Agents

Description of the agents, how they work and the best and worst use cases

Agents - General Information

Agents are specialists in their field. Based on generative AI (LLM), they respond individually and dynamically to incoming user queries. In contrast to the answers provided by intents, the agents' answers are not generic and repetitive, but react dynamically and individually to a user query. 
While the answer of a intent is based on the previously created answer content, selected resources form the knowledge of the agents.
The agent is activated via the 3-dot menu in the answer editor. Clicking on the Create agent button opens the option to change the intent.

Agents: Best and worst use-cases

Agents are primarily used to reduce the editorial effort involved in creating responses. They are therefore generally suitable in areas where a high editorial effort meets a low query volume (so-called long-tail queries). For intents with a higher volume of queries, an evaluation of the response documentation is necessary beforehand. If the documentation is good, the agents are also suitable here. 
The agents are also suitable where the answers are very repetitive and good documentation exists at the same time. 
However, the agents are unsuitable where the answers are dynamic and based on complex documentation, e.g. questions about individual product key figures. 
Agents are also unsuitable if the answer contains sensitive information, e.g. dosages of dietary supplements or information on allergens. 
Finally, an agent is not recommended if only a small amount of editorial work is required to create an answer.