AI Agent
A system that plans and takes actions with tools, rather than only answering.
Overview
An AI agent wraps a language model in a loop: it decides what to do next, calls a tool, observes the result and continues until the task is done. It solves the limitation that a single model response cannot look anything up, run code or change state. It exists because many useful tasks are multi-step and depend on information the model does not have, and the hard parts are not the model but controlling the loop, bounding cost and recovering from a failed step.
Technology Graph
How AI Agent connects to the rest of the stack.
Uses and depends on
- LLM concept Models AI Agent uses LLM High ✓ source
- LangChain framework LLM Frameworks AI Agent uses LangChain Medium ✓ source
- RAG technique Techniques AI Agent uses RAG Medium ✓ source
Applied to
- Agent Memory technique AI Agents Agent Memory used for AI Agent High ✓ source
- AutoGen framework Frameworks AutoGen used for AI Agent High ✓ source
- Constrained Decoding technique Techniques Constrained Decoding used for AI Agent High ✓ source
- Context Engineering technique Prompting Context Engineering used for AI Agent High ✓ source
- CrewAI framework Frameworks CrewAI used for AI Agent High ✓ source
- DSPy framework Frameworks DSPy used for AI Agent High ✓ source
- Function Calling technique Prompting Function Calling used for AI Agent High ✓ source
- Guardrails technique Safety & Alignment Guardrails used for AI Agent Medium ✓ source
- LangSmith tool Evaluation LangSmith used for AI Agent High ✓ source
- Prompt Caching technique Infrastructure Prompt Caching used for AI Agent High ✓ source
- ReAct technique AI Agents ReAct used for AI Agent High ✓ source
- SWE-bench benchmark Benchmarks SWE-bench used for AI Agent High ✓ source
- Structured Output technique Prompting Structured Output used for AI Agent High ✓ source