Why Is Agentic AI Important?
Agentic AI is an AI system that can achieve a certain objective under minimal supervision. It is built using AI agents, which are machine learning models trained to make decisions like humans do to solve problems in real-time. In a multiagent system, each agent carries out a particular subtask that needs to be executed to achieve the goal, and their actions are orchestrated by AI.
Agentic AI is autonomous, goal-driven, and adaptive, unlike traditional AI models, which are predetermined and need human input. The term “agentic” refers to these models’ agency, or their capacity to act independently and purposefully.
What Are the Benefits of Agentic AI?
There are numerous benefits of agentic systems over their generative predecessors, as they are constrained by information in the datasets used to train models.
Autonomous
The most significant development in agentic systems is that they enable agents to act autonomously in executing tasks without continuous human guidance. Agentic systems can also have long-term objectives, perform multi-step problem-solving tasks, and keep a record of the progress made over time.
Proactive
Agentic systems combine the flexible capabilities of LLMs, which can generate responses or actions based on complex and context-specific understanding, and the structured, deterministic, and reliable nature of traditional programming. With this method, agents are able to “think” and “do” more like humans.
LLMs by themselves can’t directly interact with external tools or databases or set up systems to monitor and collect data in real time, but agents can. Agents can search the web, call application programming interfaces (APIs), and query databases, then use this information to make decisions and take actions.
Specialized
Agents can focus on particular tasks. There are some agents that are very basic and just do one simple action, over and over, without fail. Others may use perception and recall to allow them to solve more complex problems. An agentic architecture could involve an “LLM as conductor” approach, where the LLM manages and coordinates tasks and decisions, while other agents handle more straightforward tasks. These architectures are best suited for single-step processes and are subject to bottlenecks. Other architectures are more horizontal, with agents working in harmony as equals in a decentralized fashion, but this architecture can be slower than a vertical hierarchy. Architectures vary by the type of AI that is being used.
Adaptable
Agents can benefit from their experiences, learn from feedback, and change their actions. Agentic systems can be continually improved if they have the right guardrails. Multiagent systems are scalable to be able to eventually manage large-scale projects.
Intuitive
Agentic systems are driven by LLMs, enabling users to interact with them using natural language queries. This translates to replacing all the many UI components that the SaaS platform of your choice has (tabs, dropdowns, charts, sliders, pop-ups, and so on). In theory, it is now possible for any type of software application user experience to be transformed into “speaking” to an agent who could retrieve the necessary data and make decisions on that information. This boost in productivity is hard to overstate, given the time needed for employees to learn and master new interfaces and tools.
How Does Agentic AI Make Decisions?
Agentic AI tools can come in many shapes and forms, and different tools and frameworks work best for various problems, but the general steps that agentic systems take when performing operations are as follows:
Perception
Agentic AI starts with data sensed from the environment, via APIs, databases, or user interactions. In this way, the system will have current information to process and respond to.
Reasoning
After gathering data, the AI analyzes it and identifies valuable insights. It can use natural language processing (NLP), computer vision, or other AI features to decipher the content of a user query, identify patterns, and gain a deeper understanding of the context. This capability enables the AI to make decisions on how to respond to the situation.
Goal setting
AI establishes goals according to predetermined goals or input from the user. It then formulates a plan for how to accomplish those ends, typically with the help of a decision tree, reinforcement learning, or another planning algorithm.
Decision-making
AI considers several potential actions and selects the best one, based on factors like efficiency, accuracy, and the expected outcomes. It may employ probabilistic models, utility functions, or machine learning-based reasoning to decide upon the best action.
Execution
After selecting an action, the AI executes it, either by interacting with external systems (APIs, data, robots) or providing responses to users.
Learning and adaptation
Once an action is performed, the AI will assess the result and use feedback to enhance future choices. The AI continuously updates its approaches based on its experiences through reinforcement learning or self-supervised learning, improving its capabilities in future similar tasks.
Orchestration
AI orchestration is a system of coordinating and managing systems and agents. Orchestration platforms are used to automate AI workflows, track progress towards task completion, manage resource usage, monitor data flow and memory, and handle failure events. Dozens, hundreds, or thousands of agents, in harmony, could work together with the right architecture.
