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Artificial intelligence (AI) is in its third wave, and agentic AI is its latest iteration.
What Is Agentic AI?
Agentic AI refers to autonomous artificial intelligence systems that can set goals, make decisions, and take actions to achieve those goals without constant human input. Unlike traditional AI, which follows predefined rules or responds only to direct prompts, agentic AI exhibits a level of independence, adaptability, and initiative, making it capable of handling complex tasks and environments.
What Is The Difference Between Agentic AI and Generative AI?
Generative AI (GenAI) is great at coming up with responses to prompts and queries, but its output is mainly static. It’s trained on a set of data, which can be very large or focused and small for a specific use.
Agentic AI, on the other hand, is a system that combines machine learning (ML) with goal-oriented behavior. It uses sophisticated logic and iterative planning to autonomously make decisions, map out actions, and learn from experience.
How Does Agentic AI Work?
Agentic AI takes traditional AI and incorporates chaining abilities to carry out a series of actions in response to a single request by breaking a complex task into smaller steps. Here are the four steps it follows:
#1: Perceive
The AI agent collects data from different sources—sensors, digital interfaces, and databases—and processes it. Just like a person would look at their environment and identify what’s relevant, the agent will try to identify patterns, important details, or characteristics in the information. This is the stage where it gathers context and raw material needed to make decisions or plans for what it wants to do and how to do it.
#2: Reason
The AI agent uses a large language model (LLM) as the orchestrator, or brain, to understand what is required of it, plan or generate the appropriate response, and coordinate with other specialized AI models and tools to achieve the desired result. Depending on what’s needed, the specialized models can create content, process visual inputs, or make personalized recommendations based on user preferences using techniques like retrieval-augmented generation (RAG).
#3: Act
We know that agentic AI can do more than just make plans or generate ideas; it can carry out the required task. It does so by integrating with external tools using application programming interfaces (API) to act on the plan it created in the previous step. During this stage, it might interact with the relevant software, automate any repetitive or complex tasks, and otherwise do whatever’s necessary to complete the goals it set out to achieve.
#4: Learn
As we’ve discovered, agentic AI does more than just generate responses and carry out tasks. It also learns as it works, using the concept of “data flywheel,” which is a kind of feedback loop. The more the AI system is used, the more data it generates, which is then fed back to the system, allowing it to learn from it. The more it learns, the better it becomes. This learning helps it adapt better to the preferences of the users, changes in the environment, and new scenarios.
Why Agentic AI Needs Enterprise Data
Businesses are already using their data to power GenAI and get useful, actionable information. GenAI, combined with enterprise data, is being used to automate repetitive tasks and generate content. It is also quite helpful in supporting better decision-making.
You can see why agentic AI would be able to take your enterprise data a step further. It doesn’t just use it to generate content and ideas; it can act on it by implementing AI capabilities to execute tasks efficiently.
To do so, it uses accelerated AI query engines that can process, store, and retrieve large quantities of data. Using these engines, agentic AI can handle complex, real-time tasks.
This is where it also uses RAG to identify relevant and specific information from the vast quantities of data it can access. The advantage of RAG is that it allows the system to query live data sources—documents, APIs, databases—instead of only relying on what it was trained on. As a result, its outputs are not just accurate but also highly relevant.
What’s more, it uses the data flywheel to evolve and become faster, smarter, and more effective.
Agentic AI and Agentic Automation
Traditional automation relies on robotic process automation (RPA), which is a rule-based system. It’s a very strictly defined way of automating tasks, and, as such, can’t handle complex, unstructured processes.
Agentic AI, on the other hand, enables dynamic and context-aware automation of agentic frameworks. It can make decisions autonomously and in real time. Moreover, it can quickly adapt to changes in the environment or unexpected inputs.
It is important to note that agentic automation doesn’t replace RPA, but complements and enhances it. This is a collaborative ecosystem, where AI agents, RPA robots, and people work together.
