AI Agents in one minute
A chatbot can explain how to check a catalogue. An assistant with tools can open the catalogue file. An agent can then run several checks, investigate missing information and continue until it reaches an agreed result or stopping point. All three may appear inside a chat interface; the interface alone does not reveal how the work is controlled.
AI Agents start with a useful distinction
Question → answer. For example, explain which fields a product description should contain.
Question → search, calculate or inspect a file. A connected tool makes an actual operation possible.
Goal → choose steps → use tools → check results → continue or stop.
These are practical distinctions, not a universal classification of products. A chatbot may also use tools, and one application may handle a routine task with fixed rules while approaching another more flexibly. The useful question is who determines the next step: predefined software logic, a fresh user instruction or a model operating within limits.
An LLM processes language and can propose an appropriate action. Tool Use makes operations available through defined functions. The surrounding software validates inputs, enforces permissions and executes requests. The model does not automatically acquire access to a shop, mailbox or accounting system. Without connected tools, a description of an action remains text.
An agent need not produce its entire plan as a long document before starting. It can select an initial step, examine the outcome and adjust its approach. This flexibility helps when the task depends on what is discovered. Checking whether a fixed list of product fields is empty, however, often needs only ordinary rules rather than a new model decision for every row.
Three requests from an ordinary working day
Consider this request: review 400 products and identify incomplete records. First define completeness: title, product identifier, dimensions, price or an image may be required. An empty field is different from an explicit 'not applicable', and an unknown dimension must not be invented. The system also needs to establish that it has obtained all 400 records, including any additional result pages.
Simple checks can flag empty fields. The agent may investigate less obvious cases, such as a description referring to a different variant or conflicting units in two sources. It should return a table with product identifiers, the missing or conflicting data and the evidence. Preparing that table does not automatically authorise writing new values into the shop.
For a folder of invoices, the task could be to prepare a traceable spreadsheet. Tools retrieve the authorised files and extract supplier, date, invoice number and amounts. The workflow checks totals and flags duplicate numbers, unreadable pages and possible credit notes. The result should link each row to its source. Preparing an analysis is separate from approving payments or making accounting decisions.
A third request is to check a condition periodically and notify someone when it becomes true. This requires a real scheduler, a persistent task definition and an available delivery channel. A conversational promise does not create background execution. Specify frequency, time zone, what counts as a match and how repeated notifications are prevented. A failed check must not be reported as a negative finding.
- Define the assignment
Identify the records, success criteria, allowed actions and stopping conditions.
- Execute and check
Use tools, inspect their actual results and handle missing or conflicting evidence.
- Deliver or request a decision
Provide the agreed output and obtain review before actions outside the authorised scope.
In all three examples, the agent's value comes from coordinating the work and handling variation. It is not a reason to give it every available permission. Connectors can supply access to the relevant service. MCP is one protocol for exposing tools and context. If an application lacks a suitable API, Computer Use may make a limited interface-based route possible.
Agents and automation can work together
| Question | Fixed automation | Agent-based behaviour |
|---|---|---|
| What happens next? | A predefined rule selects the next step. | A model can choose a step from the available possibilities. |
| Where does it help? | Stable, repeated operations with clear conditions. | Tasks with variation that needs interpretation or investigation. |
| What sets boundaries? | Code, permissions and configured conditions. | Those controls plus limits on model-selected actions. |
| What proves completion? | A checked result in the target system. | The same: fluent narration is insufficient. |
Automation describes work being performed with reduced manual intervention. It does not require a language model. A scheduled database check can be entirely deterministic. Conversely, an agent may work only while a person supervises an interactive session. An Agentic Workflow can combine fixed checks with flexible investigation, using each where it serves the task.
Autonomy is a design choice, not an unlimited permission. An agent can be allowed to read and compare independently while preparing proposed changes for approval. Limits can cover accounts, folders, operations, time, cost and the number of attempts. When the goal cannot be established within those limits, the correct outcome may be a clear request for help.
AI Memory can retain progress between steps or sessions. The Context Window contains the information made available to the model during a particular interaction. Neither one makes stored information automatically correct or current. An old price or an unapproved draft must not become a confirmed fact simply because it was remembered.
Human-in-the-loop gives a person a meaningful decision at the appropriate point. Show the concrete proposed change, its source and its consequences before an important action. Approval should apply to the version reviewed. If the proposal changes materially, an earlier approval should not silently cover the new action.
For SEO Bureau Eindhoven, the practical aim is better preparation and more consistent checking while retaining professional judgement. Evaluate an agent by completed, verified tasks, useful exception handling and corrections needed. A long sequence of tool calls or an impressive explanation is not a substitute for delivering the result the business actually requested.