Hands-on beginner roadmap to build, deploy, and monitor real conversational AI agents using prompt engineering, multi-agent orchestration, and production-ready tooling within three focused sessions.
Beginner
72h
ai-agent
prompt-engineering
n8n
openai
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Proof of Competence
0%
0 / 27 checkpoints declared
0/14 evidence needed for 100% — max 50% without proof
Rapid Setup and Prompt Engineering Foundations
You can create and run a prompt-driven workflow that interacts with GPT models through n8n
Milestone
Create a simple n8n workflow to send prompts and receive completions (successfully returns OpenAI response)
Checkpoint
Build a basic chat prompt flow in n8n that echoes user input through GPT to test connectivity
Project
You can design multi-step prompt workflows that extract and refine user intents dynamically
Milestone
Develop and test a prompt chain that extracts user question and requests clarification if needed (tests pass, output logical)
Checkpoint
Multi-Channel Agent Integration
You can deploy a functional Telegram bot that replies with GPT-generated responses live
Milestone
Deploy Telegram bot and validate message roundtrip via n8n workflow (real-time user message reply)
Checkpoint
Develop a Telegram chat agent that can answer FAQs powered by GPT
Project
You can build agents that handle both voice and text inputs on Telegram and WhatsApp platforms
Milestone
Send and receive a voice message on Telegram or WhatsApp, processed through AI generating text response (confirmed end-to-end flow)
Checkpoint
Long-Term Memory with RAG Systems
You can implement session-based memory storage that persists user interactions beyond single sessions
Milestone
Store and retrieve conversation snippets from MongoDB within an agent workflow (verified data integrity)
Checkpoint
Build a simple context storage system that remembers user preferences and recalls them later
Project
You can enhance AI agents to fetch relevant context dynamically using vector search
Milestone
Perform a semantic search query that retrieves context for a given user prompt and feed it back to GPT (successful relevant context return)
Checkpoint
Implement a RAG-powered chatbot that answers complex queries using stored knowledge base and embeddings
Project
Advanced Multi-Agent Systems with MCP and OpenAI Tools
You can orchestrate multiple AI agents working together in a workflow to solve complex tasks
Milestone
Develop a small system where two agents complete a joint task transferring context between them (automated success condition met)
Checkpoint
You can build and debug a custom MCP server managing agent workflows tailored to specific production needs
Milestone
Deploy a custom MCP server and verify agent workflow runs including detailed trace logs (successful inspection)
Checkpoint
Create a minimal MCP server that intercepts and logs all agent requests for analysis
Project
Production Readiness: Monitoring, Tracing, and Deployment
You can trace every token and request across your system and proactively detect failures before impacting users
Milestone
Set up a monitoring dashboard tracking agent usage metrics and error logs (operational with live data)
Checkpoint
Implement logging middleware that captures full request-response chains for troubleshooting
Project
You can deploy a fully functioning AI agent multi-channel system with persistent memory and multi-agent orchestration live in production
Milestone
Deploy the complete AI agent system and verify live interactions on Telegram and WhatsApp including persistent memory recall and multi-agent orchestration (successful end-to-end demos)
Checkpoint
Launch your multi-channel AI assistant and demonstrate production readiness with live user messaging, voice inputs, and RAG memory retrieval
Project