Sixty-one percent of large U.S. firms said they plan to use AI to automate traditionally human-based tasks, according to a recent CNN report. From paying suppliers to invoicing, financial reporting and more, tasks are being delegated to AI to cut costs and increase productivity across business sectors. Using AI is a no-brainer.<\/p>\n
But it\u2019s time to think bigger.<\/p>\n
Mastering AI agent frameworks is the next step to seeing the returns on AI, and it promises to transform how businesses operate by integrating tasks and processes to achieve efficiency and savings.<\/strong><\/p>\n To stay ahead in the AI arms race, forward-thinking companies should start planning and building their AI agent frameworks now. While the process may be challenging, the rewards \u2014 in terms of productivity gains, cost savings and enhanced capabilities \u2014 will likely be substantial.<\/p>\n AI agents are essentially large language models<\/a> (LLMs) equipped with tools to take on specific roles and autonomously make decisions. Although AI agents are “role-based,” they are not chatbots \u2014 not even close. These AI agents go beyond simple linear chatbot-style interfaces to perform complex, iterative jobs from start to finish.<\/p>\n Also known as “agentic systems” or compound AI systems, they\u2019re unique in their ability to make reasoned decisions \u2014 as in, they use reasoning \u2014 without human intervention. They can consume and analyze information and develop action plans to work toward their objective.<\/p>\n Rather than prompting to get to an answer, as with other AI tools<\/a>, AI agents are created to be autonomous entities operating on specific instructions.<\/strong> They can range from simpler rule-based systems to complex machine-learning models, but all AI agents have:<\/p>\n Think of AI agents as independent minions working either by themselves or within a web of other interconnected agents (AI agent frameworks), performing an assigned role and using tools to accomplish their tasks. While one agent can perform a single role, multi-agent frameworks can create an entire AI ecosystem.<\/p>\n For example, say you want to generate a list of popular speaking topics on AI based on published conference agendas. Several agents would perform individual tasks, such as researching conferences for speaking trends, writing compelling abstracts and pitches, reviewing proposals and requests, providing feedback and editing.<\/p>\n In this example, the agent researcher would be able to research session information from listed AI conferences to identify popular topics and talks and would be equipped with tools to search and scrape websites for keynote speakers, session titles and themes to understand trends. Likewise, each agent is equipped with the right tools to complete its assigned tasks and to move the project to the next phase.<\/p>\n The reviewer can pass it on to a writer, who can move it to an editor and so on. Each agent would not only perform its role but would interact with others to complete the task with or without human input, based on the instructions provided.<\/p>\n AI agents go beyond customer service use cases, and in fact, customer service applications typically aren\u2019t agents at all because, again, they\u2019re typically not autonomous and don\u2019t make decisions based on context.<\/strong><\/p>\n Companies are creating AI agents to conceptualize and create sales solutions in a fraction of the time. They can even leverage and learn from past proposals and documents to speed up team collaboration and the creation of highly customized solutions.<\/p>\n Consumers can also leverage AI agents to appeal parking tickets, automatically cancel free trials, cancel timeshares, change their mailing address and many other tasks.<\/p>\n AI agents can also monitor databases, diagnose issues and optimize advice. Entire industries, like insurance, healthcare, marketing, manufacturing and others, will likely see AI agents being introduced into the mainstream in the coming months and years.<\/p>\nWhat AI Agents Are<\/h2>\n
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How AI Agents Are Used Today<\/h2>\n