What Are AI Agents? How They Work and Where They Add Value

AI agents are software systems that use context, reasoning and tools to work toward a defined goal. Unlike a chatbot that only answers a prompt, an AI agent can decide what to do next, complete several steps and interact with business systems within set permissions. The useful question is not how autonomous an agent can be, but how much control the workflow requires.

For businesses, AI agents are most relevant where people repeatedly gather information, interpret documents, check rules, handle exceptions and update systems. This guide explains how AI agents work, how they differ from assistants and automation, where they create value and what must be in place before they can be trusted with operational work.

What is an AI agent?

An AI agent observes information, evaluates it against a goal and defined constraints, and then suggests or performs the next action. Its inputs can include user requests, emails, PDFs, process events, APIs and operational data. Its actions can range from preparing a recommendation to updating an approved field in a connected system.

Consider an incoming customer-order document. An agent could extract the information, compare it with customer and item data, identify missing or conflicting values, prepare a proposed order and route exceptions to the right person. The agent is useful because it can coordinate the full task. It does not remove the need for permissions, business rules or human review.

AI agents vs assistants, automation and agentic AI

These terms describe related but different ways of using technology.

Approach	What starts the work	How it operates	Best fit	Typical control
Rule-based automation	A fixed trigger	Follows predefined rules and paths	Stable, repetitive processes	Rules determine every allowed step
AI assistant	A user prompt	Helps a person search, explain, draft or decide	Individual productivity and decision support	The user initiates and completes the task
AI agent	A goal, event or request	Interprets context, chooses a next step and uses tools	Multi-step work with variation or exceptions	Permissions, rules, monitoring and review points
Agentic AI	A broader system or operating model	Coordinates one or more agents across workflows and systems	Connected operational processes at greater scale	Governance across agents, data, tools and outcomes

Use conventional automation when the path is predictable. Use an assistant when a person should remain the active driver. Use an AI agent when the task needs contextual decisions across several steps. Use agentic AI when several governed agents or workflows need to work together as part of a broader operational model.

Elvenite's Agentic AI offering explains the commercial and delivery perspective in more detail.

How do AI agents work?

Most useful business agents follow a controlled cycle.

1. Receive a goal or event

The process starts with a user request, an incoming document, a system event or a defined business condition. The goal and boundaries should be explicit.

2. Gather relevant context

The agent retrieves the information it is allowed to use. That may include policies, process documentation, master data, transaction history, emails or records from connected systems.

3. Evaluate options and constraints

The agent considers what the information means, checks business rules and determines the next permitted step. This is where context makes an agent different from a static sequence.

4. Use tools or systems

Depending on its permissions, the agent may search a knowledge base, call an API, prepare a document, create a recommendation or update a selected field.

5. Escalate or act

Low-risk actions may be completed automatically. Uncertain, sensitive or high-impact decisions should be routed to a person with the context needed to review them.

6. Log and monitor the result

Inputs, decisions, actions and exceptions should be traceable. Monitoring shows whether the workflow produces the expected result and where its rules, data or permissions need adjustment.

Three practical levels of autonomy

Not every AI agent should have the same freedom to act. A useful design starts by choosing the lowest level of autonomy that can deliver the intended value.

Decision support

The agent gathers, structures and explains information. A person makes the decision and performs the final action.

Human-in-the-loop execution

The agent prepares the task and recommends or stages an action. A person approves it before a system is updated.

Selected autonomous execution

The agent performs a clearly defined, low-risk action within approved permissions. Exceptions, confidence thresholds and monitoring determine when the work returns to a person.

This approach makes governance part of the workflow design instead of an extra control added after the agent has been built.

When should a business use an AI agent?

An AI agent is a strong candidate when several of these conditions are present:

• people repeatedly collect information from several sources;
• inputs include emails, documents or other unstructured content;
• the next step depends on context rather than one fixed rule;
• exceptions create delays or manual coordination;
• the process has a clear owner and a measurable outcome;
• the required data and system access can be governed;
• human review points can be defined before implementation.

When not to use an AI agent

Do not add an agent simply because the technology is available. A standard workflow or rule-based automation is usually better when the process is stable, the inputs are structured, the rules cover nearly every case and the cost of maintaining an agent would exceed the value.

An agent is also a poor starting point when no one owns the process, source data cannot be trusted, permissions are unclear or success cannot be measured. In those cases, fixing the workflow and data foundation comes first.

Examples of AI agents in business

Document and approval workflows

An agent can read an invoice, application or customer document, extract relevant information, compare it with records and policies, and prepare a recommendation. Straightforward cases can follow an approved path while exceptions go to a specialist.

