What is agentic AI?

TL;DR

Agentic AI is considered one of the most advanced types of artificial intelligence (AI) today. Composed of multiple AI agents that collaborate to manage complex tasks, agentic AI operates autonomously and without the need for human interaction to achieve its goals.

Agentic AI also demonstrates human-like behavior, including the following ways that agentic AI acts much like an end user:

• Continuously learns and improves
• Makes informed decisions
• Composes and performs context-based strategies

Agentic artificial intelligence (AI) explained

Agentic artificial intelligence (AI) is the latest frontier of AI. It can be characterized as a form of AI that can function autonomously, making choices and actions without the need for human intervention. It is a complex AI framework with a system that orchestrates to reason with new knowledge, formulates plans, and autonomously makes decisions to pursue goals without constant prompting.

Agentic AI is different from AI chatbots that leverage generative AI (GenAI) to produce replies based on a single interaction, such as when an end user inputs a query and the chatbot leverages natural language processing to respond. Agentic AI leverages machine learning (ML) and reinforcement learning (RL) to quickly solve complex, often multi-step problems. It demonstrates sophisticated reasoning, iterative planning, and the capacity to flexibly learn, problem-solve, and adapt to changing environments.

Agentic AI perceives context and adjusts strategies after multistep reasoning similar to end users and can behave proactively, making decisive choices on the end user’s behalf. The agentic AI framework comprises several independently acting AI agents that can manage complex workflows through collaboration. On completing goals, it automatically updates its memory through a feedback loop, learning from recent actions and outcomes. Over time, this continuous learning allows agentic AI to self-correct and improve its reliability.

In the article, Agentic AI: The Next Frontier of AI Power, Dr. Phong Nguyen writes that agentic AI “may be a more powerful phenomenon [than generative AI]”¹ and that it “[could be] stage three of [the] five stages in the development of AI”.¹ Dr. Nguyen states that while GenAI has produced a dynamic shift in how we create, agentic AI may cause the next big change due to “its ability to automate complex processing tasks via the ability of multiple agents to autonomously learn, adapt, and make decisions to accelerate and automate decision-making and problem-solving”.¹

How does agentic AI work?

Agentic AI leverages the following simple and effective framework to achieve its goals in four steps:

1. Perceiving
Data is drawn from several disparate sources, such as conversation history or databases within an IT infrastructure or the internet. The agentic AI then synthesizes the data, scrutinizing for information that is measurable, comparable, and usable in decision-making.

2. Reasoning
One or several large language models (LLMs) are leveraged to understand tasks, devise a plan, and implement a solution. These LLMs act as the agentic AI’s nerve center, allowing it to capture and process multimodal information. Agentic AI often uses retrieval-augmented generation (RAG), a technique that boosts LLMs’ performance by supplementing it with additional data, to better support that its information is being drawn from verified sources.

3. Acting
Tools and application programming interface (API) integration help an agentic AI to effectively perform tasks and orchestrate its devised plan to deliver on its goals. To increase its chances of success, agentic AI can be programmed to recognize thresholds and abide by policies. This is a must-have for its adoption in business ecosystems where processes are defined by strict rules and compliance.

4. Learning
Agentic AI can improve itself over time through feedback by drawing data from its outcomes and interactions to optimize future actions. This continuous improvement process allows the agentic AI system to gradually adapt and enhance itself.

What are several agentic AI use cases?

The following are four use cases that show how agentic AI is evolving and demonstrate how it can be applied in different scenarios.    

Solving tickets
For customer-facing businesses, agentic AI can automate touchpoint interactions and handle complex inquiries with great efficiency, leading to quicker resolutions and higher confidence in the process. It can also provide conversational support through self-healing workflows and chat interfaces, such as chatbots. An agentic AI system is virtually always operating and logging every event, including when it’s handling thousands of simultaneous interactions. Its ability to process large volumes of data in real time greatly reduces the workload on human analysts who would otherwise have to sift through a staggering number of alerts.

Searching the web
Fetching search results can feel intuitive for agentic AI because an agentic AI system doesn’t wait for users to start feeding in queries. Instead, it anticipates what the user is looking for to yield information without being prompted for it. An agentic AI can aggregate information and summarize it to provide a clear answer instead of links to multiple sources, saving end users time that would otherwise be spent navigating to different webpages and creating a more user-friendly web search process.

