AI agents

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AI agent

In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation and do not require human prompts or continuous oversight.

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• AI Agents: What They Are and Their Business Impact | BCG

  ## What Do AI Agents Do? AI agents represent a new era in artificial intelligence, far surpassing traditional software. Unlike static tools, these intelligent software agents act as autonomous, decision-making entities. They analyze data, plan tasks, take action, and continuously adapt—often in real time. Here’s what makes them so powerful: [...] AI agents don’t just respond to instructions—they have initiative. They engage with their environment, learning and adapting as they go. AI agents continually collect information from a variety of sources. They use memory and specialized tools to understand what’s happening in their environment and to keep track of important details. AI agents decide on the best course of action by considering goals, roles, and constraints. They can update their plans in real time as things change, making them more adaptable to process change and edge cases than techniques like robotic process automation. AI agents get things done by using connected systems and collaborating with other intelligent agents. [...] AI agent gathers data: On a weekly basis, the agent autonomously gathers and joins marketing data via connected data pipelines. AI agent analyzes performance: The agent performs contextual analysis on the data to understand campaign performance metrics and compare against expectations, receiving business context from an operator when necessary. AI agent offers recommendations: The agent writes a standardized report that proposes optimizations. An operator stress tests and refines the AI agent’s recommendations as needed. AI agent updates platforms: When given human approval, the agent updates media buying platforms with the recommendations. ## How Do AI Agents Work?

• What are AI agents? - GitHub

  AI agents have become essential in modern software development, particularly in supporting development workflows and enhancing security processes. AI agents help with development workflows like: [...] Multiple AI agents can be deployed together to tackle complex tasks. Working together makes AI agents even more effective in software development and other industries. ## AI agents in software development AI agents offer many advantages for developers and organizations, including: [...] ## Best practices for using AI agents AI agents have many benefits, but it's important to use them responsibly. Here are some best practices:

• What are AI agents? Definition, examples, and types | Google Cloud

  Resource-intensive applications – Developing and deploying sophisticated AI agents can be computationally expensive and require significant resources, potentially making them unsuitable for smaller projects or organizations with limited budgets. ## Deploy AI agents for scale and efficiency with Cloud Run AI agents, with their inherent need for flexible compute power to handle reasoning, planning, and tool use, can be an excellent fit for Cloud Run. This fully managed serverless platform allows you to deploy your agent's code—often packaged within a container—as a scalable, reliable service or job. This approach abstracts away infrastructure management, letting developers concentrate on refining the agent's logic. [...] ## How do AI agents work? Every agent defines its role, personality, and communication style, including specific instructions and descriptions of available tools. [...] Collaborating: Working effectively with others, whether humans or other AI agents, to achieve a common goal is increasingly important in complex and dynamic environments. Collaboration requires communication, coordination, and the ability to understand and respect the perspectives of others. Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust their behavior based on feedback, and continuously enhance their performance and capabilities over time. This can involve machine learning techniques, optimization algorithms, or other forms of self-modification.

• Types of AI Agents | IBM

  ## Simple reflex agents A simple reflex agent is the most basic type of AI agent, designed to operate based on direct responses to environmental conditions. These agents follow predefined rules, known as condition-action rules, to make decisions without considering past experiences or future consequences. Reflex agents apply current perceptions of the environment through sensors and take action based on a fixed set of rules. For example, a thermostat is a simple reflex agent that turns on the heater if the temperature drops below a certain threshold and turns it off when the wanted temperature is reached. Similarly, an automatic traffic light system changes signals based on traffic sensor inputs, without remembering past states. [...] Learning agents typically consist of 4 main components: 1. Performance element: Makes decisions based on a knowledge base. 2. Learning element: Adjusts and improves the agent's knowledge based on feedback and experience. 3. Critic: Evaluates the agent's actions and provides feedback, often in the form of rewards or penalties. 4. Problem generator: Suggests exploratory actions to help the agent discover new strategies and improve its learning. For example, in reinforcement learning, an agent might explore different strategies, receiving rewards for correct actions and penalties for incorrect ones. Over time, it learns which actions maximize its reward and refine its approach. [...] At a higher level, goal-based agents drive the factory’s specific goals, such as optimizing production schedules or reducing waste. These agents evaluate possible actions to determine the most effective way to achieve their objectives. Utility-based agents further refine this process by considering multiple factors, such as energy consumption, cost efficiency and production speed, selecting actions that maximize expected utility.

• A Comprehensive Guide to Types of AI and AI Agents | by Pranav Dixit

  A multi-layered classification of AI (capability, functionality, technology). A complete breakdown of AI agents (theoretical foundations, architectural types, and real-world categories). Developer-friendly pseudo-code examples for building different kinds of agents. Visual frameworks and forward-looking insights into how agents are reshaping the future of software. By the end, you’ll have a holistic understanding of AI’s structure, evolution, and where AI agents fit into the bigger picture. > Section 1: Types of AI (By Capability — The Intelligence Scale) This is the most common classification — how AI compares to human intelligence. [...] Simple Reflex Agents → “If condition → then action” rules. Example: Thermostat. Model-Based Agents → Use internal models of the environment. Example: Robot vacuum navigating rooms. Goal-Based Agents → Plan actions to achieve goals. Example: GPS route planner. Utility-Based Agents → Choose the best outcome using utility functions. Example: Stock trading systems. Learning Agents → Improve performance over time. Example: ChatGPT fine-tuned with feedback. > Section 6: Types of AI Agents (Architectural View) From a software design perspective: [...] Core Loop of an AI Agent: 1. Perceive — Gather data (text, image, sensors, environment). 2. Decide — Use reasoning/planning to select actions. 3. Act — Perform actions in the environment (API calls, movement, content creation). 4. Learn — Adapt based on feedback. > Section 5: Types of AI Agents (Theoretical Foundations) Classic AI literature (Russell & Norvig, 1995) defines five major types: