24/08/2026
How Are AI Agents Changing the Way UK IT Teams Work?
Artificial intelligence is moving beyond chatbots and simple automation. The next major shift in workplace technology is the rise of AI agents: systems that can interpret a goal, decide which steps are required, use digital tools and complete parts of a workflow with limited human intervention.
For UK technology employers, this creates a significant change in how IT work can be organised. Instead of AI simply answering a question or generating text, an AI agent can potentially investigate an issue, retrieve information, interact with software, create a response and escalate the task when human judgement is required.
This matters for the UK IT jobs market because the impact of AI agents is likely to be different from traditional automation. Automation normally follows predefined rules. AI agents can operate across more flexible tasks and adapt their actions according to the information they receive.
For IT professionals, the important question is therefore not simply “Will AI agents replace IT jobs?”
A more useful question is:
“Which IT tasks will AI agents perform, and which skills will become more valuable when humans work alongside them?”
What exactly is an AI agent?
An AI agent is a software system designed to pursue a goal by interpreting information, making decisions and taking actions through connected tools or systems.
A traditional chatbot might answer:
“How do I reset my password?”
An AI agent could potentially identify the employee, check the relevant account information, initiate the approved reset process and confirm the outcome.
The difference is action.
Generative AI primarily produces content.
An AI agent can potentially use AI to perform a sequence of tasks.
Depending on the system, an agent may interact with:
- Databases
- APIs
- CRM systems
- Cloud platforms
- IT service-management systems
- Business applications
- Monitoring tools
- Security platforms
- Internal knowledge bases
This makes AI agents particularly relevant to IT operations.
Why are AI agents becoming important for UK businesses?
Businesses are under continuous pressure to improve productivity while controlling costs and managing increasingly complex technology environments.
IT teams may have to manage:
- More applications
- More cloud infrastructure
- More cybersecurity alerts
- More employee requests
- More data
- More compliance requirements
AI agents could help organisations handle parts of this workload.
For example, an IT operations agent might monitor an application, identify an unusual event, investigate logs and create an incident record.
A human engineer could then review the evidence and decide what action should be taken.
The potential benefit is not necessarily removing the engineer.
It is reducing the amount of time the engineer spends gathering information.
How are AI agents different from traditional automation?
Traditional automation generally follows predetermined rules.
For example:
If a server reaches 90% capacity → send an alert.
An AI-enabled system could potentially go further:
A server shows unusual behaviour → investigate recent logs → compare with historical patterns → identify possible causes → check related services → summarise findings → recommend an action.
This distinction is important.
Automation is generally:
Rule → action
AI-agent workflows can be closer to:
Goal → reasoning → tools → actions → evaluation
That flexibility is one reason organisations are increasingly interested in agentic AI.
Which IT jobs could be affected by AI agents?
AI agents are most likely to affect roles containing substantial amounts of structured, repeatable digital work.
Potentially affected areas include:
- IT support
- Software testing
- IT operations
- Data operations
- Cloud monitoring
- Cybersecurity operations
- Service management
- Business analysis
- Technical documentation
However, “affected” does not automatically mean “eliminated”.
A job consists of many different tasks.
An AI agent may automate one part while leaving other responsibilities entirely human.
For example, a cybersecurity analyst may spend less time collecting information but more time assessing risk and responding to sophisticated threats.
How could AI agents change IT support jobs?
IT support is one of the clearest examples.
A traditional support workflow might look like:
Employee raises ticket → support technician investigates → technician searches documentation → technician resolves issue → ticket closed.
An AI-enabled workflow could become:
Employee raises ticket → AI agent understands request → checks knowledge base → gathers account information → performs approved action → documents resolution → escalates unusual cases.
This could reduce the volume of repetitive tickets reaching human technicians.
But complex support cases would still require people.
Human IT professionals may increasingly focus on:
- Difficult incidents
- User communication
- System problems
- Security-sensitive requests
- Root-cause analysis
- Infrastructure issues
This could gradually shift IT support careers towards higher-value problem-solving.
Could AI agents replace IT support technicians?
Not completely.
The more realistic possibility is that AI agents reduce the amount of repetitive work performed by support teams.
