AI Agent
An AI agent is an LLM-driven system that pursues a goal in a loop — calling tools, observing results, iterating — instead of returning one answer.
An AI agent is a system that uses a language model to pursue a goal in a loop: decide an action, run it through a tool, observe the result, repeat. That loop is the whole distinction from a single LLM call, and it is why agent quality depends on tool design, memory, and termination conditions as much as on raw model intelligence.
An AI agent is a system that uses a language model to pursue a goal autonomously: it decides on an action, executes it through a tool, observes the result, and repeats — a loop, not a single answer.
The loop is the whole distinction. A plain LLM call maps input to output and stops; an agent closes the feedback cycle — run the test, read the failure, edit the code, run it again. That makes agents capable of multi-step work (and of recovering from their own mistakes), and it makes their quality depend on more than the model: tool design, memory, and termination conditions matter as much as raw intelligence.
In practice "agent" spans a spectrum of autonomy — from a function-calling loop with three tools, through coding agents like Claude Code, to multi-agent systems with planners and workers. Frameworks like LangGraph and CrewAI (compared here) supply the orchestration scaffolding; the Model Context Protocol standardizes how agents reach tools.
Frequently asked questions
- What makes something an agent rather than a chatbot?
- The loop. A chatbot maps one input to one output. An agent decides on an action, executes it through a tool, reads the result, and decides again — repeating until the goal is met. Tool use, statefulness across steps, and self-correction against observed results are the defining traits.
- What are the core components of an AI agent?
- A model (the reasoning engine), tools (the actions it can take — search, code execution, APIs), an execution loop that feeds tool results back as observations, and usually memory (context within a task, sometimes persistent across tasks) plus guardrails bounding what it may do.
Filed under
agents · llm · tool-use · autonomy
Related
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