What is agentic AI? A guide with examples for SMEs

Summary

 

• Agentic AI is AI that independently carries out tasks: planning, deciding and acting inside your systems.
• The difference from generative AI: gen AI creates content on request, agentic AI executes processes without you steering every step.
• Concrete applications for SMEs: invoice processing, tenant communication, recruitment workflows, quote administration.
• Gartner expects 40% of all business applications to contain agentic AI features by 2026.
• A good starting point: pick one process with clear rules, measurable outcomes and low risk.

 

What exactly is agentic AI?

 

Agentic AI is software that, given a business goal, can autonomously plan and execute a multi-step workflow. The AI uses language models and other AI components to understand context, select the right tools, take actions and adjust based on feedback.

MIT Sloan defines agentic AI as a new generation of AI systems that are semi- or fully autonomous and can act without receiving explicit instructions at every step (MIT Sloan, 2025).

IBM adds that agentic AI systems can pursue complex goals with limited supervision, combining the flexible characteristics of large language models with the precision of traditional software (IBM, 2025).

The core idea: agentic AI is not a smarter chatbot. It is software that works like a digital employee with its own task list.

An agentic AI system typically works in four phases:

 

• Observe: the agent monitors the state of the system (new email, outstanding invoice, incoming request)
• Plan: the agent determines which steps are needed to reach the goal
• Execute: the agent acts: looks up information, fills in forms, sends messages, updates records
• Adapt: based on the outcome, the agent adjusts its approach if something does not go as expected

 

 

 

 

 

 

 

What is the difference between agentic AI and generative AI?

 

Generative AI, such as ChatGPT or Claude, creates content based on a question the user asks. You give a prompt, the AI writes text, and then it stops. The team member still has to copy, adjust and enter the output into the right systems.

Agentic AI handles that last step too. It is not about what the AI writes, but about what the AI does.

Forrester describes agentic AI as the next competitive frontier: where generative AI raises individual productivity, agentic AI can reshape the business model itself (Forrester, 2025).

The difference becomes clear with a concrete example. Suppose a supplier sends an invoice that does not match the order.

 

 

 

 

 

Generative AI

 

• What it does: writes a draft reply to the supplier when you describe the situation
• What it does not do: log in to your accounting system, compare the invoice against the order, flag the discrepancy or open a ticket

 

 

Agentic AI

 

• What it does: automatically detects the discrepancy, compares invoice and order, marks the difference in the ERP system, sends the supplier a personalised message and sets a follow-up task for the account manager
• What it does not do: wait until someone asks the question

 

The distinction is reactive versus proactive. Generative AI responds to what you ask. Agentic AI acts based on what is happening in your business.

 

 

How does agentic AI work in practice?

 

Most agentic AI systems consist of three building blocks: a language model that handles reasoning, tools that allow the agent to control external systems, and a memory function that retains context across multiple steps.

Weaviate describes this as agentic workflows: patterns in which AI agents, tools and memory are combined to build systems that adapt over time and can handle complex tasks (Weaviate, 2025).

In practice, this means an agentic AI system can:

 

• Log in to your CRM, ERP, mailbox or project management tool
• Look up information in multiple systems simultaneously
• Make decisions based on rules you define
• Take actions, such as creating a record, sending a message or scheduling an appointment
• Alert a team member when a situation falls outside agreed rules

 

What sets agentic AI apart from standard automation (RPA) is that it can handle variation. A traditional automation script fails the moment input deviates from the expected format. An agentic AI system recognises the deviation, independently determines the best approach and acts accordingly, or escalates to a team member if needed.

From Eindhoven, we at The Agentic Group work daily with SMEs that deploy these kinds of systems for their core processes, from invoice management to client communication.

 

 

Agentic AI for SMEs: concrete examples by sector

 

Agentic AI is not technology accessible only to large corporations. Particularly for SMEs, where every employee fills multiple roles, a digital worker that operates independently can make a significant difference.

Google Cloud reports that 74% of executives using AI agents achieve positive ROI within the first year (Google Cloud, 2025).

Here are four sectors relevant to Dutch SMEs:

 

Property management

 

• What an agent does: processes maintenance requests, links them to the right contractor, schedules the appointment and automatically informs the tenant
• Result: less manual email traffic, shorter response times, fewer complaints about communication

 

 

Accountancy and administration

 

• What an agent does: compares incoming invoices with purchase orders, flags discrepancies, books correctly matched invoices straight through and puts exceptions in a queue for a team member to review
• Result: shorter processing time per invoice, fewer errors, team members focus on exceptions instead of routine work

 

 

Construction and project management

 

• What an agent does: monitors project planning, signals delays, reschedules tasks and sends an update to all parties involved
• Result: less dependence on manual status updates, earlier detection of risks

 

 

Recruitment and staffing

 

• What an agent does: processes applications, matches candidates based on criteria, schedules interviews and sends status messages to candidates and hiring managers
• Result: shorter time-to-hire, more consistent candidate experience, less administrative load for recruiters

 

 

 

 

 

McKinsey documents that 62% of organisations are already experimenting with AI agents and 23% are scaling them in at least one function (McKinsey, 2025). That percentage is rising fast: in sectors such as accountancy and recruitment, agents are no longer an experimental project but an operational instrument.

