What is agentic AI in simple terms?

Agentic AI is software that is given a goal rather than a single question, and works towards it in several steps: it plans, uses tools such as search, code or business systems, looks at the results, and adjusts until the goal is reached or it needs a human. A chatbot answers; an agent does.

What is the difference between agentic AI and generative AI?

Generative AI produces content in response to a prompt: text, code, images. Agentic AI uses a generative model as its brain but wraps it in a loop with tools, memory and a goal, so it can take actions in the world. Every agent contains a generative model; not every generative model is an agent.

What are examples of AI agents?

Coding agents such as Claude Code and Codex that implement a feature across many files; customer-service agents that resolve tickets by looking up orders and issuing refunds; research agents that search, read and compile a report; and operations agents that triage alerts, open tickets and run runbooks.

How do AI agents work?

An agent runs a loop: read the goal and context, decide the next action, call a tool, observe the result, repeat. The model does the deciding; tools do the acting; instructions, memory and guardrails shape what the model is allowed to decide. Standards such as the Model Context Protocol define how tools are connected.

Are AI agents safe to use in a company?

They can be, with the same controls you would apply to a new employee with system access: least-privilege credentials, approval for irreversible actions, a log of what was done, and a clear list of which tools and data the agent may touch. Most incidents so far came from agents with too much access reading untrusted content.

What is agentic AI? Agents, autonomy and how they differ from generative AI

Agentic AI is the phrase of the year, and like most such phrases it is used for everything from a chatbot with a plugin to a fleet of autonomous systems. This guide gives it a precise meaning, explains how an agent actually works, draws the line against generative AI, chatbots and robotic process automation, and covers what changes when agents get real access to a company's systems.

It is written by a team that runs agents in production inside EU environments, so the second half is practical rather than speculative.

The definition

Agentic AI is AI that is given a goal and works towards it in multiple steps, choosing and using tools along the way, and checking its own progress.

Three words in that sentence do the work.

Goal. You do not ask an agent a question; you give it an outcome. "Fix the failing login test." "Find out why the invoice for customer 4821 is late and draft a reply."
Tools. The agent can act: run code, search the web, query a database, read a ticket, send a message. Without tools it is a chatbot.
Multiple steps with checking. The agent decides what to do, does it, looks at what happened, and decides again. It can recover from a wrong turn without being told.

The word "agentic" is an adjective; "AI agent" is the noun. In practice they refer to the same thing: the system that does the loop.

How an agent works

Strip away the framework and every agent is the same loop:

goal + context  →  model decides next action  →  tool runs  →  result  →  model decides again  →  …  →  done or ask a human

The pieces around that loop are what distinguish a demo from a production system.

The model. A large language model (Claude, GPT, Gemini and others) is the decision-maker. It reads everything in its context and outputs the next action, usually as a structured tool call.

Tools. Functions the model can invoke. Read a file, run a query, open a browser, call an API. The Model Context Protocol has become the standard way to give agents tools; our MCP guide explains it.

Instructions. The system prompt and any task-specific know-how. Increasingly this is packaged as skills: folders of instructions the agent loads when relevant.

Memory. Short-term memory is the context window. Longer-term memory is whatever the agent writes down: notes files, a database, a vector store it can search.

Guardrails. What the agent is allowed to do, which actions need approval, what it must never touch. These live outside the model, in the harness or a gateway, because a rule inside the prompt can be argued out of.

Orchestration. For bigger tasks, one agent delegates to others: a planner spawns a researcher and a coder, collects their results, and reports back. Subagents give isolation and parallelism.

Agentic AI vs generative AI

Generative AI produces content from a prompt. Agentic AI pursues a goal with actions. The relationship is containment: every agent has a generative model at its centre, and the agent adds a loop, tools and memory around it.

	Generative AI	Agentic AI
Input	A prompt	A goal plus access to tools
Output	Text, code, image	Actions and their outcomes, plus a report
Steps	One	Many, with self-correction
Needs	A model	A model, tools, instructions, guardrails
Failure mode	Wrong answer	Wrong action

That last row is why governance matters more for agents. A wrong answer is read by a person before it does damage. A wrong action has already happened.

