Category: Automation

No-code tools can shorten the path to a working process. However, it is crucial to determine who is responsible for data, errors, and integration maintenance.

A knowledge assistant needs organized sources, access control, and updates. Simply adding a language model does not solve the search problem.

The choice of tasks for AI should consider risk and the possibility of verifying the result. Practical criteria for code, content, and automation.

Not every screen should disappear. When does a conversation help accomplish a task and when does a table, form, or change preview provide better control?

Code generated by a model requires the same standards as the rest of the code. Key review areas: permissions, inputs, and side effects.

The user interface and integration require different access points. How to combine content publishing with controlled data sharing.

Extending the site with integrations expands the area that requires protection. How to prioritize and limit the consequences of incorrect or unauthorized requests.

Data control, maintenance costs, and infrastructure requirements influence the choice of solution. Questions to ask before making a decision.

Unnecessary API calls, duplicate tasks, and lack of measurement can increase expenses. How to look for costs resulting from architecture.