Agentic AI is more than a buzzword, it’s a new way to think about applications. These systems don’t just respond to prompts; they make decisions, take action and collaborate to solve complex problems. From chatbots that can plan tasks to multi-agent frameworks that reason together, the following examples show how developers are already putting agentic AI into practice.
Here’s a tour through some of the most compelling agentic AI examples and tutorials from the SingleStore ecosystem:
Curious what it actually takes to build an agentic AI from scratch? In this agentic AI chatbot tutorial, you'll follow the creation of a chatbot that goes far beyond basic Q&A. It covers how to set up a data pipeline with SingleStore Jobs and expose a hybrid search REST API with SingleStore Cloud functions. In the second part of the agentic AI guide, the bot gains long-term memory, persistent state and goal-setting logic, building an ai agent from scratch in Python Notebooks.
If one AI agent is powerful, what happens when you connect several? In this multi-agent AI app tutorial using AutoGen, you'll learn how to orchestrate specialized agents that communicate, debate and validate each other’s work. It’s a real-world example of collaborative AI that solves problems more intelligently and flexibly.
Of course, agentic AI needs memory and fast access to relevant information. This guide to using a vector database for AI shows how SingleStore can be an AI database and handle embeddings, similarity search and real-time updates to power agents with scalable, high-performance retrieval from unstructured and structured data.
For more advanced systems, this video demonstrates how to build multi-agent RAG systems with LlamaIndex and how agents collaborate across retrieval, summarization and synthesis. It’s not just about fetching content — these agents evaluate source quality and construct thoughtful, accurate responses.
In this enterprise-grade agentic RAG explanation, it walks through deploying production-ready agentic systems with structured handoffs, fallback logic and strict compliance guardrails. This multi-agent RAG enhances real-time AI interactions using advanced technologies like AWS Bedrock and SingleStore.
And if you’re looking to combine cutting-edge LLMs with a private backend, this gen AI app built with Vertex AI and SingleStore walks through an enterprise-grade system using tool-calling agents, RAG and a unified data pipeline. Companies can use Google Vertex AI and SingleStore to build enterprise grade private LLMs apps that are data-aware and custom to their needs.
When your agents are ready for production, you need infrastructure that keeps up. The serverless platform for agentic AI workloads explains how SingleStore Aura’s container-based design automatically scales compute as your agents act, no manual provisioning required.
Agentic AI is here — and it’s already changing the way we build. Whether you’re just experimenting or scaling for production, these examples prove that agents are more than hype. From planning chatbots to collaborative retrieval pipelines, these agentic AI examples show what’s possible today. Dive in, get inspired and start building the next generation of intelligent systems.
