What is an AI Agent?

An artificial intelligence (AI) agent is a system that performs tasks on behalf of a user or another system by deciding on its actions using available tools. IBM’s definition of AI agents describes them as systems designed to simulate intelligent human behavior by making decisions autonomously.

Also, an AI agent is a software entity that uses generative AI techniques to autonomously perform tasks or make decisions based on input from its environment. These agents are designed to achieve specific goals or objectives, often by interacting with their surroundings or other agents. Key characteristics of AI agents include autonomy, adaptability, collaboration, and the ability to learn from experience.

Elements of an Agent

Agent have abilities

Agents are systems equipped with several key abilities. They can perceive and interpret their environment using sensors, data inputs, or similar tools to create an understanding of their surroundings. Based on this perception and their predefined goals, agents can process information and make decisions by selecting appropriate actions. These actions can influence their environment, such as providing recommendations, controlling systems, or engaging with users. Additionally, agents often have the capability to learn and enhance their performance over time by leveraging historical data, past experiences, outcomes of previous actions, or user feedback.

Agents have skills

Agents possess a range of skills that enable them to operate effectively. They can role-play by adopting specific behaviors and objectives tied to a particular role, maintaining focus on tasks without getting distracted by irrelevant or time-consuming detours. Agents utilize tools to access additional information, enhancing their capabilities while minimizing distractions through effective tool use for better concentration. As part of their role-play, they can collaborate with other agents, sharing tasks and information to achieve shared objectives. Operating within predefined guardrails, agents stay grounded, avoiding hallucinations and maintaining focus. Additionally, their use of short- and long-term memory supports better collaboration and enhances learning over time.

Type of agents

• Simple Reflex Agents: React to the environment without using memory.
• Model-Based Reflex Agents: Use internal models to navigate partially observable environments.
• Goal-Based Agents: Make decisions aimed at achieving specific goals.
• Utility-Based Agents: Choose actions that maximize utility while achieving goals.
• Learning Agents: Continuously adapt and improve by learning from past experiences.

Agents in Maximo Asset Management

Using new Agent AI technologies, IBM Maximo can further achieve greater speed and ease of usage with autonomous automation.

• Generative AI reduces reliance on specialized skills not generally available in operational teams and is designed to serve the end user without requiring data science expertise.
• Generative AI can interact with watsonx using natural language for insights, actions and automation.
• AI agents can work together in networks to perform tasks, combine workflows, and automate operational practices.
• AI agents can access the real-time state of equipment using Maximo Monitor, can access historical data in Maximo Manage or stream visual data using Maximo Visual Inspection.
• AI agents can use tools based on multi-modal foundation models for language, vision and time-series.
• AI agents can use the Maximo APIs to interact with Asset Management workflows and create new business objects, like Work Orders.

Generative AI

Generative AI can Search, Analyze, Summarize and Generate content from large quantities of operational data.

• Search for similarities, abnormalities, and relations
• Make computations for data aggregations or statistical properties
• Make summarizations on search results and format results in tables, graphs or text
• Generate reports and apply document styling and formatting using desired natural language

Generative AI can perform, like in Work Order Intelligence, leveraging AI capabilities recommend problem codes for work orders using natural language processing (NLP) and machine learning to analyze historical work orders and identify patterns. Based on this analysis, it recommends a set of problem codes when new work orders are created, enhancing accuracy and data consistency in reporting workflows. By automatically suggesting relevant problem codes, it reduces the time and effort needed by technicians or operators to manually classify issues.