Key takeaways
- Agentic AI is useful when the job needs more than a prompt: it can plan, decide and act across several steps.
- Not every process needs an AI agent. The best use cases are valuable, repeatable and complex enough to justify the build.
- Hiring the right AI Specialists matters as much as choosing the technology. The right team turns an interesting agent into something reliable and useful.
Generative AI can write the email. Agentic AI can work out that the email needs sending, find the right customer information, draft it, update the CRM and decide whether a human needs to step in.
That extra autonomy is where things get interesting. It is also where many UK businesses are still finding their feet. Department for Science, Innovation and Technology research found that, among businesses already using AI, just 7% were using agentic AI, making it the least-adopted AI technology studied. The same research found that 32% faced significant barriers to implementing it.
So, forget the futuristic demos for a moment. The real question for UK SMEs is simple: where can agentic AI genuinely save time, reduce manual work or improve decisions? This guide gives you 12 practical examples of how businesses are using it.
What is agentic AI?
Agentic AI is artificial intelligence that can work towards a goal with limited human supervision. Instead of simply responding to a prompt, it can decide what needs to happen next, plan the steps, use available tools or data and take action.
An AI agent is the individual system doing that work.
The terminology can get muddled, so here is the simplest way to separate it:
- AI agent: A system designed to complete a specific task or work towards a goal.
- Agentic AI: The broader approach that gives AI more autonomy to reason, plan, make decisions and act towards an outcome.
- Multi-agent system: Several specialised AI agents working together, with each handling part of a larger task.
In short, agentic AI moves AI from simply answering questions towards getting work done. If you want to see how individual agents work in practice, our guide to the best AI agents for SMEs covers the different types, tools and what it takes to build one.
Agentic AI vs generative AI: A simple example
The easiest way to understand the difference is to look at what happens after the first output.
Generative AI is mainly designed to create something: text, code, images, summaries or answers. Agentic AI can use those capabilities as part of a wider process, deciding what to do next and taking action across different tools or systems.
| Task | Generative AI | Agentic AI |
|---|---|---|
| Customer complaint | Drafts a response | Checks the order, applies company policy, decides the next step, updates the CRM and sends or escalates the response |
| Sales lead | Writes an outreach email | Researches the lead, scores it, personalises outreach, updates the CRM and schedules follow-up |
| Invoice | Extracts invoice information | Checks it against the purchase order, flags mismatches, routes approval and updates the finance system |
That does not mean the two are separate technologies. Agentic systems often use generative AI to complete individual steps, but add planning, decision-making and action around it.
12 Agentic AI examples businesses can actually use
The easiest way to understand agentic AI is to see it doing real work. These are not twelve versions of a chatbot. Each example starts with an event or goal, works through several steps and takes action within agreed boundaries.
| Agentic AI example | Business area | What it can do |
|---|---|---|
| Customer service agent | Customer service | Investigate and resolve routine cases |
| Sales agent | Sales | Qualify, research and follow up leads |
| Marketing agent | Marketing | Monitor campaigns and coordinate actions |
| Invoice agent | Finance | Check, match and route invoices |
| Recruitment agent | HR | Source, screen and coordinate candidates |
| IT service desk agent | IT | Diagnose and resolve routine requests |
| Coding agent | Development | Plan, code, test and fix software tasks |
| Reporting agent | Management | Gather data and produce recurring reports |
| Research agent | Strategy | Find, assess and synthesise information |
| Inventory agent | Ecommerce/retail | Monitor demand and initiate stock actions |
| Supply chain agent | Operations | Respond to supplier or logistics disruption |
| Contract/compliance agent | Legal/operations | Monitor obligations, changes and exceptions |
1. Customer service resolution agent
A customer raises a complaint. Rather than simply drafting a polite reply, an agent can identify the issue, retrieve their order history, check the relevant company policy and decide which approved resolution applies. It can then update the CRM, create the response and close or escalate the case.
Humans still step in where judgement carries more weight: high-value refunds, vulnerable customers, unusual circumstances or complaints with legal implications.
2. Sales qualification and follow-up agent
A new enquiry lands in your CRM. A sales agent can research the organisation, compare it against your ideal customer criteria, enrich missing details, score the opportunity and prepare personalised follow-up.
It might also schedule the next action and keep the CRM updated without a salesperson chasing every field manually.
Salespeople remain responsible for the conversations and commercial decisions that actually need human judgement. The agent clears away more of the admin around them.
3. Marketing campaign agent
Instead of waiting for someone to check campaign dashboards each morning, an agent can monitor agreed performance measures continuously.
If performance changes, it can pull supporting information from your analytics and CRM systems, identify likely causes, prepare a summary and make pre-approved adjustments.
Bigger decisions, such as substantial budget movements or a change in campaign strategy, can still require approval.
The point is not autonomous marketing. It is giving the system permission to handle defined actions without waiting for another prompt.
4. Invoice and accounts payable agent
An invoice arrives by email. An accounts payable agent can extract the information, find the relevant purchase order, check supplier details and compare amounts and terms.
