What Is Agentic AI? Definition, Capabilities, and Business Applications

Agentic AI refers to AI systems that operate in a loop: perceive → reason → act, with the ability to pursue goals across multiple steps. You’ll also see this described as a perception–reasoning–action (PRA) loop.

A non-agentic model can answer questions. An agentic system can complete tasks: it can look up the order, check the policy, draft the reply, update the ticket, and verify the outcome.

In practice, agentic AI is not “one model.” It’s the model plus the surrounding system: tool connectors, state, permissions, and evaluation. That surrounding system is what turns a probabilistic text generator into controlled automation.

When we explain it to Dutch teams, we use a simple test: can it finish the task without a human clicking through dashboards? If it cannot, it’s a copilot or chatbot. If it can, and it does so with guardrails and logs, it’s agentic.

Agentic AI is also about accountability. Once an AI can act, you must be able to answer: what did it do, why, with what data, and who approved it.

Properties of an agentic system (what we look for in production)

When we call something agentic, we expect these properties:

• Goal-directed behaviour: the system can state what it is trying to achieve and when it is done.


• Tool-mediated action: it can call tools (APIs, databases, workflows) rather than only generating text.


• Step-by-step execution: it can sequence work across multiple steps.


• Feedback and correction: it checks outcomes and adjusts when something fails.


• Boundaries: it knows what it is not allowed to do and escalates.

Without these properties, “agentic” becomes marketing. With them, you can build automation that behaves consistently even when inputs vary.

Completion criteria (how agents know they’re done)

A subtle difference between agentic and non-agentic systems is that agentic systems need a stop condition. In production we require agents to state success criteria: “ticket status set to resolved”, “VAT totals updated and exception list produced”, “return label created and logged”. Without that, agents tend to loop or produce busywork.

This is also why we prefer structured outputs: the agent reports what it did in a form the system can verify.

The easiest way to understand agentic AI is to compare it to the systems most people already use.

Non-agentic AI (assistants / copilots)

• Produces suggestions: drafts, summaries, answers


• Depends on a human to execute actions


• Usually has no persistent state beyond the conversation

ChatGPT used as a writing assistant is non-agentic. A “copilot” in a helpdesk that drafts replies but doesn’t send them is mostly non-agentic.

Agentic AI (agents)

• Has a goal and decomposes it into steps


• Calls tools (APIs, databases, workflows) to execute steps


• Verifies outcomes and escalates when uncertain


• Keeps state across steps (case ID, extracted fields, decision history)

Our Fiscal Agent is a concrete example: it imports bank transactions via PSD2, classifies them, checks for receipts, updates quarter-to-date VAT totals, and produces an exception list for review. That’s not “a good answer.” It’s a completed bookkeeping workflow with an audit trail.

The spectrum (most real systems live here)

Autonomy is not binary. We often ship systems in stages:

1) Read-only: the agent fetches context and suggests.
2) Draft: the agent prepares actions (reply, booking) but a human approves.
3) Execute: the agent performs reversible actions automatically.
4) Approve-to-execute for high risk: money and account changes require explicit approval.

This staged approach is how you get the productivity benefits without gambling with trust.

Chatbot vs copilot vs agent (a practical comparison)

Here’s a quick table we use to clarify expectations:

A copilot can already be valuable. Many Dutch teams get ROI from “draft-only” support agents because it reduces writing time while keeping humans in control. The moment you want the system to update a record, categorise a transaction, or trigger a workflow, you need agentic design.

We also distinguish agentic AI from classic automation like RPA. RPA repeats deterministic steps. Agentic AI can interpret messy inputs and decide the next step, which is why it’s useful for emails, invoices, and exception-heavy processes.

Autonomy ladder in practice (how we roll out safely)

When a Dutch team asks for “full automation,” we translate that into a staged rollout:

• Phase 1: observe — classify and summarise, no writes.


• Phase 2: draft — propose actions for approval (draft replies, draft bookings).


• Phase 3: execute reversible — safe updates that can be undone (tags, statuses, attachments).


• Phase 4: execute with approvals — money, accounts, and statutory filings require explicit approval.

