An autonomous software system that uses large language models to reason, plan, and execute multi-step tasks by interacting with tools and data sources.
Definition
An AI agent is a software system that uses a large language model (LLM) as its reasoning engine to autonomously plan and execute multi-step tasks. Unlike a simple chatbot that responds to individual prompts, an agent can decompose a complex goal into subtasks, select and invoke tools (APIs, databases, code execution), evaluate intermediate results, and iterate until the goal is achieved. Agents operate with varying degrees of autonomy — from fully supervised (human approval at each step) to fully autonomous (executing complete workflows without intervention).
Origin
The concept of software agents predates LLMs by decades, originating in AI research from the 1980s and 1990s with systems like the Belief-Desire-Intention (BDI) model. The modern LLM-powered agent emerged in 2023 with systems like AutoGPT, BabyAGI, and LangChain's agent framework, which demonstrated that LLMs could serve as general-purpose reasoning engines for tool use. The agent paradigm accelerated with the introduction of function calling in GPT-4 and tool use in Claude, which provided structured interfaces for LLMs to invoke external tools. Current agent architectures range from simple ReAct-style loops to complex multi-agent hierarchies with orchestrators, subagents, and specialized skills.
Applications
Ecosystem
Related
Coordinating multi-step AI workflows — from single-agent task execution to multi-agent fan-out with parallel tool calls.
Distributed tracing for multi-agent AI systems — following a request from user input through orchestration, tool calls, and response synthesis.
Monitoring, tracing, and understanding AI agent behavior in production — from token usage to decision quality.
A taxonomy of how AI agents fail in production — from hallucinations and tool misuse to cascading failures in multi-agent systems.