The term Agentic AI refers to artificial intelligence (AI) models equipped with a degree of agency that operate on their own to achieve broad objectives. While Traditional AI technologies have been limited to executing predefined tasks or responding to specific commands, an Agentic AI model’s capabilities allow it to act more like a digital “agent,” capable of independently assessing situations, choosing actions and learning from its experiences.
The whole concept of a digital agent isn’t all that new; it wasn’t long ago that David Hasselhoff was first filmed quipping back and forth with KITT, the AI bot inside his sweet all-black 1982 Firebird. The concept found early applications in customer service, like AI-powered call centers and chatbots. In Thailand, for example, 7-Eleven uses AI agents to handle 250,000 phone calls daily. While many customers still prefer human interaction, developers see the potential for Agentic AI to become so adept at mimicking human conversation that these agents may eventually become indistinguishable from their fleshy counterparts.
Other early examples of Agentic AI include GitHub Copilot Workspace and Google AI Teammate. These tools have already made a significant impact in fields like software development. A study by Capgemini found that 75% of organizations plan to incorporate AI agents into their software development cycles by the next year, with 10% of large enterprises already using these technologies.
How does Agentic AI work?
Agentic AI is built on advanced AI training techniques like large language models (LLMs), machine learning, deep learning and reinforcement learning. LLMs, such as GPT-4 or Google’s Gemini, allow these systems to comprehend and respond to natural language commands. Machine learning algorithms help Agentic AI analyze data to recognize patterns, while reinforcement learning allows the system to learn from experience, refining its decision-making over time.
One of the defining characteristics of Agentic AI is the “chaining” technique, where it breaks down complex requests into manageable subtasks and puts them in order of priority. For instance, an Agentic AI model might receive a command to improve efficiency in a factory. It would break this broad directive into steps like optimizing inventory levels, streamlining the supply chain and automating routine communications. This chaining capability allows Agentic AI to handle intricate workflows autonomously.
Additionally, Agentic AI employs retrieval-augmented generation (RAG) and “tool calling." RAG enables the system to retrieve relevant information as needed, while tool calling allows the AI to access specific software tools or application programming interfaces (APIs) based on the task at hand. If you’re interested in learning about RAG, check out our quick definition here! Unlike traditional AI models that require a specific prompt to begin working, Agentic AI is always on, continuously monitoring environments and acting whenever it identifies an opportune moment.
Why is Agentic AI important?
Agentic AI has the potential to transform entire industries by providing autonomous, tailored experiences and solutions. For businesses, Agentic AI can provide detailed insights by analyzing operational data and autonomously acting on it. This capability makes it particularly effective for optimizing tasks that benefit from constant monitoring or rapid decision-making, allowing for streamlined operations without the need for human oversight. By combining machine learning capabilities with a goal-oriented approach, Agentic AI systems can tackle complex challenges in innovative ways. These systems can adjust their strategies based on new information or circumstances, making them effective in dynamic environments.
Agentic AI’s ability to provide 24/7 autonomous support makes it particularly valuable for applications such as:
- Customer Service: Agentic AI can deliver round-the-clock personalized customer service and can go beyond simple FAQ responses, resolving complex issues and anticipating customer needs. Tools like Netcore’s Co-Marketer AI and Salesforce’s Agent Force have led the way in personalizing customer interactions. Continuously learning from user behavior, these tools allow brands to deliver highly relevant recommendations and offers that adapt to an individual customer's tastes, giving businesses the freedom to focus on higher-level strategies while ensuring customers receive highly personalized, relevant content.
- Sales: Like customer service, organizations can use Agentic AI in a front-facing capacity to improve hospitality and sales turnaround. Tools like Conversica and Relevance AI are already offering AI-powered assistants that autonomously engage with potential leads, qualify them, and nurture prospects through the sales funnel. These assistants have helped businesses achieve up to a 5x increase in qualified sales opportunities. Additionally, a Gartner report suggested that by 2025, 75% of B2B sales organizations will augment their teams with AI-driven agents to automate routine tasks and improve productivity.
- Healthcare: AI agents could monitor patients continuously, adjust treatment plans in real time and provide them personalized therapeutic support.
- Software Development: AI agents could manage development lifecycles from code generation to quality assurance, autonomously creating, debugging and deploying code. A recent study from Capgemini found that 75% of organizations surveyed are looking to use AI agents for software development soon.
- Cybersecurity: Agentic AI can manage an organizations security posture, allowing human analysts to focus on more complex challenges. Companies like Darktrace and Vectra AI have developed AI agents that do just that, continuously monitoring network traffic, identifying threats and autonomously initiating responses.
- Scientific Research: Agentic AI can design and run experiments, analyzing results, and revising hypotheses, potentially accelerating the rate of scientific discovery.
Additionally, while the management of IT infrastructure has typically been handled through human intervention, platforms like Qovery has begun to leverage Agentic AI to automate the oversight, configuration, and management of these critical infrastructures. The Internet-of-Things (IOT) concept can also incorporate Agentic AI, enabling powerful AI agents to monitor, analyze and optimize operations in real time across large networks of interconnected devices.
Companies like Aflac, Atlantic Health System and NASA's jet propulsion laboratory are already exploring these applications. With time, Agentic AI could reshape entire industries.
While there are a lot of potential benefits to Agentic AI, it also carries certain challenges and ethical concerns. One major consideration is ensuring that these autonomous agents act in ways that align with human values. Given their complex decision-making processes, questions of accountability and trust become critical, especially in high-stakes environments where mistakes could have serious consequences. Begging the question of “Who bears responsibility when an AI makes a mistake?”
Data privacy and security are also especially important as AI agents both become more autonomous and deal with increasingly sensitive information. For Agentic AI to be viable, customers need guarantees that organizations will take steps to prevent their data from being misused or leaked.
In June, Fierce Networks Research conducted a survey of 120 CSP leaders and found that over two-thirds expressed confidence that AI would be able to manage networks effectively, without human intervention, in the next ten years. Projections like these have exacerbated concern about the impact of Agentic AI on the job market. While these systems could enhance productivity and create new roles in the long run, they may also displace certain jobs, raising anxieties surrounding the disruption this could cause for employees in various industries. However, Mitch Wagner, Fierce Network’s Chief Research Analyst, reported some interesting findings along with this statistic that may alleviate some of your concerns. Most notably, the majority of CSP leaders don’t share this perspective. If you’re interested in learning why and how historically technological advancement has led to increased demand for labor, check out the Bulletin here!