 "AI Agents: What are they and why technology industry is excited about them")
Artificial Intelligence (AI) agents is set to be the major evolution in AI technology, building on the advancements of generative AI. Leading companies like Google and OpenAI are already showcasing sophisticated capabilities, introducing what they describe as an agentic AI experience. But what exactly are AI agents, and how do they differ from traditional AI-powered chatbots?
What are AI agents
AI agents are advanced software tools designed to perform complex, multi-step tasks for users with minimal supervision. These systems are autonomous and proficient at managing repetitive activities that would otherwise demand manual effort. In addition to natural language processing, AI agents possess the ability to make decisions, address problems, and engage with their surroundings to carry out specific actions.
How AI agents work
AI agents interact with their environment using textual, visual, and auditory inputs. They gather data and use it to autonomously perform tasks aimed at achieving predefined objectives. While the user typically defines the goal, the AI agent independently determines the most effective actions to accomplish it.
These actions are guided by the agent’s training data and insights gained from prior experiences. The agent deconstructs the main goal into smaller, actionable tasks and executes them sequentially. After completing each task, it moves on to the next step.
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Between tasks, the agent reviews its progress by analyzing its logs to assess whether it has met the objective. If necessary, it may generate and execute additional tasks to ensure the desired outcome is achieved.
How are AI agents different from traditional AI
AI agents and AI-powered chatbots both leverage generative AI, large language models (LLMs), and natural language processing (NLP). Unlike traditional AI chatbots, which depend solely on their training data, AI agents retain past interactions in memory and use this dynamic information to plan and execute future actions. This capability enables AI agents to address complex queries and take actions autonomously on behalf of users. In contrast, AI chatbots function more as reactive digital assistants, primarily restricted to completing straightforward tasks based on user prompts.
Some examples of AI agents
Reasoning AI models, like OpenAI's o1-series and Google's Gemini 2.0 Flash Thinking models, exhibit agentic capabilities. These models process complex user queries by breaking them into smaller steps and reasoning through each one to arrive at a solution.
Google's Deep Research tool is another example of this capability, functioning as an advanced research assistant. When given a user prompt, it formulates a detailed multi-step research plan, gathers relevant information from the web, refines its findings through iterative searches, and delivers a thorough report.
Benefits of AI agents
AI agents streamline operations by automating repetitive tasks and supporting routine decision-making. They leverage data to personalize interactions, enhancing user experiences. For businesses, they boost productivity by optimizing resource utilization and facilitate more informed decision-making.