Cohere API for AI Agents

Enterprise AI with RAG and embeddings

Cohere provides APIs for text generation, embeddings, reranking, and retrieval-augmented generation. AI agents can use Cohere for enterprise search, document analysis, and knowledge-grounded responses.

What AI agents can do with Cohere

Structured actions an AI agent can execute through the Cohere API

Action

Description

Inputs

Outputs

chat

Generate a response with optional RAG

message, documents, model, connectors

text, citations, search_results

embed

Generate embeddings for text

texts, model, input_type

embeddings[], meta

rerank

Rerank search results by relevance

query, documents, model, top_n

results[], meta

Use cases for Cohere + AI agents

Enterprise search with semantic reranking
Retrieval-augmented generation for knowledge bases
Document classification and analysis
Multilingual text processing
Citation-grounded responses

How to connect Cohere to an AI agent

1Get your Cohere API key
2Generate an AgentSpec for chat, embed, and rerank actions
3Define RAG workflows with document connectors
4Publish for agent discovery
5Test with your knowledge base

Best practices

✓Use rerank after initial retrieval for better search quality

✓Choose the right embedding model for your use case

✓Include citations in agent responses for traceability

✓Implement document chunking for large knowledge bases

✓Use input_type parameter correctly (search_document vs search_query)

Frequently asked questions

What makes Cohere different for AI agents?+

Cohere specializes in RAG (retrieval-augmented generation) with built-in citation support, multilingual models, and enterprise-grade reranking. Agents get grounded responses with source attribution.

How do AI agents use Cohere reranking?+

After initial retrieval, pass the query and candidate documents to the rerank action. Cohere reorders them by relevance, improving the quality of information the agent uses for responses.

Can Cohere work with existing knowledge bases?+

Yes. Use embeddings to index your documents, then use chat with connectors for RAG. The agent can query your knowledge base and get cited responses.