Agents vs chatbots vs RPA

Chatbots converse. Modern ones use the same models as agents and may have a few tools, but the human drives every step. An agent is handed the wheel.

RPA (robotic process automation) follows a fixed script: click here, copy this, paste there. It is deterministic and brittle; a changed button breaks it. An agent understands the goal and adapts. The two are converging: agents call RPA-style automations as tools, and RPA vendors are adding models to handle exceptions.

Workflows (n8n, Zapier, Make) are graphs of steps a human designed. An agent is a workflow that designs its own steps at runtime. Many production systems are hybrids: a fixed workflow with an agent inside one node where judgment is needed.

Where agents are used today

Software development is the most mature use. Coding agents such as Claude Code and Codex take a task, read the codebase, make changes across files, run tests and open a pull request. This is where most companies first encounter agentic AI, because developers adopt it themselves.

Customer service. Agents that read a ticket, look up the order, check the policy, issue the refund or escalate. The quality is bounded by the knowledge base and the tools they are given.

Research and analysis. Search, read, extract, compile. Due diligence, competitor monitoring, literature review. Agents are good at breadth; humans still own the judgment.

Operations. Triage an alert, gather logs, run the runbook, open the incident. IT and security operations are adopting agents quickly because the steps are well defined and the tools already exist.

Back office. Reconcile invoices, chase missing data, prepare reports. Usually a workflow with an agent for the exceptions.

Levels of autonomy

Not every agent should run unattended. A useful scale:

Suggest. The agent proposes; a human executes. Safe, low value.
Act with approval. The agent executes read-only steps itself and asks before each write. Where most companies should start.
Act within bounds. The agent executes within a policy: these tools, these systems, this budget, this time window. Approval only for exceptions.
Autonomous. The agent runs end to end and reports. Appropriate for well-understood, reversible tasks with a good log.

The right level depends on how reversible the action is and how good the log is, not on how smart the model is.

How to build an agent

You rarely start from scratch. The realistic options:

Use an existing agent. Claude Code, Codex and Cursor for code; the agent features in Claude, ChatGPT and Copilot for knowledge work. Give them tools via MCP and know-how via skills.
Build on an agent SDK. The Claude Agent SDK, the OpenAI Agents SDK, LangGraph and similar frameworks give you the loop, tool handling and subagents. You supply the tools, instructions and guardrails.
Build inside a workflow tool. n8n, Copilot Studio and their peers let you drop an agent node into a fixed workflow. Good for back-office tasks with clear boundaries.

Whatever you build on, the hard parts are the same: which tools the agent gets, how narrow their permissions are, what needs approval, and where the log goes.

Running agents in a company

The questions that decide whether a pilot becomes production:

Access. Agents need credentials to act. Where do they live, who scoped them, how are they revoked? Laptops full of API keys are how it starts and how it goes wrong.

Data residency. The model call and every tool call carry data. For EU companies under GDPR, where those calls are processed is a legal question. Models and tools both need to run in the right region.

Injection. Agents read content others control: tickets, emails, web pages, documents. Instructions hidden in that content can steer the agent. Read-only sessions for untrusted content and approval on writes are the practical defences; see our MCP security guide.

Cost. Agents consume tokens in loops, and a stuck loop can burn a month's budget in an hour. Per-user and per-team budgets with hard caps are not optional.

Audit. "What did the agent do at 14:32?" must have an answer: user, agent, model, tool, arguments, result, approval.

All five are easiest to solve in one place that every agent goes through. That is what an AI gateway is for, and it is what Walma AI Hub does: models, MCP servers and skills behind one policy engine and one log, inside the customer's own Azure tenant in an EU region. If you are moving agents from pilot to production, book a walkthrough.

Where to go next

Model Context Protocol: the complete guide — how agents get tools.
Claude skills explained — how agents get know-how.
What is Claude Code? — the coding agent most teams start with.
What is an MCP gateway? — the control point for agent traffic.