Writing code
LLMs have been capable of generating code for well-contextualized problem statements for several years. Agentic AI takes code generation one step further and creates an end-to-end code generation and management process that is entirely driven by natural language prompts. Agentic AI not only generates the code for end users but also debugs it, optimizes it, and manages its deployment with limited developer intervention. From modernizing legacy codes to rapid prototyping, testing, and improving code quality, agentic AI is driving an improved user experience for web development.

Collaborating
In an agentic AI framework, one or more AI agents collaborate to achieve a common goal. Much like a team of end users combining their expertise in pursuit of a goal, agentic AI agents can integrate seamlessly and perform a sequence of steps to solve multidimensional problems. As their goals evolve or take on different forms, agentic AI agents can flexibly adapt as they go and maintain their collective problem-solving capabilities.

What are the challenges for agentic AI systems?

The following challenges must be addressed for enterprises to build confidence with broadly leveraging agentic AI:

The black box problem
A black box problem refers to how difficult it is to understand the complexity of AI systems and how they come to their conclusions. Some end users are uneasy about trusting a complex AI framework that relies on probability and sophisticated integrations to determine its next course of action. The invisible methods in which agentic AI reasons make auditing it a technical challenge, especially as decisioning processes continually evolve.

Ethical considerations and governance
Coexisiting with AI systems requires deep mutual trust and transparency. As agentic AI takes on more complex roles, its limited understanding of human values can lead to ethical conflicts and important questions, such as “Can a system that favors efficiency prioritize human safety?” and “Who is responsible for an agentic AI’s actions?”.     

Questionable accountability
Without accountability for decisions made, the IT industry will continue to tread cautiously around agentic AI. A rising concern is “What if agentic AI actions lead to adverse circumstances?”. Today’s legal frameworks lack methods to deal with unintended harm caused using agentic AI or to enforce accountability, which may lead to a lack of recourse if an agentic AI-related incident occurs.

Cybersecurity concerns
Many of today’s AI subject matter experts are trying to determine how agentic AI can be optimally leveraged to help defend against cyber threats. The increasing number of cyber-attacks and data breaches highlight that these risks are always there and that IT systems operating with high levels of autonomy and without any necessary guardrails are vulnerable to the disruption of their critical operations.  

How is agentic AI different from general AI and generative AI?

The three distinct but related types of AI are traditional AI or general AI, GenAI, and agentic AI.

General AI commonly works to perform a wide variety of tasks in the same manner that an end user might perform them. 

Generative AI or GenAI for short, is primarily a creator's tool that is designed for content generation. As a reactive system, it leverages pre-trained knowledge and strives to produce creative outputs in response to end users’ prompts. These could be text, images, code or similar content. Knowledge cutoff dates can limit its ability to access real-time information, such as comparing flight fares for the following day.

GenAI requires constant prompting and human oversight, and it cannot integrate with external tools or call application programming interfaces (APIs) to take actions. It tends to excel at providing recommendations and guidelines that users can rely on to make their own decisions.

Examples of GenAI programs and applications include chatbots, text-to-image generators, music composers, such as ChatGPT or DALL-E. 

Agentic AI frameworks are highly adaptive, possess ample versatility, and have the ability to learn from experience and adapt to changing environments. These frameworks leverage multiple AI agents within their systems to collaborate autonomously to understand context and take decisive actions. Thanks to large language models (LLMs), agentic AI excels at active perception, reasoning, multistep planning and strategy execution and can interpret natural language prompts and conversations to understand users’ intent, perform logical reasoning, and pull stored data to make instant decisions.

Agentic AI is often leveraged for decision-making, performing tasks, and working towards achieving goals or objectives. Agentic AI is leveraged for programs and applications for robotic process automation, autonomous vehicles, and more advanced customer service chatbots. 

What are the three waves of AI?

Agentic AI is widely considered to be the third wave of AI. In the Forbes article The Third Wave Of AI Is Here: Why Agentic AI Will Transform The Way We Work, Silvio Savarese states that “The first wave was really predictive AI”2 which enabled businesses to forecast trends and make data-driven decisions. The second wave was GenAI, which gave end users “[the ability to] generate content and allow conversations with humans".2 We are entering the third wave with agentic AI, which has us “[using] agentic AI to automate entire tasks and perform actions on our behalf”.2

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