The spectrum of AI agents is vast, encompassing a range of complexities, functionalities, and levels of autonomy. Understanding the nuances of different AI agents is crucial for harnessing their full potential.
Types of AI Agents
AI agents can be broadly categorized based on their complexity and autonomy. From simple rule-based systems to advanced autonomous agents, each type offers distinct advantages and applications.
- Reactive agents are simple — almost mechanical in way they operate. Think of a thermostat; they (usually) control the heating and air conditioning in a building based on a single data point: air temperature. Reactive agents use rules, which must be defined in advance by the development team, to respond to the data they are presented with. They react immediately to certain conditions and don’t remember previous actions or results. They are perfect for many scenarios, but are not useful when conditions change in any fundamental way. Reactive agents find many uses in manufacturing, for example, where systems must perform the same or similar tasks repeatedly. They are also useful in many financial services use cases; for instance, triggering a fraud alert when a debit card transaction exceeds a preset threshold.
- Deliberative agents (also called goal-oriented agents) are a bit more complex. They make decisions supported by reasoning processes grounded in digital models of their environments. They can assess long-term pros and cons for different choices before acting. Deliberative agents find use in applications like robotics where mobile units must move through a changing environment deal with unexpected challenges. For example, an aircraft autopilot system must be able to change the vector, speed, and/or altitude in response to changes in wind speed and other factors.
- Hybrid agents build on the capabilities of deliberative agents by incorporating some features of reactive agents. They’re super-fast while also considering problems that may occur on longer time horizons. Hybrid agents are very efficient and flexible; they are especially useful in smart devices that can do simple tasks like tell you how the stock market is doing today along with more complicated chores like reminding you to take medication at a certain time or change the air conditioning setting when a building is empty.
- Learning agents go much further and can learn new behaviors based on data received from their environments in the past. AI algorithms, commonly including reinforcement and supervised learning models, allow learning agents to identify patterns and make predictions; they use that information automatically to fine tune their responses. Learning agents can adapt to changing conditions and are a good fit for situations where new and potentially unexpected data appears regularly. Recommendation engines for movie apps typically use learning agents, as do programmed trading systems used by securities firms to automate certain activities.
- Autonomous agents operate independently and do not need (too much) supervision by human operators. They can take stock of current conditions and decide the optimal way to proceed based on their programmed objectives. Factory robots that move finished parts from a production area to shipping often use autonomous agents.
- Social agents are getting a lot of attention these days because of the ways they can interact with people. They can deduce the emotions a person is feeling and generate emphatic responses and have human-like interactions. Think of the famous Turing test. They use natural language processing (NLP) technology and large language models (LLMs) to process inputs and generate outputs that ordinary people can understand. Social agents, properly implemented, can be extremely useful in building effective customer service chatbots.
Choosing the best type of AI agent based on the task at hand is critical to successful user acceptance and trust. Obviously, no one will be happy with a warehouse robot implementation using only simple reactive agents. The challenges are too complex. Likewise, building out an autonomous agent to handle a relatively simple level-setting problem is overkill; it will take too much time to build, test, and deploy and will not perform demonstrably better than something much simpler.
Approached in the right way, AI agents can enhance operational efficiency, streamline and automate routine tasks, support improved organization decision-making, personalize user (and customer) interactions with the organization’s data and systems, improve security, and provide companies with substantial competitive advantages.
Click to learn more: altair.com/ai-agents.
This blog post is a contribution from Altair, world leader in computational intelligence, for the readers of Data Expo. You can find more inspiration at altair.com/knowledge-graphs or visit Altair during Data Expo at booth #21.
