An agents.md file tells a coding agent how your project works - build commands, conventions, structure. Here is what it is and why it matters for good results.

An AI agent's input context is assembled from system prompts, tool descriptions, memory, and history. Here is how each part is built and combined on every call.

Prompt engineering crafts the instruction; context engineering manages everything the model sees. Here is the difference and why the focus shifted to context.

The meaning of AI agent shifted from OpenAI's early function-calling framing to a 2025 consensus. Here is how the definition evolved and why it settled.

Cursor Agent and Claude Code are a model plus tools plus a loop. Here is how those two ingredients combine to power real coding agents, walked through step by step.

After years of confusion, a clear definition of an AI agent has settled: a model using tools in a loop to pursue a goal. Here is the current consensus, unpacked.

A loop calls an LLM repeatedly, feeding each result back in. Here is what a loop is in agentic AI and why calling the model many times unlocks complex work.

An LLM calls a tool by generating tokens the application reads as a request. Here is how a model uses tokens to decide when to reach for a calculator or code.

Tools let LLM agents do more than talk: search, run code, call APIs, and change files. Here is what tools are and how they let AI systems take real-world actions.

Inference is how an LLM actually generates a reply, one token at a time. Here is how the token-by-token process works under the hood, from prompt to finished output.