A password reset is relatively structured.
A company-wide authentication failure is not.
A simple software installation request is structured.
A complex compatibility problem across multiple systems may require human investigation.
This means IT support professionals who understand automation, cloud platforms, cybersecurity and AI-enabled service management could become more valuable.
The role may evolve from:
Ticket resolver
to:
Technology problem solver and AI-assisted service specialist.
How could AI agents change software development?
Software engineering may be one of the most interesting areas for agentic AI.
Modern AI systems can already assist developers with:
- Code generation
- Debugging
- Documentation
- Testing
- Code review
- Refactoring
Agentic systems can potentially connect these capabilities into a larger workflow.
For example:
Requirement → code generation → test creation → test execution → error analysis → code modification → documentation
This could allow development teams to automate portions of the software lifecycle.
But human software engineers remain important because software development involves much more than producing code.
Engineers still need to determine:
- What should be built?
- How should it be designed?
- Is it secure?
- Can it scale?
- Does it meet business requirements?
- What technical compromises are acceptable?
The value of software engineering could therefore shift further towards architecture, validation and decision-making.
Could AI agents change the role of DevOps engineers?
Yes.
DevOps already involves extensive automation.
AI agents could potentially assist with:
- Infrastructure monitoring
- Deployment analysis
- Incident investigation
- Log analysis
- Configuration checks
- Performance optimisation
- Cloud resource management
Imagine an application suddenly becoming slow.
Instead of a monitoring system merely producing an alert, an AI agent could potentially collect relevant metrics, inspect logs, compare recent deployments and prepare an incident summary.
A DevOps engineer could then review the findings.
This changes the workflow from:
Engineer searches for information
to:
Agent gathers information → engineer makes the decision.
That distinction could save significant time.
How could AI agents affect cybersecurity jobs?
Cybersecurity may become one of the most important areas for agentic AI.
Security teams deal with huge quantities of information:
- Alerts
- Logs
- Network activity
- Identity events
- Endpoint data
- Threat intelligence
Security operations centres can struggle with alert volumes.
AI agents could potentially help investigate routine alerts, correlate events and gather evidence.
However, cybersecurity also presents a major limitation.
Attackers can deliberately manipulate systems.
False positives can be dangerous.
An incorrect automated response could cause business disruption.
Therefore, security teams need strong human oversight.
Future cybersecurity professionals may increasingly need to understand both:
How to use AI for defence
and:
How attackers can exploit AI-enabled systems.
Will AI agents create new IT jobs?
They are likely to create new responsibilities and specialisms, although exactly how job titles develop will vary between employers.
Potential areas include:
- AI agent development
- AI orchestration
- AI operations
- AI governance
- AI security
- AI platform engineering
- Agent workflow design
- AI quality assurance
- AI systems integration
Some of these may become standalone positions.
Others may simply become responsibilities added to existing software, cloud, data or cybersecurity roles.
This is an important point for IT job seekers.
The future job title may not contain the word AI.
A cloud engineer may work on agent infrastructure.
A cybersecurity analyst may secure AI agents.
A software engineer may develop agentic applications.
A business analyst may design agent-enabled workflows.
Why will AI agent governance become important?
Giving an AI system permission to take actions introduces risk.
An organisation must decide:
- What can the agent access?
- What actions can it perform?
- What requires human approval?
- How are decisions recorded?
- What happens when the agent makes a mistake?
- How can its actions be audited?
This creates a new area of IT responsibility.
Consider an AI agent that has access to an internal database.
If the agent can read information but cannot modify it, the risk profile is different from an agent that can change records.
Similarly, an agent that can recommend a cloud configuration is different from one that can automatically deploy it.
The more authority an agent receives, the more important governance becomes.
Why is human oversight important for AI agents?
AI systems can make mistakes.
With a chatbot, an incorrect answer may be inconvenient.
With an AI agent, an incorrect action could create a business problem.
For example, an agent might:
- Modify the wrong configuration
- Escalate the wrong security alert
- Delete incorrect information
- Trigger unnecessary infrastructure changes
- Misinterpret a user request
This is why organisations need human-in-the-loop processes for sensitive tasks.
