AI Agents Explained
Artificial Intelligence (AI) Agents are revolutionizing industries, automating tasks, and enhancing customer interactions that were once handled by humans. These systems provide real-time responses, streamline workflows, and deliver personalized experiences. But what exactly are AI agents, and how do they differ from traditional chatbots?
In this post, we’ll explore the definition, core technologies, and applications of these agents, focusing on Responsible AI practices that ensure accuracy and ethical governance.
What is an AI Agent?
An AI Agent is an intelligent, autonomous virtual employee that interacts with users or other systems to offer support, answer questions and complete tasks,. Unlike traditional chatbots, that rely on predefined scripts, AI Agents utilize advanced technologies like Natural Language Understanding (NLU), Machine Learning (ML), and Knowledge Graphs to interpret user intent and deliver precise responses.
Traditional chatbots manage simple, repetitive interactions, but these Virtual Agents engage in more dynamic, meaningful conversations. They can handle real-time, context-aware interactions in customer service, business automation, and beyond.
AI Agents Explained: Key Technologies
Natural Language Understanding (NLU)
Natural Language Understanding (NLU) is vital for AI Agents to interpret user intent and context. Unlike standard Natural Language Processing (NLP), which processes text, some forms of NLU allow virtual agents to grasp deeper meaning behind user inputs. Enhanced understanding and emotional understand support more accurate, contextually and emotionally appropriate responses to complex queries.
In fact, “80% of survey respondents report measurable improvements in customer satisfaction, service delivery, and contact center performance” due to AI agents, according to a 2018 MIT Technology Review study. This shows the impact that AI has in boosting agent performance and customer experience.
Large Language Models (LLMs)
Large Language Models (LLMs) empower Virtual Agents by granting them access to extensive datasets, enabling them to generate detailed, informative responses. However, LLMs also pose risks, such as hallucinations, where the AI generates incorrect or biased information. To address this, LLM leverage human oversight and ‘tuning’ during training to mitigate inappropriate behavior in increase accuracy.
To increase accuracy and relevance, LLM-based agents are also paired retrieval-augmented generation (RAG) that grounds agent responses in documentation and language from the host organization or other relevant information from the Internet. While this is a step forward, human expert governed-in-advance approaches ensure maximum compliance and accuracy.
Knowledge Graph-Based AI (Graph AI)
To best explain AI Agents, we propose that Ethical Virtual Agents (EVAs) powered by Knowledge Graph-based AI stand out. Unlike probabilistic LLMs, Graph AI uses curated knowledge from enterprise experts to deliver consistent, fact-checked, and on-brand information. This ensures trustworthy, real-time answers, especially in mission-critical scenarios.
For example, kama.ai leverages Graph AI to ensure that the Virtual Agents align with corporate values, avoiding misinformation and bias. By using pre-verified information, these agents provide accurate fact-based responses, with the ability to seamlessly hand off complex issues to human agents or dedicated Robotic Process Automation (RPA) bots when necessary.
Applications of AI Agents Across Industries
AI Agents are transforming industries across the board. Below are key sectors already reaping the benefits:
Customer Service
In customer service, Ethical Virtual Agents (EVAs) can handle a high volume of inquiries with accurate, quick responses. This allows human agents to focus on more complex and challenging cases. EVAs operate 24/7, ensuring that customers always have access to information and support. When necessary, complex issues are escalated to human agents, creating a balanced, efficient system.
Interestingly, research from Zendesk (2024) reveals that “almost one-half of customers think AI agents can be empathetic when addressing concerns.” This growing trust in AI agents highlights their ability to manage even sensitive inquiries effectively.- Finance
In finance, AI Agents can assist in loan processing, fraud detection, and customer support. By rapidly processing vast amounts customer inquires, around the clock, they can ensure efficient customer service while maintaining compliance with regulations. - Healthcare
Healthcare is seeing a significant impact from Virtual Agents, that can streamline administrative tasks like appointment scheduling and answering health-related questions. However, Responsible AI governance is essential in healthcare to prevent risks, such as biased or inaccurate responses. Human-in-the-loop and Graph AI solutions, can be especially beneficial in healthcare environments where accurate information delivery and triage is critical. - Colleges and Universities
Higher education institutions are using Virtual Agents to automate administrative tasks like admissions, course registration, and financial aid processing. An excellent example is Canadore College, where EVAs provide real-time responses to students’ inquiries, improving the student experience while freeing up staff for more complex issues. Using Knowledge Graphs informed by human experts from the college, these AI Agents ensure that students receive accurate information about courses, schedules, or campus amenities and policies.
Challenges and Ethical Considerations
As AI Agents become more prevalent, ethical concerns around privacy, bias, and data reliability must be addressed. Platforms like kama.ai’s Designed Experiential Intelligence® address these challenges through Responsible AI governance, ensuring that AI Agents adhere to ethical standards and fact-based information delivery. Human oversight and verified knowledge sources ensure accuracy and trustworthiness, allowing organizations to confidently deploy AI Agents freeing human staff to focus on issues that require particular attention.
Conclusion
This post sets out to provide clarity on the topic of today’s AI Agents. Ethical Virtual Agents (EVAs) are transforming how businesses manage customer interactions and internal processes. By leveraging technologies like NLU, LLMs, and Knowledge Graphs in responsible ways, EVAs offer reliable, accurate, and ethical solutions across various industries.
Organizations looking to deploy AI to increase productivity and customer satisfaction should prioritize platforms that emphasize ethical AI practices. kama.ai’s Designed Experiential Intelligence® platform, kama DEI, ensures that businesses can confidently implement Ethical Virtual Agents that align with their values while improving efficiency and safeguarding brand integrity. These systems provide peace of mind, ensuring sound answers without the risk of damaging your brand’s reputation. For a understand Responsible Conversational AI in much more depth, read the ebook on Responsible AI (no forms to fill out, no strings attached).