Netlify Tutorial Shows How to Build Real-Time AI Chatbot From One Prompt.

Netlify published a tutorial on building real-time AI chatbots using Agent Runners, the company's AI-assisted development workflow. According to Nahrin Jalal, the post's author, developers can now generate a production-ready chatbot stack from a single natural language instruction. The tutorial claims to eliminate hours or days of traditional setup work by automating inference configuration, streaming logic, backend functions, authentication, and deployment.

Agent Runners generates the infrastructure layer without requiring developers to manually configure Netlify AI Gateway for model inference, Edge Functions for real-time streaming, Serverless Functions for backend logic, or managed authentication with account creation and email verification. The platform handles deployment automatically once the stack is generated.

The tutorial details the exact prompt structure that produced consistent results during internal testing. It also includes CLI equivalents for developers who prefer terminal-based workflows over web interfaces. Follow-up prompts can extend the base implementation with chat history persistence, content guardrails, model selection interfaces, and custom bot personas.

The shift Jalal emphasizes in the LinkedIn post centers on developer workflow: rather than spending time connecting service layers, developers focus on refining application behavior through conversational iteration. Each follow-up prompt adjusts specific functionality without requiring manual reconfiguration of underlying services.

Netlify has been expanding its AI tooling over the past quarter. The company recently added Google Gemini 3.5 Flash to its AI Gateway with zero-configuration integration, and demonstrated prompt-to-deploy workflows entirely within Claude Desktop chat interfaces.

The tutorial covers five distinct infrastructure components assembled by Agent Runners:

• Netlify AI Gateway handles model inference requests without requiring API key management or rate limiting configuration
• Edge Functions manage server-sent event streaming for real-time message delivery with automatic geographic distribution
• Serverless Functions process backend logic including message validation, conversation state management, and external API calls
• Managed authentication includes account creation flows, email verification, and session management without custom implementation
• Platform deployment provisions hosting, CDN distribution, and SSL certificates through a single command

The approach targets developers building customer-facing AI features who want production infrastructure without DevOps overhead. Organizations using tools like Vercel, Supabase, or Railway for similar workflows may find Agent Runners reduces the number of services requiring manual integration.

The tutorial addresses two common production requirements: content moderation and model flexibility. Follow-up prompts can add input/output filters that block or flag specific content patterns. Model selection interfaces let end users switch between different language models, including OpenAI's GPT models and Google's Gemini variants, through a dropdown menu without backend changes.

Custom personas modify chatbot behavior and response style through system-level instructions. The tutorial demonstrates how to adjust tone, domain expertise, and interaction patterns using natural language descriptions rather than complex prompt engineering.

Full implementation details, including the tested prompt structure and CLI commands, are available in the official Netlify blog post. The Agent Runners feature is currently available to all Netlify users through the platform's standard interface.