The goal is not to prevent AI agents from acting.
The goal is to ensure that the level of autonomy matches the level of risk.
Which IT skills will become more valuable as AI agents grow?
Several skills could become increasingly important.
Systems architecture
Professionals need to understand how agents interact with existing technology.
API integration
Agents need access to digital tools and services.
Cloud computing
Many AI applications depend on cloud infrastructure.
Cybersecurity
Agent access introduces new security considerations.
Data engineering
Agents require reliable information.
Automation
Understanding workflows makes it easier to identify where agents can add value.
Software engineering
Agents still need to be developed, tested and maintained.
AI governance
Organisations need controls around autonomous systems.
Critical thinking
Humans must evaluate agent decisions.
The strongest profile may therefore be:
IT expertise + AI + automation + security + business understanding.
Why are APIs becoming more important for AI agents?
An AI agent becomes much more useful when it can interact with other software.
APIs provide that connection.
For example, an agent could potentially use APIs to:
- Retrieve customer information
- Search a knowledge base
- Create a support ticket
- Check cloud resources
- Query a database
- Send a notification
- Update a workflow
This means IT professionals who understand APIs, authentication, permissions and integrations can play an important role in agentic AI projects.
AI agents are therefore not isolated AI tools.
They are increasingly becoming part of broader software ecosystems.
Will AI agents increase demand for cybersecurity professionals?
Potentially.
Every additional system that can take automated actions introduces security considerations.
Organisations may need professionals who can evaluate:
- Agent permissions
- Authentication
- Data access
- Prompt injection
- Tool misuse
- API security
- Identity controls
- Audit trails
AI agents can therefore create security work even while automating some security tasks.
This is a recurring pattern in technology:
New automation reduces certain tasks while creating new requirements around managing and securing the automation.
How should IT professionals prepare for agentic AI?
The best approach is not to learn every new AI agent platform.
Instead, understand the underlying concepts.
Start with:
- AI fundamentals
Understand generative AI, LLMs and their limitations.
- APIs
Learn how systems communicate.
- Automation
Understand workflow automation and orchestration.
- Cloud
Learn how modern applications are deployed.
- Security
Understand identity, permissions and data protection.
- Your existing IT speciality
Keep building depth in your core profession.
- AI-agent experimentation
Build small projects to understand how agents work.
This creates durable knowledge even when individual tools change.
Should graduates learn AI agents?
Yes, but they should not treat agentic AI as a replacement for core IT knowledge.
A graduate who understands Python, APIs, databases and software engineering can learn AI agents more effectively than someone who only knows how to operate a visual AI tool.
For example, a graduate could build a small project where an AI system:
- Receives a support request.
- Classifies the issue.
- Searches a knowledge base.
- Generates a suggested response.
- Requests human approval.
- Records the outcome.
Such a project demonstrates several skills at once.
It shows:
- Programming
- AI
- APIs
- Workflow design
- Data handling
- Human oversight
That is much more useful for a portfolio than simply saying “I know AI.”
How could AI agents change IT management?
The impact may extend beyond technical roles.
IT managers may increasingly manage teams where humans and AI systems work together.
This creates new management questions.
For example:
- Which tasks should be automated?
- Which require human approval?
- How should AI performance be measured?
- Who is responsible when an agent makes an error?
- How should employees be trained?
- How should productivity be measured?
Technology leadership could therefore become partly about designing human-AI workflows.
The manager's job may increasingly involve deciding where automation creates value and where human expertise is essential.
Could AI agents make small IT teams more productive?
This is one of their potentially significant benefits.
A small IT team can struggle when it has to manage hundreds of applications, users and infrastructure components.
AI agents could potentially handle portions of repetitive monitoring, documentation and information gathering.
This may allow smaller teams to support larger environments.
However, productivity gains depend heavily on implementation quality.
An organisation cannot simply deploy an agent and assume productivity will automatically increase.
Processes must be redesigned around the technology.
What are the biggest risks of AI agents?
