Learn about what AI agents are, their similarities to related AI technologies, and how to determine if they’re a good next step for your organization.
The world of artificial intelligence can be dizzying, with new terms and technologies emerging at breakneck speed. Among newer technologies, AI agents have generated a significant buzz – and for good reason. Many companies already employ AI to automate tasks and increase productivity. AI agents are the next breakthrough in technological transformation.
We can think of AI agents as being on the same level as the steam engine was during the Industrial Revolution. Just as steam engines provided energy to drive machinery, enabling unprecedented mechanical work that revolutionized industries, AI agents offer more than rote automation. They offer intellectual and decision-making capabilities that can augment human intelligence and help business leaders solve problems in new ways.
But what are AI agents? And how do they differ from more traditional and well-known AI chatbots and virtual assistants?
We aim to demystify AI agents and explore their relationship with other somewhat similar technologies, such as AI chatbots and AI-powered virtual assistants. Understanding these nuances can help you determine whether implementing an autonomous AI agent makes sense for your organization and what steps to take as you prepare.
What Are AI Agents?
AI agents are large language models (LLMs) equipped with tools to take on specific roles and autonomously make decisions. Also known as agentic systems or compound AI systems, they’re unique in their ability to make reasoned decisions without human intervention.
While some refer to chatbots as AI agents or use the terms interchangeably, they’re not the same. Although AI agents are role-based, they are not chatbots, which lack the decision-making capabilities, autonomy, and problem-solving skills that define AI agents.
AI agents operate based on predefined protocols, unlike conventional AI tools like ChatGPT that respond to user prompts. While they can range from simpler rule-based systems to complex machine learning models, all AI agents share four common threads:
- Autonomy.
- Extensibility or the ability to integrate outside sources of data and capabilities.
- Problem-solving skills.
- Specialization – or the ability to perform specific tasks from start to finish.
This may lead you to question: Is ChatGPT an AI agent?
No. While it has some characteristics of an AI agent, such as the ability to perform tasks like answering questions, generating text, and helping solve problems, it lacks true autonomy. ChatGPT does not make independent decisions or act beyond generating text responses. In addition, it can’t use tools like AI agents, does not learn from interactions or update its knowledge. It’s trained on a fixed dataset up until its last update, meaning the version you interact with has a defined knowledge cutoff marking the point up to which it has been trained. Finally, unlike AI agents, developers created ChatGPT to focus on language tasks, and it cannot interact with the world as agents can.
Now that we’ve defined these technologies, let’s explore how they compare and where they fit in the AI ecosystem.
A Comparison of AI Agents to Other AI Tools
AI-Powered Chatbots: In simple terms, an AI chatbot, as defined by Salesforce, is “software that simulates human conversation with an end user.” Unlike traditional — or non-AI chatbots — AI chatbots use natural language processing (NLP) to understand user inputs and generate responses. They’re typically designed for specific tasks or domains and can range from a simple, rule-based system to more sophisticated AI-driven models.
Virtual Assistants: Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa are all examples of AI-powered virtual assistants. They perform tasks for users based on commands or questions, often incorporating elements of chatbots and agents into their responses. Unlike chatbots, though, virtual assistants offer a wide range of functionalities across multiple domains.
In terms of complexity and capability, the scale would have simple, rule-based chatbots on the left, fully autonomous AI agents on the right, and virtual assistants and AI-powered chatbots somewhere in between — depending on their degree of sophistication.
Simpler chatbots provide static responses based on predefined rules. More advanced AI-powered chatbots and virtual assistants can learn from interactions to improve their responses. The most versatile AI agents, however, are designed to continuously learn and evolve, adapting their behavior based on new information and experiences.
With the scalability, adaptability and diversity of these tools, it’s no wonder they’re creating a buzz in tech circles. But is all the chatter and excitement just hype, or are AI agents the next big innovation in AI-powered business tools?
What’s All the AI Agent Fuss About?