Customer and order operations

An agent can gather order details from emails and attachments, validate them against customer, item and pricing data, and prepare a transaction. In an ERP context, the highest-value role is often reducing manual handling while keeping approval and write-back controlled. See Elvenite's concrete examples of AI agents in Infor M3 and the indexed AI agents for Infor M3 offering.

Supply chain and production exceptions

When a delivery, stock level or production condition changes, an agent can identify affected orders, collect relevant context and propose the next action. The goal is not to let a model improvise freely. It is to speed up a known operational response while preserving business rules and escalation paths.

Master-data quality

An agent can detect missing, inconsistent or duplicated records, gather supporting information and prepare proposed corrections. A human can review sensitive changes, while selected low-risk corrections can follow a controlled path.

Internal support and knowledge work

An assistant may answer a question from approved documentation. An agent goes further by gathering account or case context, checking the relevant procedure, preparing the next step and routing the task to the correct team.

For more workflow examples, read where AI agents create value across industrial value chains.

What an AI agent needs to work reliably

A defined workflow and outcome

The agent needs a specific job, a clear owner and a result that can be measured. “Use AI in operations” is not a workable use case. “Reduce manual handling of customer-order documents while keeping exceptions under review” is.

Trusted context and data

Agents can work quickly with poor information and still produce poor results. Reliable master data, clear definitions, accessible documentation and governed data products are part of the solution. Elvenite's perspective on data ownership as an AI bottleneck and its Data Intelligence offering explain this foundation.

Tools, integrations and permissions

An agent needs controlled access to the systems required for its task. Permissions should be limited by role, action and context. Read-only access, proposed updates and approved write-back are different risk levels and should be designed separately.

Business rules and review points

The workflow should define what the agent may decide, what must be checked and when a person takes over. Confidence thresholds alone are not a substitute for process ownership.

Logging and monitoring

Teams need to see what information the agent used, which action it took, whether a human intervened and what outcome followed. This supports operational improvement, compliance and accountability.

How to start with AI agents

Start with one workflow where friction is visible and value is measurable.

1. Map the current task, exceptions and decision points.
2. Define the desired outcome and baseline the present cost, time or error level.
3. Identify the systems, documents, data and permissions involved.
4. Choose the appropriate autonomy level.
5. Design escalation, logging and monitoring before the pilot.
6. Test with representative cases, including failure and exception scenarios.
7. Expand only after the workflow produces reliable results.

A modern data platform can be part of the foundation when the agent needs governed access to reusable operational data across systems.

Frequently asked questions about AI agents

What are AI agents?

AI agents are software systems that use context, reasoning and tools to work toward a defined goal. They can decide what to do next and complete several steps within set permissions. In business workflows, they often gather information, check rules, prepare actions, handle exceptions and interact with connected systems.

How do AI agents make decisions?

An AI agent evaluates available context against its goal, business rules and permitted actions. It may use a language model, deterministic rules, other models or a combination. Good implementations limit the possible actions, define escalation conditions and log what the agent used and did.

How do AI agents differ from AI assistants?

An AI assistant generally responds to a person's prompt and helps that person complete a task. An AI agent can continue through several steps, choose a next action and use connected tools within a defined workflow. The practical difference is agency and controlled execution, not whether the interface looks like a chat.

What is the difference between an AI agent and agentic AI?

An AI agent is a system performing a defined task or workflow. Agentic AI is the broader approach of using governed agents, tools, data and orchestration across connected processes. One agent may support a single approval flow; an agentic system may coordinate several agents and systems across a larger operation.

Do AI agents always use large language models?

No. Some agents use large language models to interpret language or plan steps. Others combine models with rules, optimization, retrieval and traditional software. The right design depends on the task, risk, data and required level of control.

Can AI agents work autonomously?

They can perform selected actions autonomously when permissions, monitoring and risk make that appropriate. Many business use cases should start with decision support or human approval. Maximum autonomy is not the objective; reliable value at the right control level is.

What are examples of AI agents in business?

Examples include agents that prepare customer orders from documents, validate master data, triage support cases, coordinate approval workflows, investigate supply-chain exceptions or recommend production responses. The strongest use cases combine a clear process, reliable data, controlled system access and measurable outcomes.

From understanding AI agents to applying them

The best AI-agent opportunity is rarely the broadest one. It is the workflow where manual handling, fragmented information and repeated exceptions create a measurable cost—and where the organisation can define the right permissions and review points.

If you are ready to identify that workflow, explore Elvenite Agentic AI and the practical route from use-case selection to a governed pilot.