If everything matches, it can route the invoice through the normal approval process and update the finance system. If something looks wrong, it can flag the discrepancy rather than quietly pushing it through.
Humans remain responsible for exceptions, disputed payments and approvals beyond agreed financial limits.
5. Recruitment agent
Recruitment creates plenty of repetitive work around the hiring decision itself. An agent can search candidate pools, compare profiles with role requirements, coordinate outreach, answer routine candidate questions and arrange interviews.
It's crucial to remember: AI can organise and support the process. Consequential employment decisions still need appropriate human judgement.
6. IT service desk agent
An employee reports that they cannot access a system. Instead of immediately adding another ticket to somebody's queue, an IT agent can identify the likely problem, check the user's permissions, consult internal documentation and carry out approved troubleshooting steps.
Routine requests such as password resets or standard software access can potentially be resolved end to end. Anything involving unusual security risks, elevated permissions or an unclear diagnosis goes to an IT professional.
7. Software development agent
A coding agent can take a development objective, inspect the relevant codebase, plan the work, edit files, run tests and respond to errors before returning the result for review.
That is a considerable step beyond autocomplete. Current coding agents already support multi-step development workflows involving codebase inspection, changes, testing and debugging. Our guide to the best AI coding tools for UK SMEs looks at how these tools differ in practice.
Someone still needs to own architecture, security, testing and what ultimately reaches production.
8. Business reporting agent
Monday morning does not need to start with somebody exporting five spreadsheets.
A reporting agent can collect information from your finance, sales, marketing or operational systems, bring it into one view, identify notable movements and produce a recurring management report.
If revenue drops sharply or another agreed threshold is crossed, it can also flag the issue for attention rather than simply reporting it next week. Reliable reporting, however, depends on reliable underlying data – exactly the groundwork a Data Engineer helps build.
9. Research and competitive intelligence agent
Instead of giving AI ten separate research prompts, give an agent the research objective.
It can decide which information it needs, search approved sources, compare findings, investigate gaps and return a structured briefing.
IBM gives a real-world example of a multi-agent legal research assistant that routes simpler and more complex queries between different specialist components.
For important commercial decisions, people still need to check the evidence, challenge assumptions and decide what the research actually means for the business.
10. Inventory and ecommerce agent
An inventory agent can watch sales velocity, stock levels, supplier lead times and expected demand rather than responding only when somebody notices a shelf is getting empty.
When stock looks likely to fall below an agreed level, it could prepare or trigger a reorder, adjust an approved replenishment plan or alert the purchasing team.
11. Supply chain disruption agent
This is where agency becomes particularly useful: the right action changes with the situation.
A delayed supplier shipment could trigger an agent to identify affected orders, assess alternative suppliers or routes, compare the available options and recommend or execute actions within agreed limits.
Humans can retain approval for supplier changes, major cost increases and decisions with wider customer or contractual consequences.
12. Contract and compliance monitoring agent
Contracts create work long after they are signed. An agent can monitor renewal dates, service-level commitments, obligations and agreed commercial terms, then flag deadlines or exceptions before somebody discovers them the hard way.
It could also compare new clauses against internal policies and route higher-risk terms to the appropriate person. Workday's current procurement agent, for example, uses contract and supplier data to monitor compliance and identify issues such as off-contract spend or overcharges.
Legal interpretation and higher-risk decisions should, of course, stay with qualified people.
When is agentic AI actually worth using?
Not every business process needs an AI agent. Sometimes a simple automation will do the job perfectly well - and cost less to build, test and maintain.
Agentic AI starts to earn its keep when the work involves several steps, changing circumstances and decisions along the way. A useful opportunity will usually have:
- A clear outcome the agent is working towards.
- Several connected steps rather than one repetitive action.
- Decisions that depend on what the agent finds.
- Reliable data it can access.
- Business systems or tools it needs to work across.
- Enough volume, repetition or value to justify the investment.
- Clear rules for when a person needs to take over.
A simple test is to look at the workflow itself.
If the process is basically "when X happens, do Y", conventional automation may be all you need.
If it is closer to "achieve X, work out the next step from the information available, then act accordingly", that is where agentic AI becomes much more important.
For SMEs, the aim should not be maximum autonomy. It should be using autonomy where it genuinely removes work or improves the process.
Off-the-shelf AI agent or build your own?
You do not need to build from scratch just because the words "agentic AI" are involved.
For a fairly standard job, a ready-made agent may already do most of what you need. The more your workflow depends on your own data, systems and business rules, however, the more likely you are to need configuration or a custom build.
| Choose | Makes sense when |
|---|---|
| Ready-made agent | The workflow is common and your requirements are fairly standard |
| Configure an existing platform | You need to add your own data, rules or integrations |
| Build a custom agent | The workflow, decisions or systems involved are specific to your business |
| Multi-agent system | Several specialist agents need to work together across a larger process |
So the decision is not simply "which AI should we buy?" Once an agent starts touching real customers, data or business systems, the bigger question becomes: who is going to build it properly, connect everything up and keep it working?
