What is AGENTS?
Quick definition
Autonomous software entities that perceive their environment, make decisions, and take actions to accomplish specific goals with minimal human intervention.
Agents are intelligent systems designed to operate independently within defined boundaries, using perception, reasoning, and action to solve problems. In the context of AI and development, agents can be powered by large language models (LLMs) like Claude, enabling them to understand natural language instructions, decompose complex tasks, interact with tools and APIs, and adapt their behavior based on feedback. Agents form the foundation of agentic workflows where they collaborate with other agents or systems to achieve larger objectives.
Agents differ from simple automation by incorporating decision-making capabilities and contextual understanding. They can handle multi-step processes, manage state across interactions, and respond dynamically to changing conditions. Common agent architectures include autonomous agents that operate independently, human-in-the-loop agents that request approval for critical actions, and subagents that specialize in specific domains. Agents are essential in headless architectures, autonomous coding systems, and multi-agent systems where coordination between multiple specialized agents drives complex outcomes.
Example
How it shows up in practice
A coding agent receives a feature request, breaks it into tasks, writes code, runs tests, commits changes, and reports status—all without manual intervention between steps.
Frequently asked questions
How do agents differ from traditional automation?
Agents use reasoning and decision-making to handle unpredictable scenarios, while traditional automation follows fixed rules. Agents adapt to new situations and can use tools dynamically.
What role does the model context protocol (MCP) play with agents?
MCP enables agents to discover and use external tools and data sources reliably. It standardizes how agents interact with capabilities, ensuring consistent communication.
Can agents work together in teams?
Yes. Multi-agent systems allow specialized agents to collaborate through message passing or orchestration layers, dividing work and combining results to solve larger problems.
What is a human-in-the-loop agent?
A human-in-the-loop agent pauses at critical decision points to request human approval before proceeding, balancing automation with oversight and control.