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AI agent

An AI agent is an LLM running in a loop: think → call a tool → observe the result → think again. The loop continues until the task is done or a stopping condition is hit.

Updated May 2026 · 4 min read
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Definition

An AI agent is an LLM running in a loop: think → call a tool → observe the result → think again. The loop continues until the task is done or a stopping condition is hit.

What it means

A minimal agent has: an LLM (Claude Sonnet, GPT-5, etc.), a set of tools the LLM can call (web search, file edit, code execution, MCP servers), and a control loop. Modern frameworks (Claude Agent SDK, OpenAI Agents SDK, LangGraph) handle the orchestration. The hard part is reliability over long horizons — by step 30, the model is reading 80k tokens of accumulated context and prior tool outputs, which most models start to drift on.

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Why it matters

Agents are the post-chatbot interface for AI. Coding agents (Claude Code, Cursor Background, Devin), research agents (Perplexity Pro Search), and operator agents (ChatGPT Operator) all use the same loop pattern. Reliability differences between models compound across 30+ steps — 95% per-step accuracy = 22% end-to-end at 30 steps.

Related free tools

Free toolAI Agent Loop Cost EstimatorAgent loops accumulate context every step. This calculator runs the triangular-sum cost across 7 frontier models so you don't get a surprise bill.Open tool →Free toolAI Agent Platforms Compared10 agentic AI platforms compared: ChatGPT Operator/Atlas, Claude Computer Use, Devin, Manus, Replit Agent, Cursor Background Agents, Bolt.new, v0, Lovable.Open tool →

Frequently asked questions

Best model for agents?

Claude Opus 4.7 leads on long-horizon agentic reliability. Sonnet 4.6 is the cost-effective default. GPT-5 is competitive but drifts sooner past ~30 steps.

Cost?

Agent costs explode with horizon length because context accumulates. Use prompt caching aggressively. Estimate yours with the AI agent loop cost estimator.

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