// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
AI Agent
A computer program that can perceive its environment, make decisions, and take actions to achieve specific goals, often without constant human oversight.
TECHNICAL DEFINITION
An AI agent is a software entity that operates autonomously within an environment, perceiving its state, processing information, and executing actions to achieve predefined objectives, often incorporating components like memory, planning, and tool use.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Autonomous agent
- Intelligent agent
- Software agent
- Goal-seeking AI
USAGE NOTE
AI agents are being developed for tasks ranging from customer service to scientific research.
DEVELOPERS
Organizations developing technology related to AI Agent.
Develops foundational models and tools like the Assistants API and function calling that enable the creation of sophisticated AI agents capable of complex interactions and task execution.
Conducts cutting-edge research and development in advanced AI agents, focusing on areas like autonomous decision-making, robotics, and complex problem-solving across various domains.
Microsoft Research developed AutoGen, an open-source framework that simplifies the orchestration, optimization, and automation of multi-agent conversations for complex AI agent systems.
Provides a popular open-source framework for developing applications powered by large language models, specifically designed to help build sophisticated AI agents by chaining together various components.
Offers a data framework for LLM applications, specializing in connecting custom data sources to large language models, which is crucial for building knowledge-aware and context-rich AI agents.
Focused on building 'universal AI agents' that can learn to perform tasks across any software, aiming to make humans more productive by automating complex workflows.
Develops large language models like Claude, which are leveraged by developers as the core intelligence to build robust, steerable, and reliable AI agents capable of advanced reasoning and interaction.