What is agentic AI?

Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. With an emphasis on goal-oriented behavior, agentic AI (also known as AI agents) can accomplish tasks by creating a list of steps and performing them autonomously.

You can think of agentic AI as a way of combining automation with the creative abilities of a large language model (LLM). To bring agentic AI to practice, you create a system that provides an LLM with access to external tools, and algorithms that supply instructions for how the AI agents should use those tools.

The way agents communicate with tools involves orchestration, with flows or graphs depending on the framework being used. This approach allows the LLM to “reason” and determine the best way to answer a question–such as deciding whether the query can be answered with available information or whether an external search is necessary.

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Think of an AI agent as an entity that sits on top of other software tools and operates them. Agentic AI can be a physical structure, a software program, or a combination of the 2.

An AI agent in a robotic system might use cameras, sensors, and monitors to collect data about its environment, then run that information alongside software to make determinations about its next step. This is a process known as sensor fusion.

Meanwhile, agentic AI in a software setting would collect data from other sources, such as APIs, online searches, text prompts, and databases that help the agents create a sense of perception and context.

Let’s dig a little deeper into some of the specialized features of agentic AI:

Agentic AI is adaptive and dynamic

Agentic AI learns from previous patterns and data. This means that it can change its strategy based on new or changing information it receives, in real time. While traditional workflows only move forward, agentic workflows can move forward and backward, with the ability to backtrack and mend errors as it goes. In other words, agentic AI can proactively anticipate needs and reflect on its own work.

For example, an autonomous vehicle may use agentic AI to improve its ability to sense the difference between a piece of trash on the road and a squirrel. As it continuously monitors and analyzes its own behavior, it can improve the outcome of its actions.

Agentic AI can independently manage and complete tasks

Agentic AI is sometimes referred to as autonomous AI. This is because it has the capability to communicate and collaborate with other AI systems and digital infrastructures on behalf of a human user, or another AI agent.

For example, you can tell an AI agent that you want to make spaghetti for dinner. The AI agent could then complete the steps necessary to find a recipe, make a list of ingredients, and place an order for those ingredients to be delivered to your home from a local grocery store.

Agentic AI has a “chaining” ability

This means that the AI system can perform a sequence of actions in response to a single request. For example, if you ask an AI agent to “create a website” it can perform all the steps needed to carry out that task. This means that from 1 prompt, the AI agent can carry out the tasks of writing the code for the structure, populating the pages with content, designing the visuals, and testing for responsiveness.

Agentic AI is most useful for tasks that require continuous monitoring or rapid decision making. The benefits of agentic AI include:

More productivity - delegating tasks to an AI agent allows for more focus to be placed on initiatives that add value to an organization. Think of it as an intern that works 24 hours a day, 7 days a week.

Reduced cost - agentic AI reduces human error, taking away the cost associated with inefficiencies, oversights, and mistakes.

Informed decision making - agentic AI uses machine learning to filter and process massive amounts of real time data–more than any human ever could. Gaining insights from larger pools of good data result in better predictions and strategies.

Improved user experience- traditionally, creating an automated workflow requires expertise in engineering and coding. With agentic AI, users can interact with plain language, similarly to the way we’ve learned to interact with platforms like ChatGPT.

With these unique features, agentic AI can be used for many purposes. Let’s take a look at how agentic AI may be used in an industry setting:

• Business operations could use an AI agent to manage supply chains, optimize inventory levels, forecast demands, and plan logistics.
• Healthcare fields could use an AI agent to engage with clients, monitor needs, carry out treatment plans, and provide personalized support.
• Software development could grow more efficient from using agentic AI to automatically generate debugging code, manage development lifecycle, and design system architecture.
• Software operations could use agentic AI for the autonomous operations of networks and other IT infrastructure or services.
• Cybersecurity could benefit from an AI agent helping to monitor network traffic, detect issues, and respond to threats in real time.
• Researchers could use agentic AI to design and run experiments, analyze data, formulate new hypotheses, and generally speed up the pace of innovation by operating faster than a single human (or group of researchers) ever could.
• Finance and trade could be enhanced by agentic AI’s ability to constantly analyze market trends, make trading decisions, and adjust strategy based on streams of real-time data it has access to.

