What is Agentic AI? Everything You Need to Know About

How agentic AI works

Agentic AI tools can take many forms and different frameworks are better suited to different problems, but here are the general steps that agentic systems take to perform their operations.

Perception

Agentic AI begins by collecting data from its environment through sensors, APIs, databases or user interactions. This step ensures that the system has up-to-date information to analyze and act upon.

Reasoning

Once the data is collected, the AI processes it to extract meaningful insights. Using natural language processing (NLP), computer vision or other AI capabilities, it interprets user queries, detects patterns and understands the broader context. This ability helps the AI determine what actions to take based on the situation.

Goal setting

The AI sets objectives based on predefined goals or user inputs. It then develops a strategy to achieve these goals, often by using decision trees, reinforcement learning or other planning algorithms.

Decision-making

AI evaluates multiple possible actions and chooses the optimal one based on factors such as efficiency, accuracy and predicted outcomes. It might use probabilistic models, utility functions or machine learning-based reasoning to determine the best course of 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

After executing an action, the AI evaluates the outcome, gathering feedback to improve future decisions. Through reinforcement learning or self-supervised learning, the AI refines its strategies over time, making it more effective in handling similar tasks in the future.

Orchestration

AI orchestration is the coordination and management of systems and agents. Orchestration platforms automate AI workflows, track progress toward task completion, manage resource usage, monitor data flow and memory and handle failure events. With the right architecture, dozens, hundreds or even thousands of agents could theoretically work together in harmonious productivity.

Examples of agentic AI
Agentic AI solutions can be deployed across virtually any AI use case in any real-world ecosystem. Agents can integrate within complex workflows to perform business processes autonomously.

An AI-powered trading bot can analyze live stock prices and economic indicators to perform predictive analytics and execute trades.

In autonomous vehicles, real-time data sources such as GPS and sensor data can improve navigation and safety.

In healthcare, agents can monitor patient data, adjust treatment recommendations based on new test results and provide real-time feedback to clinicians through chatbots.

AI can streamline supply chain management through process automation and optimization, autonomously placing orders with suppliers or adjusting production schedules to maintain optimal inventory levels.

Challenges for agentic AI systems

Agentic AI systems have massive potential for the enterprise. Their autonomy is their primary benefit, but this autonomous nature can bring serious consequences if agentic systems go “off the rails.” The usual AI risks apply, but can be magnified in agentic systems.

Many agentic AI systems use reinforcement learning, which involves maximizing a reward function. If the reward system is poorly designed, the AI might exploit loopholes to achieve “high scores” in unintended ways.

Consider a few examples:

An agent tasked with maximizing social media engagement that prioritizes sensational or misleading content, inadvertently spreading misinformation

A warehouse robot optimizing for speed that damages products to move faster.

A financial trading AI meant to maximize profits that engages in risky or unethical trading practices, triggering market instability.

A content moderation AI designed to reduce harmful speech overcensors legitimate discussions.

Some agentic AI systems can become self-reinforcing, escalating behaviors in an unintended direction. This issue happens when the AI optimizes too aggressively for a particular metric without safeguards. And because agentic systems are often composed of multiple autonomous agents working together, there are opportunities for failure. Traffic jams, bottlenecks, resource conflicts—all of these errors have the potential to cascade. It’s important for models to have clearly-defined goals that can be measured, with feedback loops in place so models can move ever closer to the organization’s intention over time.

Frequently asked questions about agentic AI

What is the difference between agentic AI and generative AI?
Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals. A generative AI model like OpenAI’s ChatGPT might produce text, images or code, but an agentic AI system can use that generated content to complete complex tasks autonomously by calling external tools. Agents can, for example, not only tell you the best time to climb Mt. Everest given your work schedule, it can also book you a flight and a hotel.

How do I measure the ROI of agentic AI?

Measuring the ROI (return on investment) of agentic initiatives requires a framework that compares total development and implementation costs against benefits such as efficiency gains and revenue growth. This can be tricky, because benefits are often indirect, abstract and may not materialize in the short term.

What happens when an agent gets stuck?

It depends on the type of agent, but fixing a stuck AI agent typically involves terminating the stalled process, clearing the agent’s memory or context, and refining prompt instructions to help the agent avoid getting stuck in logical loops.

How do I integrate AI agents into my existing software?

The process requires identifying specific repetitive workflows ideally suited for automation, setting up secure API connections and beginning a pilot program with AI agent frameworks. Unique agent identity provided by platforms such as IBM Verify is useful in minimizing the security risks that come with integration.

Can we ensure agentic AI is transparent?

Total transparency in agentic AI is difficult to achieve because of the complex nature of how models work, but some level of transparency is often considered an architectural requirement for safe and effective deployment in enterprise contexts. An IBM Technology Summit focusing on Agent Ops and Responsible AI covered the operational, risk and governance challenges introduced by AI agents.

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