The Benefits of Automation Using Agentic AI
Better Automation Capabilities
Agentic AI can be used to automate complex tasks that are outside the scope and capabilities of RPA, including those that require precision, creativity, and adaptability. It combines LLMs for flexibility and human-like reasoning with traditional programming, allowing it to operate independently and carry out multi-step tasks without requiring human oversight.
Improved Efficiency and Productivity
Since agentic AI can handle more decision-intensive tasks, people are free to take on strategy, problem-solving, and real interactions with customers. AI agents are faster and more efficient at what they do and can work 24/7.
Enhanced Customer Experience
AI agents can learn about user preferences to infer intent and anticipate needs, and can offer personalized solutions at any time, day or night, showcasing the use case for AI capabilities in customer service. Since they have access to customer data and are constantly learning “on the job,” they can provide high-quality interactions for better engagement.
Intuitive Interfaces
Agentic automation uses natural language interactions to make user interactions simpler. The person can simply request the information they need instead of having to navigate through menus or datasets.
Scalability and Adaptability
Since evolution is a built-in feature of agentic AI, agents can adapt to new data, refine strategies, and improve over time. They can access, retrieve, process, and analyze real-time information to deliver informed and relevant outputs. And since they can proactively monitor systems, they can adjust their strategies to keep up with any changes in their environment.
Better Collaboration Between Humans and AI
Agentic automation creates a symbiotic ecosystem where AI, robots, and humans come together to deliver better results. Each component plays to its strengths, helping improve efficiency and productivity.
What’s Agentic AI Useful For?
Customer Support: For round-the-clock personalized assistance, or limited to times during high call volumes, where AI agents provide answers to questions and recommendations based on past interactions with the customer.
Content Creation: To generate high-quality, creative content that saves marketers time, as well as creating and deploying marketing campaigns to reduce production time.
Software Development: To speed up software engineering (by automating tasks like code generation, debugging, and testing) and optimize full lifecycle management, including tasks like systems architecture design, quality assurance, and development pipeline management.
Healthcare: As patient care assistants, administrative relief, and help with drug discovery during research and development.
Finance: For portfolio management, risk management, and creating personalized financial plans.
Logistics and Supply Chain: For real-time optimization of routes, inventory, and delivery schedules, predictive adjustments of bottlenecks and demand fluctuations, and helping businesses respond effectively to disruptions.
Cybersecurity: For continuously monitoring networks to detect anomalies and responding to threats immediately and proactive defense.
Human Resources: To create streamlined recruitment—where AI agents handle candidate screening, interview scheduling, and onboarding processes—and recommend personalized development strategies.
Insurance: Automating the entire claims process—from filing to payout—and communications with customers.
Business Operations: For operational efficiency, where AI agents manage supply chains, forecast demands, optimize logistics, and plan workflows.
Risks and Challenges of Agentic AI
Autonomy and Oversight
While giving AI the power to make decisions helps streamline operations, there is a risk associated with giving unlimited power to machines. That’s why it’s important to balance the autonomy granted to AI with human oversight. This prevents unintended consequences and ensures legal and ethical standards are followed.
Transparency and Trust
The decision-making process of AI can be difficult to understand and interpret, particularly when it involves vast amounts of data. If users and stakeholders cannot understand the “black box” nature of the system, they can’t be sure about its reliability and fairness. It also becomes difficult to detect and manage bias or determine accountability. Building trust in agentic AI systems requires clear processes so it’s easy to verify if the outcomes are fair, unbiased, and as per expectations.
Security and Privacy
To get the most out of your AI agents, you need the system to have access to your business data, some of which can be proprietary or sensitive. Protecting this information is crucial, as breaches or privacy violations can not only erode trust with users but also lead to regulatory fines and penalties. Cyber security and privacy measures are a must to keep this information safe.
Power Your Agentic AI Model With Your Enterprise Data
Connectivity is important for building agentic AI systems. Not only must the LLM be connected to the AI-ready data, but it must also be able to communicate with the specialist models and tools.
The best way to do it is with APIs, and you can use RAW to build, host, and manage them. Connect your enterprise data with your AI solution securely, reliably, and at scale.
