What is Agentic AI? How AI Agents Are Changing Enterprise Work
Artificial intelligence has spent the last few years helping people find information, write content and generate answers. The next phase is more ambitious.
AI is beginning to take on work.
That is the promise of agentic AI, systems that can understand a goal, plan a sequence of actions, use software tools, and adjust their approach as they work toward an outcome. Unlike a conventional chatbot, an AI agent is designed to do more than respond to a prompt. It can act within defined boundaries.
For businesses, that distinction could be significant.
Instead of asking an employee to move information between several applications, for example, an AI agent could retrieve the information, update the relevant systems and trigger the next step in a workflow.
The technology is still developing. So are the rules around it. But in 2026, AI agents are moving from experimental projects into enterprise workflows.
What is agentic AI?
Agentic AI refers to AI systems that can independently plan and execute actions to achieve a defined goal, usually with human oversight and predetermined boundaries.
An agent can typically:
- Understand an objective
- Gather relevant information
- Reason about the next step
- Use tools, APIs or business applications
- Complete multiple tasks
- Evaluate results
- Adjust its actions when circumstances change
Microsoft describes AI agents as systems that perceive their environment, make decisions and take actions to achieve specific goals.
That makes agentic AI different from traditional automation.
A rules-based automation system follows a predefined sequence: if X happens, do Y.
An AI agent has more flexibility. It can determine how to reach a goal based on the information available to it.
How are AI agents different from chatbots?
A chatbot generally waits for a question.
An AI agent can be given an objective.
That may sound like a small distinction, but it changes how businesses can use the technology.
Imagine a customer reports a problem with an order.
A chatbot might explain the company’s return policy.
An AI agent could potentially check the customer’s order, identify the problem, determine whether the order qualifies for a replacement, create the relevant request and notify the customer.
The agent is not simply generating text.
It is participating in the workflow.
That shift, from answering to executing, is one of the most important developments in enterprise AI.
Why are AI agents becoming important to businesses in 2026?
Companies have already spent years automating individual tasks.
The next opportunity is connecting those tasks.
Deloitte’s 2026 research found that 85% of surveyed companies expect to customise AI agents for their specific business needs, while nearly three-quarters plan to deploy agentic AI within two years. At the same time, only 21% of those organisations reported having mature agent governance.
That contrast tells an important story.
Interest is moving quickly.
Readiness is not.
Businesses are discovering that deploying an AI agent is relatively straightforward compared with redesigning the processes around it.
How are businesses using AI agents?
Enterprise use cases are emerging across almost every major business function.
Customer service
AI agents can handle parts of the customer-service journey, from finding account information to processing routine requests.
A human employee can remain involved when a case requires judgement, negotiation or empathy.
The potential benefit is not simply lower costs. It is reducing the amount of time employees spend moving information between systems.
IT and software development
AI agents can assist with code generation, testing, documentation and troubleshooting.
An agent may be able to identify an issue, examine relevant logs, suggest a fix and open the appropriate ticket.
In software development, this could shift developers from manually completing every step toward supervising systems that handle parts of the development cycle.
Finance
Finance teams can use agents to gather information, reconcile records, prepare reports and flag unusual transactions.
High-risk decisions still require appropriate human review, but agents can take care of much of the administrative work surrounding them.
Sales and marketing
An AI agent could research an account, analyse previous interactions, prepare a briefing and update a CRM system.
Marketing teams can use agents to monitor campaigns, analyse results and recommend adjustments.
The important change is that the system can potentially move from analysis to action.
Supply chains
Supply chains involve multiple systems and decisions, making them a natural area for agentic AI.
Agents can monitor inventory, track shipments, identify potential disruptions and coordinate information across different applications.
IBM notes that deployed AI agents can interact with databases, business software and other AI systems to complete tasks within real-world workflows.
What is multi-agent AI?
Not every problem needs a single AI agent.
A more complex workflow can involve several specialised agents working together.
One agent might analyse demand. Another could examine inventory. A third could review logistics options. An orchestration layer can then coordinate their work.
McKinsey defines agentic systems as architectures in which different AI agents coordinate, plan, reason and execute tasks across workflows.
This approach could be particularly useful for large enterprises where a single business process crosses several departments.
But it also introduces another layer of complexity.
More agents mean more systems to monitor, secure and govern.
What are the benefits of agentic AI?
The attraction is straightforward: AI agents can potentially handle work rather than merely assist with it.
Businesses could use them to:
- Automate multi-step processes
- Reduce repetitive administrative work
- Speed up customer service
- Improve employee productivity
- Connect disconnected business systems
- Analyse information continuously
- Respond to operational changes faster
- Support employees with complex workflows
- Scale certain processes without adding equivalent manual effort
Microsoft’s 2026 Work Trend Index describes this shift as AI and agents taking on more execution while people gain more capacity to direct work, make decisions and own outcomes.
That may ultimately be more important than the technology itself.
The goal is not to replace every human task.
It is to change who, or what, does each part of the work.
What are the risks of AI agents?
Greater autonomy creates greater responsibility.
An AI system that only generates a draft can usually be reviewed before anything happens.
An agent that can send an email, approve a transaction or modify a database can cause real consequences.
That makes governance essential.
Businesses need controls around permissions, identity, data access, monitoring, human approval and escalation.
Gartner reported in April 2026 that only 13% of organisations believed they had the right AI-agent governance in place, while warning about risks including misinformation, excessive data sharing and data loss as agent deployments expand.
There is also the problem of AI agent sprawl.
If every department starts creating its own agents, organisations can quickly lose track of what those agents are doing and what information they can access.
Will AI agents replace employees?
That is the wrong question to ask in isolation.
The more immediate change is likely to be the redesign of jobs.
Deloitte found that 43% of surveyed business leaders expect significant workforce disruption from agentic AI within the next 12 to 18 months, including changing job requirements, new training needs and new ways of combining human and AI capabilities.
Some tasks will become automated.
Other responsibilities will become more important.
Employees may spend less time collecting information and more time evaluating it. They may supervise AI systems, handle exceptions, make decisions and manage relationships.
The result will depend heavily on how companies redesign work.
What does the future of enterprise AI look like?
Agentic AI could eventually change the way businesses interact with software itself.
Gartner estimates that up to $234 billion of enterprise application spending could be exposed to agentic AI by 2030, because agents may increasingly complete tasks across several applications without employees interacting directly with every traditional software interface.
That is a substantial shift.
For decades, employees have learned how to operate software.
The emerging model is different: people describe the outcome they want, and AI coordinates the systems required to achieve it.
But reaching that point will require more than powerful models.
Companies will need reliable data, secure infrastructure, clear governance and employees who know how to work alongside autonomous systems.
Deloitte’s August 2026 research found that only 5% of surveyed organisations considered their business processes highly prepared for AI agents, while just 15% had scaled orchestrated, cross-functional multi-agent adoption.
That gap may determine who benefits first.
Conclusion
Agentic AI represents a meaningful change in enterprise technology.
Traditional AI has largely helped people produce information.
Agentic AI aims to help businesses get work done.
That could mean an agent handling a customer request, coordinating a supply-chain task, preparing a financial workflow or assisting developers across several stages of software delivery.
But autonomy should not be confused with independence.
The most effective enterprise AI systems will likely combine machine execution with human judgement, clear limits and continuous oversight.
The companies that understand that distinction will be better positioned to use AI agents as more than another productivity tool.
The real promise of agentic AI is not that machines can do everything. It is that businesses can rethink who or what should do each part of the work.