An AI agent is a software system that uses an AI model to decide how to progress towards a goal. It can work with information and connected tools, observe the results of its actions and choose what to do next within the boundaries set for it.
For a business, the useful distinction is that the system can participate in a process. It might retrieve records, assess missing information, prepare an update and ask a person to approve the next step. What it can actually do depends on its tools, permissions, instructions and implementation.
How does an AI agent work?
A common pattern begins with a goal and a set of instructions. The model considers the available context and chooses a next step, such as calling a tool or producing an answer. The system receives the result and may continue until it completes the task, reaches a limit or needs help.
The surrounding software controls how those steps run. It can restrict tools, validate inputs, record actions and pause for approval. The model is one part of the system; reliable operation depends on the design around it.
Definitions vary across the industry. A useful distinction is between a fixed workflow, where the path is largely specified in advance, and an agent, where the model has more discretion over the sequence of actions. Many business implementations combine both approaches.
AI agent versus chatbot
A chatbot describes a conversational interface. An agent describes capabilities and behaviour behind an interface. A chatbot may answer questions without taking actions. It may also provide the front end for an agent that uses connected tools.
For example, an assistant that explains a delivery policy is providing information. An assistant that checks an order, identifies an exception and prepares a support task is participating in a wider process. The visible chat window can look similar while the underlying responsibilities differ.
Ask what the system can access, what decisions it makes and what changes it is authorised to carry out. Those questions reveal more than the label used in a product demonstration.
AI agents versus traditional automation
Traditional automation follows defined rules and branches. If an order meets a known condition, the system performs a specified action. This approach can be effective when the inputs and decisions are predictable.
An agent can be useful where inputs are less structured or the next step depends on interpretation. It might read a free-text enquiry, recognise that a key detail is missing and choose to retrieve relevant information before preparing a response.
More discretion also creates more to evaluate. If a straightforward rule can solve the task reliably, introducing an agent may add cost and uncertainty without enough benefit. Start with the process and choose the simplest approach that serves it.
What components does an AI agent need?
The goal should describe the outcome and the scope. Instructions should explain how to handle the task, including what to do when information is incomplete. Context provides the records or reference material needed for the work.
Tools connect the system with useful capabilities, such as searching documents or reading a customer record. Permissions determine which records and actions are available. Evaluation checks whether the system performs acceptably on realistic tasks.
Some agents also use persistent state or memory. That can help continue a process, but it needs a deliberate design for what is retained, who can access it and how outdated information is handled. More stored context does not automatically produce a better decision.
Where can businesses use AI agents?
Potential uses include preparing customer support responses, organising inbound enquiries, assembling research, checking records for missing information and supporting internal knowledge searches. These are possibilities rather than guarantees of value.
A useful first case has a recognisable outcome, accessible information and a manageable cost of error. It should also have a person or team responsible for reviewing performance and handling exceptions.
Avoid beginning with an instruction such as “run marketing”. Break the work into observable tasks. Preparing a weekly campaign review from approved reports is easier to evaluate than giving a system a broad responsibility with no clear stopping point.
How much autonomy should an AI agent have?
Autonomy should follow the task and its consequences. Retrieving a public document is different from changing a customer record, committing a price or sending an external message. Those actions can have separate permission and approval rules.
A practical design can allow the agent to gather information and prepare work independently, then pause before a consequential action. This keeps the review focused on a concrete result rather than a vague intention.
Approval alone is not sufficient if reviewers cannot understand what will happen. Show the proposed action, the relevant evidence and any unresolved uncertainty in a form the responsible person can assess.
What can go wrong?
An agent may misunderstand a request, use incomplete information or select an unsuitable tool. It may repeat a step unnecessarily or fail to recognise that a process has already completed. Errors can also originate in the connected systems rather than the model.
Information retrieved from documents or websites may contain misleading statements or instructions. A well-designed system distinguishes reference content from authorised instructions and limits what retrieved material can cause it to do.
Controls should match the workflow: restricted access, validated inputs, clear action boundaries, execution limits and useful logs. The objective is to make failures detectable and recoverable within the operating model.
How do you evaluate an AI agent?
Use realistic examples, including ordinary cases, ambiguous requests, missing records and exceptions. Define what successful completion looks like before assessing the result. Measure task quality, escalation behaviour, time, cost and the frequency of human correction.
Begin with a limited pilot. Compare the complete workflow with the current process, including review and rework. A fast draft may save little time if a person must verify every claim from the beginning.
Keep evaluating after launch because tools, instructions and business information change. For the conceptual distinction between workflows and agents, see Anthropic's guide to effective agents. To explore suitable business use cases, see AI consulting, strategy and workflows at Linford.
A practical example
Illustrative example: an enquiry agent reads a new request, checks approved service information and prepares a summary for the sales team. It identifies missing details and drafts follow-up questions. A team member reviews the proposed response before it is sent, while the system records the evidence used.
Why it matters
AI agents can connect interpretation with action in processes that do not fit a simple rule. Their practical value depends on choosing a suitable task, setting clear boundaries and measuring the whole workflow, including human review.
