Agentic AI is coming to enterprise workflows: Is your technology stack ready?

Most organizations are familiar with generative AI: ask a question, provide a prompt, and receive an answer.

Agentic AI takes that concept a step further.

Instead of only generating information, an AI agent can be given a goal and take a series of actions to help accomplish it. Depending on how it is designed, an agent might retrieve information, interact with applications, call APIs, update records, trigger processes, or coordinate tasks, with varying levels of human oversight.

Think of the difference this way: Generative AI can tell an employee what needs to be done. Agentic AI can potentially help do it.

That shift has significant implications for enterprise technology.

Agentic AI requires a technology environment that can securely connect AI agents with trusted data, applications, APIs, and workflows while controlling what agents can access and what actions they can take.

From answering questions to taking action

Consider a routine IT service request.

A generative AI assistant might explain how an employee can resolve the problem.

An AI agent could potentially take the next steps: identify the employee, retrieve relevant account information, check system status, initiate an approved process, update the service ticket, and notify the employee when the task is complete.

That ability to move across systems is what makes agentic AI potentially valuable, and more complicated.

McKinsey describes AI agents as software entities capable of pursuing goals, executing tasks, and making decisions while interacting with tools, systems, and other agents.

For business leaders, the important distinction is straightforward: AI is moving closer to the operational workflow.

Applications need to work together

An AI agent cannot complete a process if it cannot interact with the applications involved.

This makes integration increasingly important.

APIs—which allow different software applications to exchange information and initiate actions—can provide agents with structured ways to interact with enterprise systems.

An agent supporting a customer service process, for example, might need to retrieve account information from one application, check an order in another, update a case, and initiate a follow-up action.

Disconnected systems and difficult-to-integrate legacy applications can limit how much of that process an agent can complete.

For IT leaders, agentic AI creates another reason to ask whether enterprise applications can securely exchange information and support connected workflows.

Agents need trusted data

Access to more data does not necessarily make an AI agent more effective.

Agents need reliable information and sufficient context to determine what actions are appropriate.

Organizations therefore need to understand:

• Where critical data resides.
• Whether that data is accurate and current.
• Which information an agent can access.
• How sensitive information is protected.
• Whether data use can be tracked.

These aren't entirely new challenges. But the consequences change when AI can use information to initiate an action rather than simply generate an answer.

Every agent needs rules

If an employee accesses a business application, the organization typically knows who that employee is and what that person is authorized to do.

AI agents require similar controls.

An organization needs to determine which agent is acting, what systems and data it can access, and which actions it has permission to perform.

NIST has identified AI agent identity and authorization as an emerging cybersecurity consideration.

In practical terms, organizations may need to establish:

• A unique identity for each agent.
• Clearly defined permissions.
• Limits on application and data access.
• Controls over which actions an agent can perform.
• Records of agent activity.
• Processes for changing or removing access.

This applies a familiar security principle to a new type of technology: give the agent only the access it needs to perform its job.

Organizations need visibility into agent activity

As agents take more actions, organizations also need to see what they are doing.

This is sometimes described as observability; the ability to monitor and understand activity across a technology environment.

For agentic AI, that means being able to determine which systems an agent accessed, what information it used, which actions it performed and whether something went wrong.

That visibility can help IT teams identify failures, investigate unexpected behavior, maintain audit trails, and determine whether agents are delivering the intended business outcome.

Human oversight remains part of the architecture

Agentic AI does not mean every process should become autonomous.

The appropriate level of independence depends on the action and its potential consequences.

Retrieving information presents one level of risk. Updating a customer record presents another. Initiating a financial transaction or changing a security configuration could require much stronger controls.

Organizations can establish different levels of autonomy accordingly:

Low risk- Agent acts automatically

Moderate risk- Agent acts within defined rules

Higher risk - Human approval is required

The objective isn't maximum autonomy. It is controlled autonomy based on business risk.

Agentic AI changes the technology readiness question

Organizations don't necessarily need to rebuild their technology environments before exploring agentic AI.

But they do need to understand whether their existing architecture can support AI that interacts with the business rather than simply providing information to it.

That means examining how several technology foundations work together:

Applications - APIs - data - identity - security - workflows - oversight

For IT and business leaders, this creates a different AI readiness conversation.

The question is no longer only:

Are we ready to use AI?

Increasingly, organizations also need to ask:

Are we ready to let AI act?

FAQs

What is agentic AI?

Agentic AI refers to AI systems that can pursue a goal, determine steps, and take actions using applications, data, and other tools, with varying levels of human oversight.

What is an AI agent?

An AI agent is software that uses AI to understand a goal and perform tasks toward achieving it, such as retrieving information, interacting with applications, or initiating approved actions.

How is agentic AI different from generative AI?

Generative AI primarily produces responses or content. Agentic AI can go further by using tools and applications to perform actions and complete multiple steps toward a defined goal.

What technology does agentic AI require?

Enterprise agentic AI can depend on trusted data, connected applications, APIs, identity and access controls, cybersecurity, monitoring, governance, and clearly defined workflows.

Why are APIs important for AI agents?

APIs allow software systems to exchange information and initiate actions. They can provide structured connections that enable AI agents to interact with enterprise applications.

How should organizations secure AI agents?

Organizations should control agent identity, permissions, data access, and application access while monitoring activity and requiring human approval when an action presents greater business or security risk.