This ladder turns “trust” into measurable outcomes: you can track how often drafts are accepted, how often exceptions occur, and where guardrails need tightening.

Agentic AI works when a few capabilities are designed deliberately. In our projects, these are the ones that matter most.

1) Planning (task decomposition)

The agent needs to break a goal into steps: fetch data, apply rules, execute, verify. Planning can be simple (a fixed checklist) or dynamic (choose steps based on the case).

2) Tool use (safe action execution)

Tool use is what separates “talking” from “doing.” But it must be constrained. We treat tool calls like contracts: validated inputs, known outputs, and explicit permissions.

3) Memory and state

Short-term context is the case itself. Long-term memory is pattern learning with feedback (vendor categories, customer tier rules). The system should store IDs and structured fields, not raw personal data duplicated everywhere.

4) Verification and self-correction

Agents should verify outcomes. After an update, re-fetch the record and check invariants. If the data is missing or inconsistent, the correct behaviour is to ask for missing info or escalate—not to guess.

5) Guardrails and approvals

Bounded autonomy is the point. Read-only by default, reversible writes when safe, approvals for anything that moves money or changes customer accounts.

6) Observability and evaluation

In production, you need logs (tool calls and decisions) and metrics (exception rate, correction rate, time saved). This is what lets you improve the system without relying on anecdotes.

Planning is not just “reasoning”

In production, planning should end in a structured plan that can be validated. We often require a plan to specify: which tools will be called, what data is needed, and what success looks like.

Tool use needs permissions

A common failure mode is giving an agent broad access “to be helpful.” We do the opposite: start with read-only scopes, then add the smallest write scopes needed for reversible actions. Refunds, payments, and account changes are always approval-gated.

Memory should be deliberate

We separate:

• Case state: what is true right now for this ticket/transaction (IDs, status, extracted fields).


• Learned patterns: vendor rules, customer tier rules, policy exceptions—updated via feedback, not silently.

Self-correction needs verification

If an agent sends an email or updates a record, it should re-fetch and verify the result. This is where you catch race conditions, permission errors, or incorrect assumptions.

Evaluation is a capability

We treat evaluation as part of the system: regression cases, sampling, and metrics like exception rate and correction rate. Without evaluation, agentic AI drifts as tools and data change.

Capability checklist (what to ask a vendor or your own team)

When you evaluate an agentic system, ask these concrete questions:

• What tools can it call, and what are the permission scopes per tool?


• Does it produce structured actions that you can validate?


• What happens when data is missing (no receipt, no order number)?


• Can it show an audit log of every tool call and decision?


• How do you update policies and prevent regressions?

If the answers are vague, you’re not looking at a production agentic system—you’re looking at a demo.

Agentic AI is most valuable where work is repetitive but still requires context. Here are areas where we see it working in 2026.

Finance and bookkeeping

• Import transactions via PSD2, categorise, match receipts, maintain BTW totals (21%/9%), and produce exception lists


• Prepare VAT overviews for quarterly deadlines (30 April, 31 July, 31 October, 31 January)

Customer service

• Triage tickets, fetch order context, draft replies, and prepare reversible actions


• Escalate complaints, refunds, and GDPR requests with a clear summary

IT operations

• Triage alerts, look up runbooks, propose changes, and open tickets with context

Healthcare, legal, real estate

These domains can benefit from agentic workflows, but the risk is higher. We treat them as human-led with AI augmentation unless governance and evidence requirements are clearly defined.

The common thread is not the industry—it’s the workflow: multi-step work across tools with measurable outcomes.

Dutch examples (where we see fast ROI)

• BTW preparation: an agent can keep quarter-to-date totals and surface missing receipts before the deadline. VAT rates are stable (21% and 9% in most cases), and deadlines are predictable.


• KOR tracking: the KOR threshold is €20,000 annual turnover. An agent can track turnover and warn when you approach the limit.


• GDPR requests: support teams receive access/deletion requests. An agent can route them correctly and prepare a summary, but a human should approve the response.

The common success factor is verification. If the system can check the order status, receipt evidence, or policy rule, it can act safely. If it can’t, it should ask for missing information or escalate.

