Practical guide

Agentic AI: from answers to useful actions.

An agentic AI system uses a model to choose steps or tools toward a goal. Its usefulness depends on the workflow, the boundaries around its actions and how its results are checked.

What is the difference between a chatbot and an agent?

A chatbot is a conversational interface. It may answer questions or retrieve information. An agentic system can also decide which tools to use and what to do next. The distinction is about how the work is controlled, not whether the product has a chat window.

For example, a workplace assistant might answer a policy question. A more agentic workflow could identify missing information, prepare a request and route it to the correct reviewer. This is an illustrative use case, not a claim about a deployed product.

When is a simpler automation better?

If the same input should always trigger the same known steps, start with ordinary automation. Adding model-driven decisions introduces variability and more evaluation work. Use an agent when choosing between steps creates value that a fixed workflow cannot easily provide.

Five decisions before a pilot

Define the task. Describe one bounded result, who needs it and what counts as a correct outcome.
Limit access. Identify the information and tools required. Separate read access from permission to change records or send messages.
Choose approval points. Require review for consequential actions such as payments, sensitive record changes or external communications.
Prepare evaluations. Test realistic cases, incomplete inputs, tool failures and misleading instructions inside retrieved documents.
Assign an owner. Decide who reviews failures, maintains the workflow and can stop it.

How should you measure usefulness?

Compare the pilot against the existing process using the same representative tasks. Record completion quality, time spent including human review, corrections, cost per completed task and cases requiring escalation. Choose acceptance criteria before seeing the results.

A fast answer that needs extensive checking may not improve the workflow. A smaller system that reliably prepares work for review can be a better first step than broad autonomy.

Do you need your own GPUs?

Not necessarily. Using a hosted model API generally avoids running model inference on your own hardware. Dedicated GPU infrastructure is a separate decision involving model requirements, data constraints, workload, cost and operational capability.