Updated: July 28, 2026

Most generative AI applications respond to a request and stop.

AI agents are designed to go further: they can pursue a goal across multiple steps, use tools, and adapt to the results of their own actions.

Agents therefore allow enterprises to automate more work, but they also introduce important questions about autonomy, control, reliability, and oversight.

In this article, we explain what AI agents are and how they work, then examine their enterprise applications, benefits, and challenges.

What is an AI agent?

An AI agent is a software system that uses an underlying AI model to determine how to pursue a defined goal. Instead of merely following a fixed sequence of preprogrammed steps, an agent can interpret the current state of a task, select an appropriate next step, use available tools to carry out that step, and use the result to guide what happens next.

Consider how a standard generative AI application and an AI agent might handle the same customer inquiry. With a standard AI application, a user typically directs each step of the process. They might ask the application to retrieve relevant account details, then ask it to compare those details with company policy, and then prompt it to generate a draft response for the customer. By contrast, an AI agent might handle these steps itself. It could determine which information it needs, retrieve account details, run the necessary checks, draft a response, and then route it for human review.

An agent can be invoked directly by users or triggered automatically by events in connected business systems. What makes it a form of agentic AI is that the model directs the task’s execution at runtime by selecting permitted steps and responding to their results.

The level of autonomy granted to agents can vary. An agent might be limited to making recommendations or preparing actions for human approval, while another agent might be authorized to carry out specific actions directly. In enterprise environments, agents typically operate within defined instructions, approved data sources and tools, permission boundaries, approval requirements, and escalation rules.

How do AI agents work?

Once an AI agent is given a goal, it uses its instructions and the context available for the task to determine how to pursue that goal. It then selects an appropriate next step from the actions available to it, such as producing an output, retrieving additional information, taking an action in a connected system, or requesting human input.

The result of each step updates the task state. The agent can then use the updated state to select another step, revise its approach, or determine that the task is complete.

Core components of an AI agent

Several components work together to enable an AI agent to operate:

AI model: The model interprets the task and the information presented to it, generates outputs, and selects the agent’s next step. The wider agent system supplies the model with relevant context and makes approved tools available for it to select.
Instructions: Instructions define the agent’s role, expected behavior, and operating rules. They can specify which tasks the agent should handle, how it should use available data and tools, which standards its outputs must meet, and when it should ask for clarification or escalate the task.
Task context, state, and memory: Task context is the information presented to the model when it determines what to do next. Task state is the evolving record of the current task, including the original request, steps completed, information retrieved, intermediate outputs, and work still pending. Relevant parts of the task state can be assembled into the context for each new decision. Agents may also use persistent memory to retain selected information across tasks or interactions. The agent system can then retrieve relevant information from that memory and add it to the task context when needed.
Tools: In agent systems, tools are callable capabilities that allow the agent to access information or act through external systems. Information tools might search a document collection or query a live business system. Action tools might update a record, send a message, run code, or submit an approval request. The permissions attached to available tools determine which data the agent can access and which external actions it can take.
Agent harness: The agent harness is the surrounding software layer that connects and coordinates the elements above. It assembles the context passed to the model, routes permitted tool calls to the appropriate tool, returns the results, and updates the task state. It also manages the agent loop, repeating the cycle of model decisions, tool use, and updated context until the task is complete or a stopping condition is reached. Depending on its design, the harness may enforce configured access restrictions, pause specified tool calls for human approval, and run validation checks before or after model and tool calls.

Further reading: How to build an AI agent: A practical guide for enterprises

What are the main types of AI agents?

AI agents can be broadly classified by the scope of their intended role.

General-purpose agents: These agents are configured to handle a broad range of tasks across different contexts, with instructions and tools that support several kinds of work. This flexibility can be useful, but it means testing more possible behaviors and tool interactions.
Specialized agents: These agents are configured for a defined role or narrower set of tasks, with instructions, data access, tools, and permissions tailored to that more limited scope. Their narrower remit makes it easier to set clear performance criteria and restrict what the agent can access or do.

These categories are best understood as ends of a spectrum rather than fixed classifications. Agents can also operate alone or as part of a multi-agent system, where several agents handle distinct parts of a broader task or process.

Examples of AI agents in the enterprise

AI agents can be used across a range of enterprise functions and workflows.

Here are some examples:

Customer service agents

A customer service agent could interpret a request, retrieve account or transaction details, and check the case against company policies. It might resolve a routine issue, make a permitted account change, or escalate a more complex case to an employee with the case history and completed checks.