More application examples (and where we set boundaries)

• Sales ops: qualify inbound leads, enrich with company data, create CRM records, and schedule follow-ups—while avoiding spam and respecting opt-out rules.


• Back-office operations: route invoices for approval, chase missing PO numbers, and update ERP statuses.


• Internal IT: reset access requests, provision accounts, and triage incidents—always with strong authentication and logs.

For sensitive domains (healthcare, legal), we keep execution permissions narrow and focus on drafting, summarisation, and evidence collection. The safety boundary is part of the product.

Agentic AI is real, but it’s not free. The work is mostly engineering and governance, not prompts.

You’ll see surveys and reports that claim numbers like “35% of organisations already use AI agents broadly” and “68% plan integration by 2026.” The exact percentages vary by survey, but the direction is consistent: companies are moving from experimentation to deployment.

The limiting factor is rarely model quality. The limiting factor is the unglamorous work:

• Integrations and permissions


• Data quality and systems of record


• Logging and audit trails


• Clear policies for exceptions and approvals

In our client work, the fastest wins happen when a workflow is already understood and measured. The agent then removes repetitive steps. If the workflow is not understood, agentic AI forces the team to clarify it—which is still valuable, but not instant ROI.

What the adoption numbers miss

Even when surveys say “X% plan to adopt,” the hard part is not deciding to adopt. The hard part is operationalising: integrating systems, defining policies, and building an audit trail that your security team accepts.

In our implementations, the timeline often looks like this:

• Week 1–2: pick one narrow workflow, connect the minimum tools, run read-only + draft.


• Week 3–4: tighten guardrails based on exception cases, add verification steps, introduce limited execution for reversible actions.


• Month 2+: expand to adjacent workflows, add more tools, and formalise evaluation.

This is why agentic AI is real: you can ship value incrementally. But it’s also why “agentic” is not a weekend project if you care about reliability.

Why “80% is unglamorous” is a useful mental model

Most agentic AI effort is spent on things that don’t look like AI: cleaning data, defining what counts as correct, building integrations, and writing approval policies. That work is also what makes the automation durable.

If you want a realistic success metric, don’t ask “does it sound smart?” Ask: does it reduce cycle time, reduce errors, and produce an audit trail that your finance or security team accepts?

Agentic systems can create value quickly, but the failure modes are different from chatbots. The risks are mostly operational.

Common risks:

• Hallucinated facts (“refund processed” when it wasn’t)


• Prompt injection through untrusted content (emails, tickets)


• Data oversharing (fetching or exposing more personal data than needed)


• Wrong actions (wrong record updated, wrong booking)

Guardrails we implement:

• Least-privilege tool access


• Structured outputs and validation


• Verification steps and rollback/compensation paths


• Human approvals for money and irreversible actions


• Audit logs that tie every action to a case ID

For European businesses, governance is not optional. GDPR/AVG sets strict expectations for data minimisation and accountability (with fines up to €20 million or 4% of global annual turnover). The EU AI Act adds another layer, with high penalties for prohibited practices. The practical response is simple: design bounded autonomy, log everything, and keep humans responsible for high-impact decisions.

Operational guardrails (what makes agentic AI safe)

We treat guardrails as layered controls:

1) Tool-level permissions: the agent cannot do what it cannot call. Start read-only, then add narrow write scopes.
2) Policy checks: deterministic rules validate requests (for example: refund limits, required evidence, customer identity).
3) Verification: after an action, re-fetch the record and check invariants.
4) Human approvals: money, contracts, and account changes are approval-gated.
5) Monitoring: track exception rate, rollback rate, and “draft accepted without edits”.

Data governance (GDPR/AVG in practice)

For European businesses, the question is not “is the model good?” It’s “is the data flow defensible?” We minimise personal data in prompts, keep EU-only processing where required, and log tool access. GDPR administrative fines can reach €20 million or 4% of global annual turnover.

EU AI Act (what changes)

The EU AI Act introduces additional compliance expectations and significant fines (up to €35 million or 7% of global annual turnover for the most severe categories). You don’t need to memorise every article to act responsibly. You do need a governance posture: clear documentation, risk assessment, and controls for high-impact use cases.

The practical takeaway: agentic AI is safest when it is bounded, logged, and designed to escalate.