Retrieval Agent
An agent that specializes in finding and synthesizing information from knowledge bases, databases, documents, and external sources. Retrieval agents combine search strategies with reasoning to locate relevant information for complex queries.
Retrieval agents go beyond simple RAG by actively strategizing their search approach. Instead of a single vector similarity lookup, a retrieval agent might reformulate the query, search multiple sources, cross-reference results, follow citation chains, and synthesize findings. The agent decides when it has gathered enough information and when to dig deeper.
For organizations with large knowledge bases, retrieval agents significantly outperform static RAG pipelines on complex questions that span multiple documents or require inference. A customer support retrieval agent might search the knowledge base, check recent bug reports, review the customer's account history, and cross-reference with product documentation to construct a comprehensive answer. The engineering investment is in giving the agent access to diverse search tools (vector search, keyword search, SQL queries, API calls) and clear source metadata so it can prioritize and cite its findings. Monitor retrieval agents for search quality metrics like recall and precision to ensure they are finding relevant information efficiently.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.