, leaving the rep with conversations instead of admin.
- Procurement and sourcing: An agent shortlists suppliers or venues against a brief, emails each one about availability and price, nudges the ones that go quiet, and lays the offers side by side for a person to choose from.