The main risks include:
- Incorrect actions
- Excessive permissions
- Data leakage
- Security vulnerabilities
- Poor oversight
- Unclear accountability
- Integration failures
- Hallucinated information
- Over-automation
One of the most important principles is therefore:
An AI agent should not automatically receive more authority than it needs to perform its task.
Least-privilege security becomes particularly important when AI systems can take actions.
Will AI agents make IT jobs more strategic?
For some roles, potentially.
If AI takes over parts of repetitive information gathering, IT professionals may have more time for:
- Architecture
- Planning
- Risk management
- Problem-solving
- Stakeholder engagement
- Innovation
- Strategy
This is similar to previous waves of automation.
When technology reduces manual work, the remaining human work often shifts towards tasks requiring judgement.
But this transition is not automatic.
Employers need to redesign roles and invest in training so employees can move into higher-value responsibilities.
What does agentic AI mean for IT job seekers?
For job seekers, the rise of AI agents creates an important career opportunity.
Instead of asking:
“Which AI job should I apply for?”
consider:
“How can AI agents change the IT profession I already want to enter?”
If you want to become a software engineer, learn AI-assisted development and agent integration.
If you want cybersecurity, learn AI security and automated investigation.
If you want cloud engineering, explore AI infrastructure and autonomous operations.
If you want IT support, learn intelligent service management and workflow automation.
This approach connects AI to a real career path.
What will AI agents mean for the future of UK IT jobs?
AI agents are likely to change the structure of IT work rather than simply eliminate entire categories of jobs.
Some repetitive tasks will become easier to automate.
Some existing roles will absorb AI-agent responsibilities.
New specialist roles may emerge.
Human professionals will remain responsible for judgement, governance, architecture, security and business decisions.
The biggest change may therefore be the relationship between people and software.
For decades, employees have used software as a tool.
With AI agents, software can increasingly become an active participant in the workflow.
That is a major change.
For the UK IT workforce, the professionals best positioned for this transition are unlikely to be those who simply know the latest AI terminology.
They will be people who understand technology deeply enough to decide:
What should the AI agent do?
What should it never do?
How should it be monitored?
How should humans work with it?
And most importantly:
How can it solve a genuine business problem safely?
That is where the future value of AI-agent skills is likely to emerge.
Frequently Asked Questions
What are AI agents?
AI agents are software systems that can interpret goals, reason through tasks, use connected tools and perform actions with varying levels of human supervision.
How are AI agents different from chatbots?
A chatbot primarily responds to users, while an AI agent can potentially use tools, interact with other systems and complete multi-step tasks.
Will AI agents replace IT jobs?
AI agents are more likely to automate or change specific tasks within IT jobs than eliminate every role. The impact will vary according to the complexity and level of human judgement required.
Which IT jobs could be affected by AI agents?
IT support, software development, testing, DevOps, cybersecurity operations, data operations and service management may all experience changes as agentic AI becomes more capable.
Are AI agents useful for IT support?
Yes. AI agents can potentially handle repetitive support requests, search knowledge bases, gather information and perform approved actions before escalating complex issues to human technicians.
Will software engineers still be needed if AI agents can write code?
Yes. Software engineering includes architecture, security, testing, requirements, system integration and decision-making in addition to writing code.
What skills are useful for AI agent careers?
Useful skills include software engineering, APIs, cloud computing, automation, cybersecurity, data engineering, AI fundamentals and systems architecture.
Why is cybersecurity important for AI agents?
AI agents may have access to sensitive data and business systems. Security professionals are needed to control permissions, protect data, monitor activity and reduce risks associated with autonomous actions.
Should graduates learn AI agents?
Yes, but AI-agent knowledge should complement core IT skills such as programming, databases, cloud, networking or cybersecurity.
What is human-in-the-loop AI?
Human-in-the-loop AI means that people remain involved in reviewing, approving or supervising AI decisions, particularly when actions carry significant business or security risks.
Can AI agents make small IT teams more productive?
Potentially. AI agents can automate portions of monitoring, information gathering, documentation and workflow management, allowing human teams to concentrate on more complex tasks.
What is the biggest risk of AI agents?
One of the biggest risks is allowing an AI system to take actions without appropriate controls, permissions, monitoring and human oversight.
