AI Agents
Also known as: LLM agents, Autonomous agents
LLMs that loop: plan → call tools → observe results → repeat until done.
Autonomous systems that use LLMs as reasoning engines to plan, decide, and execute multi-step tasks. Typically combine tool use, memory, and planning loops (e.g. ReAct, function calling).
In practice
The fastest-growing interview area. Expect questions on ReAct vs function calling, planning failures, and how you'd evaluate an agent.
How it compares
Agents are the systems doing the work; MCP is one standard for how they call tools.
Comparisons that include AI Agents
Related topics
Related terms
Prompt Engineering
Crafting the input text so the model produces the behavior you want.
Model Context Protocol (MCP)
An open protocol (from Anthropic) for letting LLMs call tools and read resources from any compatible server.
Chain-of-Thought (CoT)
Ask the model to think step by step before answering. It boosts accuracy on reasoning tasks.
In-Context Learning (ICL)
Show the model a few examples in the prompt and it learns the pattern, no fine-tuning needed.
Function Calling
The model emits structured JSON specifying a tool call instead of free text; the host executes it.
Prompt Injection
Attacker text in user input or retrieved content overrides the model's system prompt or hijacks tool calls.