Streaming Agents on Confluent Cloud Quickstart

Build real-time AI agents with Confluent Cloud Streaming Agents. This quickstart includes three hands-on labs:

Lab	Description
Lab1 - Price Matching Orders With MCP Tool Calling	*NEW!* Now using new Agent Definition (CREATE AGENT) syntax. Price matching agent that scrapes competitor websites and adjusts prices in real-time.
Lab2 - Vector Search & RAG	Vector search pipeline template with retrieval augmented generation (RAG). Use the included Flink documentation chunks, or bring your own documents for intelligent document retrieval.
Lab3 - Agentic Fleet Management Using Confluent Intelligence	End-to-end boat fleet management demo showing use of Agent Definition, MCP tool calling, vector search, and anomaly detection.
Lab4 - Public Sector Insurance Claims Fraud Detection Using Confluent Intelligence	Real-time fraud detection system that autonomously identifies suspicious claim patterns in disaster insurance claims applications using anomaly detection, pattern recognition, and LLM-powered analysis.

Prerequisites

Required accounts & credentials:

• 
• LLM Provider: AWS Bedrock API keys OR Azure OpenAI keys - or BYOK
• Lab1 & Lab3: Zapier remote MCP server (Setup guide)

Note: SSE endpoints are now deprecated by Zapier. If you previously created an SSE endpoint, you'll need to create a new Streamable HTTP endpoint and copy the Zapier token instead. See the Zapier Setup guide for updated instructions.

Required tools:

• Confluent CLI - must be logged in
• Docker - for Lab1 & Lab3 data generation only
• Git
• Terraform
• uv
• AWS CLI or Azure CLI tools for generating API keys

brew install uv git python && brew tap hashicorp/tap && brew install hashicorp/tap/terraform && brew install --cask confluent-cli docker-desktop && brew install awscli # or azure-cli

Windows:

winget install astral-sh.uv Git.Git Docker.DockerDesktop Hashicorp.Terraform ConfluentInc.Confluent-CLI Python.Python

🚀 Quick Start

1. Clone the repository and navigate to the Quickstart directory:

git clone https://github.com/confluentinc/quickstart-streaming-agents.git
cd quickstart-streaming-agents

2. Auto-generate AWS Bedrock or Azure OpenAI keys:

# Creates API-KEYS-[AWS|AZURE].md and auto-populates them in next step
uv run api-keys create

1. One command deployment:

uv run deploy

That's it! The script will autofill generated credentials and guide you through setup and deployment of your chosen lab(s).

[!NOTE]

See the Workshop Mode Setup Guide for details about auto-generating API keys and tips for running demo workshops.

Directory Structure

quickstart-streaming-agents/
├── terraform/                          
│   ├── core/                           # Shared Confluent Cloud infra for all labs
│   ├── lab1-tool-calling/              # Lab1-specific infra
│   ├── lab2-vector-search/             # Lab2-specific infra
│   └── lab3-agentic-fleet-management/  # Lab3-specific infra
├── deploy.py                           # Start here with uv run deploy
└── scripts/                            # Python utilities invoked with uv

[NEW!] Streamlined architecture:

• No heavyweight AWS/Azure Terraform providers needed - just LLM API keys generated with one command
• MongoDB is now pre-configured: No need to set up your own MongoDB Atlas cluster anymore - we provide MongoDB endpoints with read-only credentials, pre-populated with vectorized documents so you can get started faster

Cleanup

# Automated
uv run destroy

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