What Is an AI Agent?

An AI agent is a software system that can use AI to interpret a goal, determine what needs to happen, use available tools or systems, and take actions towards completing a task.

Unlike a simple question-and-answer interface, an agent can participate in a wider workflow. Depending on its design, it may decide which information it needs, call tools, perform actions, evaluate results and continue until the task is completed or human intervention is required.

The important point is that an AI agent is not simply a more advanced chatbot. It is an approach to executing work, and it should only be used where the additional autonomy creates meaningful value.

The term "AI agent" is used in different ways, but a useful business definition is a system that can interpret an objective and take actions within a defined environment to achieve that objective.

An agent typically combines an AI model with instructions, context, access to tools or systems, and rules that determine what it is allowed to do.

The level of autonomy can vary considerably. Some agents may perform a narrow sequence of actions under tightly controlled conditions, while others may handle more open-ended tasks with greater independence.

An AI model can generate an answer, classification, summary or other output in response to an input.

An agent adds a surrounding system that allows the model to participate in a process and potentially take actions.

• The model provides intelligence such as language understanding or generation.
• Instructions define the agent's role and boundaries.
• Context provides information relevant to the task.
• Tools allow the agent to interact with external systems.
• Workflow logic controls what can happen and when.
• Guardrails restrict inappropriate or risky actions.
• Human oversight provides intervention where required.

A simplified agent workflow might look like this:

• Receive a goal or request
• Understand the objective and available context
• Determine what information or actions are required
• Use an appropriate tool or system
• Review the result
• Decide whether another action is necessary
• Complete the task or escalate to a person

Not every agent needs to perform every step explicitly. The architecture depends on the task, the technology and the level of autonomy required.

An AI system becomes more useful for many business tasks when it can interact with tools and systems rather than simply generate text.

Depending on the permissions and architecture, tools could allow an agent to retrieve information, search a knowledge source, create a record, update a system, send information to another workflow or perform another controlled action.

Tool access also increases risk. The more consequential the action, the more important it becomes to define permissions, validation and human oversight.

Many business processes involve several systems rather than one application.

An agent can potentially act as an orchestration layer between those systems when the task requires interpretation or decision-making alongside system actions.

For example, an agent might interpret an incoming request, retrieve relevant customer information, determine which workflow applies and initiate the appropriate next step.

The exact architecture depends on the business process and the systems involved. An agent should not be given unrestricted access to business systems simply because integration is technically possible.

A chatbot is primarily an interface through which a person interacts with a conversational system.

An AI agent is more concerned with accomplishing a task or objective and may operate across tools and systems to do so.

The two can overlap. A chatbot may provide the interface through which a customer asks an agent to perform an action.

However, not every chatbot is an agent, and an agent does not necessarily need to be exposed through a chatbot.

Traditional automation is generally strongest when the process can be described using predictable rules and defined conditions.

Agents become more interesting when the workflow contains interpretation, unstructured information, changing circumstances or decisions that cannot easily be represented through fixed rules.

This does not mean that agents should replace traditional automation. A useful business workflow may combine conventional rules, deterministic automation, AI capabilities and human oversight.

The simplest reliable approach should normally be preferred.

AI agents can potentially create value where employees spend time coordinating information, interpreting requests, navigating systems or carrying out multi-step tasks.

• Handling and routing business enquiries
• Research and information gathering
• Preparing information for employees
• Supporting operational workflows
• Coordinating tasks across systems
• Processing and interpreting documents
• Supporting customer or employee self-service
• Monitoring defined workflows and initiating appropriate actions

These are potential applications rather than a recommendation that every business should introduce agents into these areas.

The case for an agent is strongest when the business problem requires more than a single AI response or a simple deterministic workflow.

• The task involves multiple steps.
• The information required may vary from case to case.
• The system needs to interpret unstructured information.
• Different tools or systems need to be used.
• The workflow contains decisions that benefit from AI reasoning.
• There is enough business value to justify additional complexity.
• The actions can be appropriately constrained and monitored.

Greater autonomy is not automatically better.

If a task can be completed reliably using a conventional workflow, rule-based automation or a simple AI interaction, introducing an agent may create unnecessary complexity.

Agents can introduce additional failure modes, operational dependencies, monitoring requirements and security considerations.

The appropriate level of human involvement depends on the consequences of the agent's actions.

An agent that prepares information for an employee may require much less control than an agent that can make changes to customer records, approve transactions or trigger consequential business actions.

• Define what the agent is allowed to do.
• Limit access to only the systems and information it needs.
• Require approval for higher-risk actions where appropriate.
• Provide clear escalation paths.
• Log important actions and outcomes.
• Monitor performance and failure patterns.
• Review the system as the underlying business process changes.

AI agents can fail in ways that differ from conventional software.

An agent may misunderstand an instruction, select an inappropriate tool, use incomplete information, produce an incorrect interpretation or take an unexpected sequence of actions.

This means testing should consider not only whether the intended workflow works, but also what happens when information is missing, instructions are ambiguous, systems fail or the AI produces an unexpected result.

The consequences of failure should influence how much autonomy the system is given.

Giving an AI system the ability to take actions changes the security model of the application.

Tool permissions should therefore be designed deliberately rather than treating the agent as a user with unrestricted access.

Access to customer data, internal information and business systems should be limited according to the actual requirements of the task.

Security considerations should include both the AI model and the surrounding tools, integrations and workflows.

An agent needs appropriate information to make useful decisions.

That information may come from business systems, structured data, documents, knowledge bases, APIs or other approved sources.

The business should understand which sources the agent can access, how current they are and what happens when the required information is unavailable or contradictory.

An AI agent should not be considered in isolation from the wider business problem.

A sensible AI strategy considers the problem, process, potential value, feasibility, readiness, risk and complexity before deciding whether an agent is appropriate.

The eventual solution could be an AI agent, an AI assistant, traditional automation, an AI-powered website, a process improvement or no technology change at all.

The technology should follow the business requirement.

Before building an agent, consider the process systematically.

• What business problem are we trying to solve?
• What outcome would improve if the problem were solved?
• What does the current process look like?
• Which steps require human interpretation or judgement?
• Which systems and information are involved?
• Which actions would the AI need to take?
• What happens when the AI is uncertain?
• What level of human approval is required?
• Can a simpler solution achieve the same outcome?
• Is the expected value sufficient to justify the additional complexity?

These questions help determine whether the opportunity is genuinely agentic or whether another solution would be more appropriate.

DEEPS approaches AI agents from the business problem backwards.

The first question is not "Where can we deploy an agent?" It is "What are we trying to improve?"

From there, the process, friction, frequency, manual effort, data, systems, exceptions and AI suitability can be examined before deciding whether an agent is an appropriate solution direction.

This helps prevent businesses from introducing autonomous systems where a simpler and more controllable solution would achieve the same result.

If you want to identify potential AI opportunities in your business, the DEEPS AI Opportunity Finder provides an initial way to explore potential opportunities. It is a starting point for further discovery rather than a final recommendation.

AI agent systems combine AI models with tools, workflows and permissions. Their design therefore benefits from considering broader AI risk and application security principles.

• NIST AI Risk Management Framework
• OWASP Top 10 for Large Language Model Applications
• Anthropic — Building Effective Agents

These sources provide broader technical and risk context. The appropriate level of autonomy still depends on the specific business process, systems, information and consequences of failure.