In this segment of Joseph Ours’ Forbes Technology Council column, Joseph talks about the rise of AI agents and how these tools can help businesses excel.
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.
But it’s time to think bigger.
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.
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 — in terms of productivity gains, cost savings and enhanced capabilities — will likely be substantial.
What AI Agents Are
AI agents are essentially large language models (LLMs) equipped with tools to take on specific roles and autonomously make decisions. Although AI agents are “role-based,” they are not chatbots — not even close. These AI agents go beyond simple linear chatbot-style interfaces to perform complex, iterative jobs from start to finish.
Also known as “agentic systems” or compound AI systems, they’re unique in their ability to make reasoned decisions — as in, they use reasoning — without human intervention. They can consume and analyze information and develop action plans to work toward their objective.
Rather than prompting to get to an answer, as with other AI tools, AI agents are created to be autonomous entities operating on specific instructions. They can range from simpler rule-based systems to complex machine-learning models, but all AI agents have:
- Autonomy: From controlled to fully autonomous, agents offer automation flexibility.
- Extensibility: Agents integrate outside sources of data and capabilities.
- Problem-Solving Skills: Agents use natural language to accomplish tasks without the need for complex and complete sets of rules.
- Specialization: Agents can be specialized to perform specific tasks.
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.
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.
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.
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.
How AI Agents Are Used Today
AI agents go beyond customer service use cases, and in fact, customer service applications typically aren’t agents at all because, again, they’re typically not autonomous and don’t make decisions based on context.
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.
Consumers can also leverage AI agents to appeal parking tickets, automatically cancel free trials, cancel timeshares, change their mailing address and many other tasks.
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.
For example, say, a growing healthcare management company that uses AI and data analytics to provide personalized care plans for patients wants to better manage patient prescriptions for organizations it works with across the United States. Today, that might require a department of people who understand what they’re looking for to manually review each document — a time-intensive and error-prone series of tasks.
An LLM-powered AI agent can contextually understand and extract vital demographic data from various hospital discharge notes. By leveraging the power of retrieval-augmented generation (RAG) and advanced prompt engineering techniques, the AI adeptly navigates the varying formats and intricate details within these notes, significantly reducing manual labor and increasing accuracy.
Getting Ready For An AI Agent Framework
In many ways, preparing to implement an AI agent framework is like preparing for any new AI project or implementation. It requires a strategic approach in which business leaders must:
- Assess the organization’s needs and current and future states.
- Create an AI roadmap to determine how agents could fit into your strategy.
- Define agent-specific goals and KPIs.
- Address data integration, security and compliance.
- Ensure you have the technology in place to support your framework.
- Build a skilled team that understands LLMs and the various roles and responsibilities.
The true potential in developing AI agents is not in automating individual tasks but in creating intelligent ecosystems that can automate entire workflows. From healthcare to manufacturing and other industries, these frameworks will transform how work is viewed and done.
In creating adaptive, autonomous systems, businesses can turn the promise of AI agents into tangible, transformative results. These tools will revolutionize industries. Forward-thinking companies should start planning for AI agent frameworks today, even if they’re not ready for implementation. Organizations that employ AI agents could be leaders in 2025.
This article was originally published on Forbes.com.
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