Agentic AI is software built around a language model that is given a goal, then decides for itself which steps and tools to use to reach it, checking the results as it goes. An AI agent is one working example: a program that can read information, choose an action (look something up, draft a reply, update a record), carry it out, and decide what to do next. If you want the commercial version, our custom AI agents service covers what we build.
What are AI agents?
An AI agent has four parts. A model (such as Claude or GPT) does the reasoning. A set of tools lets it act: search your documents, query a database, send an email, create a ticket. Memory or context tells it what has happened so far. And a goal or instruction says what “done” looks like.
The difference from older software is who chooses the path. In a normal program a developer writes every branch in advance. In an agent, the model picks the next step based on what it finds. That makes agents good at messy work where inputs vary, and worse at work where every step must be identical.
What is agentic AI, and how is it different from “AI agents”?
In practice people use the two terms loosely. “Agentic” describes the behaviour: acting towards a goal over several steps with some autonomy. “AI agent” names the thing that does it. A more careful reading of ai agents vs agentic ai: agentic AI is the broader approach (including systems where several agents cooperate, or where one workflow has agentic steps inside it), while an agent is a single actor within it. Nobody will mark you down for mixing them up, but when you scope a project, describe the behaviour you want rather than the label.
How is an AI agent different from a chatbot or a fixed automation?
- A chatbot answers questions in a conversation. It usually does not take actions in your systems. We cover this in more depth in AI agents vs chatbots.
- A fixed automation (a Zapier or n8n flow, an RPA bot) follows steps you defined: when X happens, do Y, then Z. It is cheap, fast and predictable, and it fails when the input does not fit the pattern.
- An agent receives a goal and works out the steps. It can handle an email it has never seen before, but it can also get things wrong in ways a fixed flow never would.
Many good systems mix them: a fixed workflow handles the predictable parts, and an agent handles one step that needs judgement, such as reading a free-text request and deciding what it is.
What does an AI agent look like in a business? An example
Take a shared support inbox. Customers write in English and Maltese, about orders, invoices, bookings and complaints. A fixed rule can sort by keyword, but it misses most real messages.
An agent for this job could read each email, classify it, look up the customer in your CRM, check order status in your system, and draft a reply for a team member to approve. It would route complaints and anything it is unsure about straight to a person. This is the idea behind an AI email assistant: it does the reading, lookup and first draft, and a human stays accountable for what is sent. Our catalogue has related jobs such as channel-based ticket routing and FAQ and knowledge base answer drafting.
How to build an AI agent
You do not need to start with a framework. The sequence that works:
- Pick one narrow job. “Triage the support inbox” is a job. “Run customer service” is not.
- Write down what a good result looks like, including examples of tricky cases and what should happen to them.
- List the tools it needs, and give it the minimum access to each. Read-only where possible.
- Build the loop: model, instructions, tools, and a way to record every step.
- Test on real past cases before it touches live work, and keep a set of those cases to re-run whenever you change something.
- Start with a person approving the output, then relax that only where the record shows it is reliable.
AI agent builders, platforms and frameworks
Options range from visual builders to code. Workflow tools such as n8n include an AI agent step, so you can wire a model to tools inside a workflow you can see. Developer frameworks (LangChain and LangGraph, and the agent SDKs offered by the model providers) give more control and take more engineering. Cloud platforms from Microsoft, Google and AWS sell hosted agent services. No single choice is correct. For a first agent with a handful of tools, a workflow builder is often enough. Choose code when you need tight control over behaviour, testing, or where data lives. Our comparison of n8n, Make and Zapier goes into that trade-off.
Controls: permissions, approval and logging
An agent that can act can also act wrongly, so build the controls first.
- Permissions. Give it its own account with the narrowest access that works. If it only needs to read invoices, it should not be able to edit them.
- Human approval. Require sign-off for anything irreversible or external: sending to customers, payments, deleting data. Auto-approve only low-risk steps.
- Logging. Record the input, each tool call, the output and who approved it. When something goes wrong you need to see why.
- Limits. Cap spend, number of steps and retries so a loop cannot run away.
- Data handling. If personal data passes through, GDPR applies. See our note on GDPR and AI automation in Malta, and confirm specifics with your adviser.
When not to use an agent
Skip the agent when the process is the same every time (use a plain automation), when a wrong answer is costly and there is no human check, when you cannot describe what “correct” means, or when volume is so low that a person is simpler. A good test: if you could write the steps as a flowchart without any “it depends”, you do not need an agent. For the model side of this decision, see our LLM implementation guide.
Frequently asked questions
What is agentic AI in simple terms?
It is AI that works towards a goal by choosing its own steps and tools, rather than only answering a question. It acts, checks the result, and carries on.
What are AI agents used for in business?
Common uses are triaging inboxes, answering customer questions from company documents, preparing reports, checking documents against rules, and keeping records in sync. The best ones have a narrow scope and a person reviewing the output.
How do I build an AI agent?
Define one narrow job, list the tools and access it needs, build the loop with logging, test on real past cases, and start with human approval. A workflow tool or a developer framework can both work.
Are AI agents safe to use with customer data?
They can be, with limited permissions, approval steps, logs and a clear data-handling basis under GDPR. Treat the agent like a new junior employee: restricted access and supervised at first.
Where Automata fits
We build custom AI agents for specific jobs: inbox triage, document handling, data checks and internal assistants, with permissions, approval steps and logs designed in from the start. If your job is narrower and well defined, one of our ready-made automations may be cheaper, such as email thread logging to your CRM.
If you have a process in mind, get in touch and we will tell you plainly whether it needs an agent or something simpler.