No-Code AI Building Course

Aug 19, 2025

Overview

This course teaches you how to build powerful no-code AI agents and workflows using Naden, progressing from basic concepts to advanced automations and integrations—no coding experience required.

Course Agenda & Structure

  • The course starts with AI agent fundamentals, then covers foundational concepts in Naden (UI, workflows, credentials).
  • Step-by-step tutorials build practical workflows; more advanced topics (APIs, agent memory, multi-agent systems, and webhooks) follow.
  • Real-world use cases and examples are included, culminating in lessons learned and best practices.

Fundamentals: AI Agents vs. Workflows

  • An AI agent combines a large language model (LLM), memory, and system prompts to act autonomously and make decisions.
  • AI workflows are linear, deterministic processes, ideal for tasks with consistent steps—reliable, efficient, and easy to debug.
  • AI agents are best for non-deterministic, unpredictable tasks requiring autonomous decision-making.
  • Key parts of an agent: Input, Brain (LLM + memory), Instructions (system prompt), Tools (integrations), Output.

Setting Up Naden & User Interface

  • Sign up for a free Naden trial; workspace setup requires minimal onboarding and no billing info upfront.
  • Workflow components: triggers (manual, schedule, webhooks, chat), nodes (actions, data transformation, AI), and credential management.
  • Data moves through nodes with three main panels: input, configuration, and output.

Data Types & JSON

  • Naden supports five data types: string (text), number, boolean (true/false), array (list), and object (structured collection).
  • JSON (JavaScript Object Notation) is used for input/output data and template sharing; it's key-value pairs and widely understood by LLMs.

Step-by-Step Builds: Example Workflows

  • Three core workflows:
    1. RAG (Retrieval Augmented Generation) chatbot using Pinecone (vector database), Google Drive, and Open Router models.
    2. Customer support automation: classifies and responds to emails using AI agents, Gmail, Pinecone RAG, and Open Router.
    3. LinkedIn content creator: automates content research and posting with Tavi, Google Sheets, and AI agents.
  • Example bonus build: Invoice workflow extracts data from PDFs/emails and updates Google Sheets.

APIs & HTTP Requests

  • APIs enable connecting Naden workflows to any external tool; HTTP requests can be GET (retrieve) or POST (submit).
  • To use an API, you typically configure a method, endpoint, query/header/body parameters, and authentication/keys.
  • cURL commands and API docs are used to simplify HTTP node setup in Naden.

Advanced Integrations & Webhooks

  • Webhooks allow external platforms to trigger Naden workflows, sending/receiving data asynchronously.
  • Example integrations: Firecrawl (web scraping), Appify (web actors), OpenAI (image generation), Runway (video), Perplexity (search), AirTable, Superbase/Postgres (memory and vectors).

Agent Architectures & Prompting

  • Multi-agent systems: orchestrator/parent agents delegate to specialized subagents for complex tasks.
  • Agentic workflow patterns: prompt chaining, routing, parallelization, evaluator-optimizer loops.
  • Effective prompting is iterative (reactive) and includes concise instructions, tool lists, examples, and clear output requirements.

Practical Lessons & Best Practices

  • Most “AI agent demos” are proof-of-concept; production requires robust workflows, error handling, and iterative improvements.
  • Plan workflows before building; wireframe and break tasks into small components.
  • Use context, memory, and RAG for reliable agent outputs.
  • Start with simple workflows and scale up to agents and multi-agent systems only when necessary.

Key Terms & Definitions

  • AI Agent — An autonomous system that uses LLMs, memory, and tools to act and make decisions.
  • Workflow — A linear, pre-defined sequence of steps to automate a process.
  • LLM (Large Language Model) — Advanced AI model (e.g. GPT-4) for generating text and reasoning.
  • RAG (Retrieval Augmented Generation) — An architecture where an LLM retrieves external knowledge to enhance answers.
  • Vector Database — Stores semantic representations (vectors) for efficient data similarity search.
  • Credential — Authorization token/API key needed to connect services.
  • JSON — Standard format for representing structured data (key-value pairs).
  • Webhook — A URL endpoint that allows external apps to trigger workflows by sending data.

Action Items / Next Steps

  • Set up a free Naden trial account and complete onboarding.
  • Practice building the three example workflows step-by-step.
  • Configure API credentials for any external services used (OpenAI, Google, Pinecone, etc.).
  • Download workflow templates and resources from the community for further practice.
  • Experiment with prompting, error workflows, and building multi-agent systems.

Full transcript