Six common types of AI agents and concrete examples for businesses by Andreas Welsch.


Copyright: – “The Next Wave In Generative AI Deployment: AI Agents”


SwissCognitive_Logo_RGBThe last few weeks have brought several innovations in foundation models, including announcements from OpenAI, Google, and Anthropic. What most of the coverage has been missing: the bigger picture. Yes, these models are another leap forward, especially those that are multi-modal such as OpenAI GPT-4o and Google Gemini. But it’s not about building better chatbots.

It’s rather the answer to “What’s next in Generative AI?” After the initial scenarios, such as generating, summarizing, and translating text (and other types of media), are implemented, the next level of capabilities is just around the corner. And along comes the next level of productivity gains.

It’s just that this time, it won’t be automating clicks (Robotic Process Automation), individual approval steps in a process (Machine Learning), or language tasks (large language models). This next phase is all about using agents to automate problems with limited uncertainty and complexity. So, let’s jump in…

Six Common Types of AI Agents

Agents are software components that can make decisions under uncertainty based on defined objectives and interact with their environment. Agents have existed for decades.

For example, the thermostat in your house is an agent. A sensor measures the current room temperature and if that temperature is outside of a defined threshold during the next measurement (e.g. colder than what you have set it to), the thermostat fires up your heating until the target temperature is reached.

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But Generative AI adds unique, new abilities to agents: they use Generative AI models to understand an abstract goal, divide it into subgoals, evaluate possible options for achieving these subgoals, and execute the steps necessary to do so.

Agents are built into applications and available as stand-alone extensible frameworks.[…]

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