Not only are AI agents autonomous, problem-solving marvels, but they also bring leaps in technology to the forefront. Enhanced processing capabilities, emotional intelligence, and continuous learning and adaptation all help hype these tools.
Developers are building AI agents to understand emotional cues and respond accordingly, and to continually learn and adapt. Examples include:
- ChemCrow is a chemistry agent capable of tapping into chemical databases to plan and execute complex syntheses. It has already been used to create a new bug spray, develop multiple eco-friendly chemical catalysts, and discover a new compound that could have various applications in scientific fields.
- OS-Copilot enables the creation of digital assistants that can interact with various operating system components to provide support across diverse computing tasks.
- D-Bot uses language models to learn from ongoing database maintenance tasks and offers advice on diagnostic issues and database performance optimization, improving its capabilities with experience.
These are only a few examples of companies getting an early start in AI agentic systems. Consumers can also use these tools today with companies like DoNotPay to “fight big corporations, protect your privacy, find hidden money, and beat bureaucracy.” The agent helps consumers appeal parking tickets, automatically cancel free trials, cancel timeshares, change their mailing address, and perform many other tasks they may not be able to devote the time or energy to complete on their own.
Another Real-World AI Agent Example
Consider a thriving healthcare startup we helped streamline a complex process to improve time and accuracy. The company, which specializes in advanced medication management and personalized patient care, uses cutting-edge AI and data analysis to serve an expanding network of medical institutions nationwide.
The firm handles an enormous amount of sensitive information in diverse formats, from patient records and clinical notes to prescription details and miscellaneous medical documentation. These files, which are stored in various systems under different classifications, pose challenges for those required to sift through and find the information they need to manage prescriptions.
Their team combed through each document to manage ongoing prescriptions, a process both time-consuming and susceptible to human error.
We built an LLM-powered AI agent that can contextually understand what to look for, which reduced manual labor by 82 percent and increased accuracy to nearly 100 percent. At the core was an LLM designed to parse and extract vital demographic data from a variety of hospital discharge notes.
This AI-driven agent approach capitalized on the power of retrieval-augmented generation (RAG) alongside advanced prompt engineering techniques to adeptly navigate the varying formats and intricate details within discharge notes.
To answer the question posed earlier: AI agents are both worth the hype and the next big innovation in AI for businesses. As AI agents continue to evolve, it’s crucial for businesses to stay ahead of the curve.
My Company Would Benefit from an AI Agent
Maybe. Probably. While it might be easy to say, “Yes, let’s do this AI agent thing!” it’s harder to implement.
There’s a simple reason not every company is leveraging AI agents yet. It’s not easy.
Make no mistake, while AI agents offer tremendous promise, there are still risks to contend with, like those related to implementing ChatGPT and other AI. You must balance economic, social, ethical, and other implications as you consider your approach.
Successfully integrating an AI agent into your organization will require your tech and leadership teams to:
- Conduct a thorough organizational assessment, examining both current operations and future state goals.
- Develop a comprehensive AI strategy incorporating agent technology, aligning it with broader business objectives.
- Establish clear, measurable goals and performance indicators specifically for AI agent deployment.
- Tackle critical infrastructure challenges, including data management, security protocols, and regulatory compliance.
- Evaluate and upgrade existing technology systems to ensure they can support and integrate with AI agent frameworks.
- Assemble a diverse team with expertise in AI technologies, focusing on those with knowledge of large language models and their applications.
- Define clear roles and responsibilities within the team to manage AI agents’ development, deployment, and maintenance.
- Create a plan for continuous learning and adaptation, as AI agent capabilities evolve rapidly.
Conclusion: The Future of AI is Now
The future of transformative AI is AI agents, and by getting ready now, your company can be positioned to use the technology, gain a competitive advantage, and drive innovation across business processes when the time comes.
In our on-demand webinar, our artificial intelligence expert provides an executive’s guide to what leaders need to know about adopting ChatGPT and AI in the workplace.
By working with experienced AI consultants and staying up to date on new developments, organizations can position themselves to benefit from AI agents while navigating the challenges they may present. Contact us