Agentic AI brings us closer to creating intelligent systems that can operate independently, collaborate effectively, and learn from their interactions with data. Agentic AI works because of a process known as an agentic workflow.

An agentic workflow is a structured series of actions managed and completed by AI agents. When an AI agent is given a goal to complete, it begins the workflow by breaking down a task into smaller individual steps, then performing those steps.

To carry out this series of steps, an AI agent spins up more versions of itself, creating a multi-agent system (MAS). In this workflow, the main agent (also known as a meta agent, orchestrator, or supervisor) delegates tasks to other agents, assigning values and interacting with memory in a feedback loop. Together, the committee of agents work in parallel until the overall goal is complete.

Within this MAS, each agent is made up of an internal structure that allows it to function both independently and collaboratively within its system. This collaboration is dependent on shared memory stores, which provide context regarding individual knowledge, past experiences, and belief states.

If generative AI focuses on creating, agentic AI focuses on doing. Generative AI creates new content using predictive modeling and linear regression. Agentic AI uses mathematical systems to make decisions based on predictive modeling, but it goes a step further by carrying out an action–or a series of actions–on behalf of the user.

Generative AI creates output based on prompts we input. Agentic AI differs from traditional AI in that it has the ability to initiate action. For example, an AI agent can create its own additional prompts and outputs based on the information it has access to.

Retrieval-augmented generation (RAG) is a method for getting better answers from a generative AI application by linking an LLM to an alternate instructed prompt, or an external resource. Agentic RAG takes traditional RAG a step further by enabling the LLM to actively investigate rather than simply retrieve.

While RAG can retrieve answers and provide some context from documentation and datasets it has access to, it relies on manual prompt engineering. Traditional RAG also has limited contextual awareness and relies exclusively on the initial query to retrieve relevant information.

Agentic RAG is comparatively more sophisticated and dynamic. It can come up with questions of its own, create context from its memory, and carry out additional tasks without being explicitly asked to do so. This step beyond traditional RAG grants agentic RAG the ability to make more informed decisions on your behalf, independently of your manual intervention.

For example, with traditional RAG, you can query a chatbot to show you a company’s return policy. With agentic RAG, your query could serve you the return policy, then prompt you with the option of initiating a return. Upon which, the AI agents could carry out the logistics of filling out the return form with your order number, verifying your credit card information for reimbursement, and completing the transaction on your behalf.

Agentic AI brings the promise of innovation and speed to many of our systems. However, there are ethical and technical issues that are still being addressed. For instance, how can we make sure that agentic systems are aligned with our values? Who is responsible for when an agentic AI makes a mistake? In some cases, there are transparency challenges, in that we don’t know for sure how the agent came to the conclusion that it is offering as an output (also known as the “black box” problem).

From a privacy and security standpoint, it’s important that we treat any AI model we build or use with care and consideration. That is, making sure that the architecture is built with security parameters in place to protect the flow of data.

It’s also worth noting that agentic AI requires substantial computing resources, including a lot of processing power and storage needs. The environmental impact of which is important to be mindful of.

Lastly, as with any emerging technology, there is a learning curve to be aware of. Implementing and managing LLM agentic workflows requires specialized skills, especially at an enterprise level.

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For organizations still in the exploratory phase of realizing the benefits of generative AI, AI agents might be the key to discovering tangible business value. Red Hat® AI and our AI partner ecosystem can help you design the frameworks for building agentic workflows and scaling AI agents.

Red Hat Enterprise Linux® AI is an agentic orchestration of InstructLab, and can be used for fine-tuning the LLMs and SLMs used by agentic workflows.

Red Hat OpenShift® AI provides a unified platform to create multi-agent systems. Plus, the adaptive learning and reasoning that AI agents use can be controlled via OpenShift’s MLOps capabilities.

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