What Are Some Examples of Agentic AI?
Agentic AI solutions can be used in any real-world ecosystem, across any use case in AI. Agents can be embedded in intricate workflows to carry out business processes independently.
- An AI-powered trading bot can analyze current stock prices and economic indicators, conduct predictive analysis, and make transactions.
- Autonomous vehicles can benefit from real-time data sources like GPS and sensor data for better navigation and safety.
- In the medical sector, agents can keep track of patient information, modify treatment suggestions on the basis of fresh test results, and supply real-time feedback to medical practitioners via chatbots.
- In the realm of cybersecurity, agents can be tasked with keeping a close watch on network traffic, system logs, and user activity to identify any suspicious patterns that could signal potential vulnerabilities to malware, phishing, or unauthorized access.
- Process automation and optimization in the supply chain can be optimized with the help of AI, which can automatically order from suppliers or modify production plans to ensure optimal inventory levels.
What Are the Challenges of Agentic AI Systems?
There is a huge potential for enterprises using agentic AI systems. The only upside of their autonomy is that they are autonomous, and that can lead to big problems if agentic systems go “off the rails.” All the usual risks of AI can be exacerbated in agentic systems.
Reinforcement learning (RL) is a method of many agentic AI systems that involves maximizing a reward function. A poorly designed reward system could lead the AI to find loopholes in order to “high score” games in unintended ways.
Let’s take a look at some examples:
- A representative whose job it is to get as many social media users as possible to engage in the most sensational or misleading content, who ends up spreading misinformation.
- A warehouse robot that is programmed for speed and can damage items to be moved.
- A financial trading AI designed to make a lot of cash that trades carelessly and morally dubiously, causing market turmoil.
- Overcensorship is the term used for a content moderation AI that filters out good content because it also filters out bad content.
There are some agentic AI systems that can develop behaviors that become self-reinforcing and go off in unintended directions. The problem occurs if the AI is too aggressive in optimizing for a single metric without protection. Agentic systems, on the other hand, may consist of several autonomous agents, and there are opportunities for failure. All these errors can cascade – traffic jams, bottlenecks, resource conflicts. Clearly defining models’ goals in measurable terms and providing feedback loops to enable the models to approach the organization’s goals over time is crucial.
Frequently Asked Questions About Agentic AI
What’s agentic AI vs generative AI?
Agentic AI is based on generative AI (gen AI) and relies on large language models (LLMs) to operate in dynamic contexts. In generative models, the AI system is focused on generating content from patterns in learned data, while agentic AI takes it a step further by using generative outputs to achieve a specific goal.
An agentic AI system can call outside tools to complete complex tasks that might generate text, images, or code based on a generative AI model, such as OpenAI’s ChatGPT, which is what ChatGPT and the GPT-3 system are designed to do. Agents can, for instance, not only inform you about the optimum time to climb Mt. Everest with respect to your work schedule, but they can also book an airline flight and a hotel for you.
What is the return on investment (ROI) for the use of agentic AI?
To assess the ROI of agentic initiatives, it is necessary to have a framework that calculates the total development and implementation expenses and then compares them with the benefits they bring, which include efficiency, revenue growth, etc. This can be difficult as benefits are often indirect and intangible and may not be realized in the short term, depending on the issue.
What do you do if an agent is stuck?
The approach to fixing a stuck AI agent will vary by agent type, but generally, the process includes terminating the stuck process, resetting the agent’s memory or context, and adjusting the instructions for the prompt to make it easier for the agent to avoid getting stuck in logical loops.
How can I use AI agents in my current software?
It involves identifying the repetitive workflows that are best suited for automation, establishing secure API connections, and starting a pilot program using AI agent frameworks. Platforms like IBM Verify can offer a unique agent identity, which can help reduce the security risks of integration.
Is it possible to make agentic AI transparent?
The transparency of agentic AI is hard to come by due to the complexity of how AI models work, but is often seen as an “architectural requirement” for safe and effective deployment in enterprise applications. An IBM Technology Summit focusing on Agent Ops and Responsible AI covered the operational, risk, and governance challenges introduced by AI agents.