Software development agents

A software development agent could inspect a codebase, investigate an issue, and make changes within an approved development environment. After identifying the affected code, it might implement a fix, run automated tests, and prepare a pull request for developer review.

Further reading: Introducing North Mini Code: Cohere’s first model for developers

Supplier review agents

During supplier onboarding, an agent could examine a new supplier request, review submitted documents and available supplier information, and check whether the required evidence has been provided. It might flag missing information or exceptions to onboarding requirements and assemble the case for procurement, legal, or security teams to review.

Security investigation agents

When an alert is triggered, a security investigation agent could gather related alerts, account activity, device information, and threat intelligence from approved systems. It could then organize the evidence and recommend a response. Depending on its permissions, it might also initiate a preapproved containment action or route the case to an analyst when further judgment or authorization is required.

Further reading: AI and cybersecurity: How AI strengthens cyber resilience

What are the enterprise benefits of AI agents?

AI agents extend the capabilities of AI from generating outputs to actively moving work forward.

Some of their main benefits to organizations include:

Higher throughput with less manual coordination

An agent can maintain task state, carry relevant information from one step to the next, and determine what needs to happen without requiring a person to direct every action. This reduces the need for people to initiate each successive step or re-enter information as a workflow progresses.

Also, since agents can continue working whenever no human input is needed, organizations can increase throughput by running multiple agents concurrently across separate tasks.

Adaptive task execution

Because an agent selects its next step at runtime, it can adapt to new information as the task unfolds, rather than following a single predetermined sequence.

For example, it might gather more information when something is missing, revise its approach after an unsuccessful action, or escalate a task when it lacks the information, authority, or permissions needed to proceed.

Connection to enterprise data and actions

Tool access allows an agent to retrieve current information from approved sources and carry out permitted actions in connected systems. This means it can work directly with the data and applications relevant to a task instead of merely producing an output that someone must implement manually.

Scalable access to specialized support

Organizations can configure specialized agents for defined roles and make them available across teams. This gives more users access to specialized assistance without requiring an expert to handle every routine request directly.

What are the main challenges of using AI agents?

The same capabilities that allow AI agents to move work forward also create distinct execution, control, and oversight challenges.

Some of the main challenges include:

Compounding errors across multiple steps

The result of one step can shape what the agent does next. An inaccurate model output, incorrect tool result, or incorrect information retained in the task state or retrieved from memory can therefore influence later decisions. In other words, a problem early in the task may lead to further incorrect outputs or actions before it is detected.

This is why high-impact agentic workflows need validation checkpoints at important stages. These checks can be automated, with human review added where the consequences of an error warrant it. The agent system should also be able to stop, retry, or escalate when a step produces an unexpected result.

Control of access, actions, and external inputs

Broad permissions can turn an incorrect decision into a consequential action, such as changing a record, sending a message, or exposing sensitive information. An agent can also be exposed to agent hijacking, a form of indirect prompt injection. This occurs when malicious instructions are embedded in content the agent processes, such as a document, email, webpage, or tool output. These instructions may direct the agent to disregard its intended operating rules, disclose information, or misuse its tools.

Access restrictions, approval requirements, and validation checks should therefore reflect both the sensitivity of the data involved and the potential consequences of each action the agent can take. These controls should form part of a broader AI governance framework that defines how agents are approved and monitored in practice.

Evaluation and oversight of variable execution

An agent can take different paths through similar tasks depending on how a request is phrased, what information becomes available, and whether earlier actions succeed or fail.

Teams therefore need to test representative tasks and edge cases before deployment and continue reviewing the agent’s behavior in production. They should also examine tool selection, intermediate actions, and escalation behavior, rather than only evaluating final outputs.

Cost, latency, and stopping behavior

Tasks that require many model and tool calls can take longer and cost more to complete. Without clear stopping conditions, an agent may also repeat actions or continue working without making meaningful progress.

Organizations can contain these problems by setting limits on steps, time, or cost and defining when the agent should stop, retry, or escalate the task for human review.

Put AI agents to work across the enterprise with Cohere

The value of AI agents is not about handing entire workflows over to fully autonomous systems.

In real-world enterprise settings, agents create value through controlled delegation: carrying tasks forward within defined boundaries while people remain accountable for the goals, exceptions, and high-consequence decisions that shape the final outcome.

North, our agentic AI workspace for enterprises, helps teams put this approach into practice. The platform provides a secure environment for building and deploying custom agents connected to business tools and data, with enterprise-grade protocols built into every layer.

Request a demo today.

AI agents: What they are and how they work