Who do you need to build agentic AI?
Building an agent is rarely one neat job for one neat job title. A simple internal agent might only need a couple of AI Specialists. But once you are connecting proprietary data, several business systems and multiple agents, it becomes a proper engineering project. The roles you need depend on where the complexity sits.
| Role | Where they fit |
|---|---|
| AI Consultant | Identifies worthwhile use cases, requirements, architecture, risks and governance before you start building |
| AI Engineer | Designs the agent, orchestration, model setup and wider AI architecture |
| AI Developer | Builds agent functionality and integrates AI capabilities into your applications |
| Machine Learning Engineer | Builds, fine-tunes or productionises models where off-the-shelf models are not enough |
| Data Engineer | Gets company data into usable shape and builds the pipelines agents rely on |
| Backend Developer | Connects the agent securely to APIs, databases and existing business systems |
| DevOps/MLOps Engineer | Handles deployment, monitoring, scaling and reliability once the system is live |
| QA Engineer | Tests workflows, permissions, edge cases and what happens when the agent gets things wrong |
You probably do not need all eight.
A contained customer-service or reporting agent might need an AI Engineer, AI Developer and some backend support. A multi-agent product working across several internal systems could need data, machine learning, infrastructure and testing expertise as well.
That is why the first hiring decision should not be "we need an AI person". Start with the problem you are solving, work out the technical gaps, then build the team around them.
At Black Piano, we already help UK SMEs build remote AI and machine learning teams around exactly that principle. Whether you need one specialist to strengthen an existing team or several people around a larger build, we can find the roles, employ them and handle the ongoing HR, payroll and compliance behind the scenes.
From a good idea to an agent that actually works
The fastest way to waste money on agentic AI is to start with the technology instead of the problem. A better route is to keep the first version narrow, useful and easy to test.
1. Pick one outcome
"We need agentic AI" is not an outcome. "Reduce the time spent triaging support tickets" is. Start with something specific enough to measure.
2. Map how the work happens today
Look at the inputs, decisions, systems, exceptions and approvals involved. If the existing process is unclear, automating it will not magically fix that.
3. Decide what the agent can access
That might include your CRM, internal knowledge base, email, APIs or databases. Give it what it genuinely needs, not everything it could possibly reach.
4. Set sensible boundaries
Be clear about what the agent can decide, what it can execute and what still needs human approval.
5. Build small and test properly
Test the obvious scenarios, but also the awkward ones: missing data, conflicting information, failed integrations and unusual requests.
6. Earn more autonomy
Do not give the agent more freedom just because it technically can handle it.
Track accuracy, reliability, escalation rates and business impact first. If the system performs well, widen its scope gradually.
The goal is not maximum autonomy. It is useful autonomy, applied where it actually makes the work better.
Build agentic AI – Hire the specialists with Black Piano
Once you know what you want your agent to do, the next challenge is finding the people who can build it without turning one AI project into a very expensive UK hiring spree.
That is where a remote team starts to make sense. India gives UK businesses access to a sizeable AI talent market. Quess Corp's 2026 workforce analysis estimates that around 920,000 professionals in India now work in AI-related roles.
At Black Piano, we give UK businesses access to that talent market. Through our model, businesses can save around 50–70% compared with local hiring, while recruiting AI Engineers, Machine Learning Engineers, Data Engineers, Developers and other specialists.
We find the people, employ them in India and handle payroll, HR and compliance. You get a dedicated remote team working as part of your business, without setting up an Indian entity.
Need the whole project delivered instead? Black Piano also helps businesses with outsourcing AI projects.
Talk to Black Piano and let's put the right team behind your agentic AI project.
FAQs
What is an example of agentic AI?
A customer service agent is a simple example. When a complaint arrives, it can check the customer's order, review company policy, decide which approved resolution applies, update the CRM and send a response. If the issue falls outside its limits, it passes the case to a person.
What are AI agents?
AI agents are systems designed to work towards a goal with some degree of autonomy. Unlike a basic chatbot that waits for prompts, an agent can plan steps, use data and tools, make decisions and take actions within rules set by the business.
What are some agentic AI examples in real life?
Businesses can use agentic AI for customer support, candidate sourcing, invoice processing, software development, research, IT support, stock management and supply-chain operations. The common thread is that the AI does more than generate an answer: it works through several steps towards an outcome.
What businesses can use agentic AI?
Agentic AI is not just for large enterprises. SMEs can use it just as effectively. What matters more than company size is whether you have a worthwhile workflow, accessible data and enough repetitive or high-value work to justify using an agent.
Do you need AI Engineers to build an AI agent?
If your business needs custom agents, then yes. That's a more technical job than a ready-made agent. Custom agents need proprietary data, APIs, permissions, orchestration or production deployment. That is where an experienced AI Engineer or wider AI team becomes valuable.
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