Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
In this comprehensive guide, you will find a collection of AI agent-related content such as educational explainers, hands-on tutorials, podcast episodes and much more.
An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools. AI agents can encompass a wide range of functionalities beyond natural language processing including decision-making, problem-solving, interacting with external environments and executing actions.
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As the first step in your journey, explore introductory AI agent explainers to obtain a high-level understanding.
AI agents rely on a set of interconnected components that enable them to perceive their environment, process information, decide, collaborate, take meaningful actions and learn from their experience.
Agentic architecture refers to the structure and design of agentic AI frameworks. An agentic architecture shapes the workflow structure to automate AI models within an agentic AI system.
A multiagent system consists of multiple AI agents working collectively to perform tasks on behalf of a user or another system.
Agentic frameworks are the building blocks for developing, deploying and managing AI agents. These software platforms have built-in features and functions that help streamline and speed up the process.
AI agent governance refers to the processes, standards and guardrails that help ensure agentic systems are safe and ethical.
Agentic RAG is the use of AI agents to facilitate retrieval augmented generation (RAG). Compared to traditional RAG systems, agentic RAG allows large language models to conduct information retrieval from multiple sources and handle more complex workflows.
AI agents can be applied to several industries including customer service, human resources, sales, procurement and much more.
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