AI agents are programs designed to work autonomously to achieve certain goals. They are trained on sophisticated machine learning or deep learning algorithms and are able to affect their environment.
This capability is useful for a diverse range of tasks, from robotics and autonomous vehicles to chat applications. Although the agent’s purpose is predetermined by design, the way they are trained gives them the capacity to respond to external stimuli and react appropriately.
A piece of software designed to “learn” from and react to its environment to solve problems on its own.
AI agents can be used to complete tasks and solve problems quickly, freeing up time for more complex work.
AI agents can’t be applied to every single problem and are also opaque in how they work, offering little transparency in how a task is done.
AI agents can be connected to corporate systems and tools to automate certain tasks and increase efficiency.
What are AI agents?
AI agents are pieces of software that are developed in such a way that they can learn and adapt to their environment. This is what separates AI agents from AI models. A model cannot affect its environment independently and cannot (by itself) perform a series of tasks in an order it decides. (An AI agent might well include more than one model.) They are a powerful tool for automating tasks, offering helpful suggestions and making sense of vast amounts of data.
What’s in it for you?
AI agents offer a range of benefits for businesses. These include:
Increased efficiency: AI agents excel at handling repetitive tasks like scheduling meetings, generating reports, or processing customer inquiries. This can free up time for more strategic and value-adding work.
Analytics support: AI agents can analyze massive amounts of data to identify trends, predict outcomes and uncover new insights that could inform strategy or product development.
Personalization: AI agents can personalize experiences. This can aid everything from personal productivity to marketing recommendations.
Ultimately, by automating tasks and improving efficiency, AI agents can lead to significant cost savings for businesses. This can free up resources for investment in other areas.
What are the trade-offs of AI agents?
AI agents pose a number of challenges. These include:
Development and implementation costs: Creating and integrating AI agents can be expensive, requiring investment in technology and expertise, though the cost is expected to decrease in the next few years.
Limited creativity and critical thinking: AI agents aren’t great at tasks that require the creativity, empathy or complex problem-solving that’s often needed in human interactions.
Security and privacy risks: AI agents that handle sensitive data may threaten privacy and create security vulnerabilities — these need to be properly monitored and managed.
Explainability and trust: It can be challenging to understand how AI agents reach certain decisions especially at a level regulators would like to see. Adoption is slowed by lack of trust from the humans who have to interact with the agent.
Bias and fairness: AI agents trained on biased data will perpetuate those biases in their decisions and recommendations.
Hallucinations
There are two things that can be done to tackle these issues: carefully consider where AI agents are applied and deployed and ensure there is a human-in-the-loop to monitor them.
How are AI agents being used?
AI agents can be found in a number of high-profile and popular products, including virtual assistants like Siri and Alexa. Here, AI agents use voice recognition to understand requests and complete tasks, answer questions and control smart devices.
More broadly, AI agents can unleash a new era of efficiency. They can, for instance, be connected with corporate systems and real-world APIs to automate certain tasks. used AI agents in a project with a telecoms company to automate parts of the customer support process, in which fully autonomous AI agents were used to identify customer problems and solve them in the background.
In some contexts fully autonomous agents might be inappropriate; a mixed approach might be to get customer service reps to identify problems with the AI agent suggesting potential solutions, making the reps work easier and solving the customer problem much more quickly.
