What is Agentic AI?

An AI agent is a system that is given a goal rather than a single instruction. It breaks that goal into steps, chooses the tools it needs, executes them, inspects the result, and iterates until the goal is met or a defined stop condition is hit.

The language model is only one component. The agent additionally needs memory or state, a set of tools (APIs, databases, business systems), a policy for when to act autonomously and when to escalate to a human, and telemetry so every action is traceable.

A retrieval-augmented chatbot retrieves context and generates one answer. It is stateless, read-only and its scope ends with the response. That is a good fit for knowledge access and drafting.

An agentic system is stateful and write-capable. It can create the credit note, update the CRM record, post the journal entry or open the ticket. That capability is where the business value sits — and also why guardrails, permissions and evaluation matter far more than in a chat use case.

Customer service: multichannel agents that resolve routine requests end to end across voice, email and chat, and hand over cleanly with full context when they cannot.

Document and invoice processing: extraction, validation against master data, exception handling and posting into the ERP.

Finance operations: reconciliation and month-end close support, where the agent gathers evidence, matches transactions and prepares the items a human reviewer signs off.

Sales and quoting: configuration, pricing lookups and proposal generation from product and contract data.

Internal knowledge access: enterprise search agents that answer across scattered systems with source citations.

Production agents rarely depend on one model. Orchestration routes tasks to the model that fits the cost and quality profile, while tool access is standardised — increasingly through the Model Context Protocol (MCP), which gives agents a consistent way to reach internal systems without one-off integrations.

Underneath sits the data layer: a lakehouse or well-governed data platform, clear ownership, and interfaces that expose only what an agent is permitted to see.

Because agents act, every action needs an owner, a permission scope and an audit trail. Role-based access, human-in-the-loop for high-impact steps, prompt and output logging, and PII handling under GDPR are baseline requirements.

For EU organisations, the AI Act adds classification, documentation and transparency duties depending on the risk category of the process being automated. Building that into the architecture from day one is far cheaper than retrofitting it.

Agentic systems degrade quietly. Continuous evaluation against a golden dataset, tracking of resolution and escalation rates, latency, and per-task token cost (FinOps for GenAI) is what keeps a pilot from becoming an expensive experiment.

Pick one process with high volume, measurable outcomes and tolerable error cost. Define the target metric before you build. Ship a narrow agent with tight tool scope, measure it against the human baseline, then widen scope once quality holds.