 

 

What makes agentic AI different from a chatbot or RPA?

 

Many business owners are already familiar with two forms of automation: chatbots on websites and RPA scripts (Robotic Process Automation) that replicate fixed actions. Agentic AI is fundamentally different.

Microsoft Copilot's documentation makes the distinction clear: traditional chatbots are conversational interfaces with limited context awareness. They fail the moment a conversation becomes complex. AI agents, in contrast, are goal-driven systems that can plan, reason and execute tasks across multiple steps (Microsoft, 2025).

 

Chatbot

 

• Function: answers questions based on scripts or a knowledge base
• Limitation: does not act in external systems, no context across multiple sessions

 

 

RPA (traditional process automation)

 

• Function: copies a fixed sequence of clicks and inputs in defined systems
• Limitation: breaks the moment input deviates from the expected format, no reasoning capability

 

 

Agentic AI

 

• Function: understands a goal, plans the steps, uses tools across multiple systems simultaneously, adapts when something deviates and escalates to a person when the situation calls for it
• Limitation: requires solid data preparation, clear governance and thoughtful design

 

The comparison with AI agents and process automation shows that the real value lies not in the technology itself, but in the combination of reasoning capability and room to act. An agentic AI system does not need to be perfect. It only needs to perform better than the current situation: a team member going through the same steps manually.

 

 

When does agentic AI make sense for your business?

 

Agentic AI is not the right solution for every process. It works best when a number of conditions are met.

Salesforce puts it this way for the SME segment: start with processes that have high volume, low variation and clear success criteria, then build from there (Salesforce, 2025).

Processes that work well for agentic AI:

 

• High volume of repetitive steps (invoice processing, email sorting, status updates)
• Clear decision rules that can be formulated (if invoice exceeds order by more than 5%, flag as discrepancy)
• Measurable outcomes so you can see whether it is working
• Low risk if an error occurs, or a clear escalation path to a team member

 

Processes to approach with more caution:

 

• Situations that depend heavily on relationship and nuance (complex client negotiations)
• Processes where an error carries direct legal or financial consequences without review
• Cases where the data in your systems is incomplete or inconsistent

 

Gartner predicts that around 40% of all business applications will contain embedded agentic AI features by end of 2026, compared with less than 5% in 2024 (Gartner, via Sigma Computing, 2025). The question for SMEs is not whether agentic AI will become relevant, but when and with which process you start.

A good first step is an AI strategy that determines which processes in your specific business are the first candidates for agentic automation. Also take a look at what agentic AI solutions have concretely delivered for comparable businesses.

 

 

 

 

 

 

Frequently asked questions about agentic AI

 

What is agentic AI in simple terms?

 

Agentic AI is software that, given a goal, independently determines and executes the steps to reach that goal. Where a chatbot waits for your question, agentic AI acts on its own initiative: it logs in to systems, carries out actions and adjusts its approach when something does not work as expected.

 

What is the difference between agentic AI and a chatbot?

 

A chatbot answers questions through a conversational interface but does not carry out anything in external systems. Agentic AI is a goal-driven system that can plan, decide and act across multiple steps and systems. The difference is comparable to the difference between an adviser who tells you what to do and a team member who actually gets it done.

 

Is agentic AI the same as RPA or traditional process automation?

 

No. RPA (Robotic Process Automation) replicates a fixed sequence of steps and fails the moment input deviates. Agentic AI can handle variation: it understands context, makes decisions and adjusts its approach. This makes agentic AI suitable for processes that are too unstructured for classical automation.

 

Can agentic AI make decisions independently?

 

Yes, within the boundaries you set. You determine which actions the agent may take and when it brings in a team member. This is called the autonomy setting or governance layer. Good implementations give the agent latitude for routine decisions and preserve human oversight for exceptions and higher-risk situations.

 

How much does implementing agentic AI cost for an SME?

 

Costs vary considerably depending on the platform chosen, the complexity of the process and the integrations required. A focused pilot for one process typically starts in the range of a few thousand euros for a pragmatic implementation. The payback period is short when the selected process has sufficient volume and value.

 

What risks does agentic AI carry for my business?

 

The main risks are: the agent taking actions outside its intended boundaries, data leakage if the agent has access to sensitive information, and errors on unexpected input. These risks are manageable with clear governance, access controls, logging and an escalation path to a team member. Always start with a limited autonomous process before expanding.

 

What are good first processes to automate with agentic AI?

 

Strong candidates are invoice processing, email sorting and routing, status updates to clients or tenants, candidate screening in recruitment and maintenance requests in property management. These are processes with high volume, clear rules and measurable outcomes.

 

How do I get started with agentic AI as an SME?

 

Start by identifying one process that takes a lot of time, has clear steps and produces measurable results. Then define success criteria before you begin. Work with an implementation partner who understands the chosen process thoroughly, builds the right integrations and delivers a governance framework. That way you avoid building a solution that requires more maintenance than it saves.

 

 

Curious what agentic AI looks like in your business?

 

The AI Business Brain is our concrete approach: a connected system of AI agents that takes over the repetitive tasks in your business, tailored to your processes and systems. Discover what it looks like for a company like yours.

 

Discover the AI Business Brain