This intermediate to advanced course develops the skills needed to design, build, integrate, and deploy AI agents using n8n and OpenAI. Participants explore how language models, agent instructions, memory, tools, and enterprise knowledge work together to support intelligent automation.

The course progresses from OpenAI integration and conversational agents to API-connected workflows, retrieval-augmented generation (RAG), advanced orchestration, Model Context Protocol (MCP), autonomous agents, and multi-agent systems. It also covers human approval controls, security, governance, technical evaluation, monitoring, and production deployment.

An integrated capstone brings these capabilities together in an enterprise automation solution that combines knowledge retrieval, tool execution, agent coordination, and human oversight.

Duration 6 Days – 42 hrs.

 

Objectives

• Explain agentic AI concepts and distinguish chatbots, AI assistants, autonomous agents, and multi-agent systems.
• Integrate OpenAI models into n8n workflows using effective instructions and structured outputs.
• Build conversational agents with tools, memory, session handling, and reusable workflows.
• Connect agents to REST APIs, databases, and business applications.
• Develop RAG pipelines using document loading, chunking, embeddings, vector stores, and retrieval.
• Design advanced workflows with task decomposition, tool chaining, retries, recovery, and controlled autonomy.
• Explain MCP architecture and integrate supported MCP tools into agent workflows.
• Coordinate specialized agents through routing, delegation, handoffs, and shared context.
• Implement human approval gates, access controls, validation, and safeguards against agent security threats.
• Evaluate agent responses, retrieval quality, tool selection, workflow reliability, and operational performance.
• Prepare n8n agent solutions for deployment, monitoring, scalability, recovery, and cost control.
• Build an integrated agentic automation solution for a business use case.

 

Target Audience 

• Software developers and AI application developers.
• Automation engineers and workflow developers.
• Solution architects and enterprise integration specialists.
• Technical leads responsible for AI-enabled applications.
• DevOps engineers supporting workflow deployment and operations.
• Technical business process specialists with automation experience.

 

Prerequisites 

• Working knowledge of n8n or a comparable workflow automation platform.
• Familiarity with generative AI, language models, and basic prompting.
• Understanding of REST APIs, HTTP methods, JSON, and authentication concepts.
• Basic programming knowledge, preferably JavaScript or Python.
• Familiarity with databases, application integrations, and data transformation.
• Access to an n8n environment and an OpenAI API account with suitable credentials and usage capacity.
• Basic Docker and cloud deployment knowledge for the production deployment topics.

 

 

Course Outline 

Day 1 — Agentic AI and OpenAI Fundamentals

Module 1 — Understanding Agentic AI

• Introduction to Generative AI
• Generative AI vs. Agentic AI
• Chatbots vs. AI Assistants vs. AI Agents
• Characteristics of an AI Agent
• Goals and Instructions
• Reasoning and Planning
• Knowledge and Grounding
• Tools and Actions
• Memory and Context
• Observation and Feedback
• Human-in-the-Loop
• Conversational vs. Autonomous Agents
• Single-Agent vs. Multi-Agent Systems

 Module 2 — OpenAI Fundamentals 

• Introduction to OpenAI Models
• Understanding Large Language Models
• Tokens and Context Windows
• System and User Instructions
• Prompt Engineering Fundamentals
• Model Selection
• Model Parameters
• Structured Outputs
• JSON Responses
• Reasoning Models
• Model Limitations
• Hallucinations and Grounding
• Responsible AI Usage

 Module 3 — Introduction to n8n 

• What is n8n?
• n8n Architecture
• n8n Cloud vs. Self-Hosted n8n
• n8n User Interface
• Workflows
• Nodes
• Connections
• Triggers
• Actions
• Credentials
• Executions
• Workflow Data
• Expressions
• Environment Variables
• Importing and Exporting Workflows

 Module 4 — Integrating OpenAI with n8n 

• Configuring OpenAI Credentials
• OpenAI Nodes
• Chat Models
• Sending Prompts
• System Instructions
• User Messages
• Processing AI Responses
• Structured Output
• Using AI Output in Workflows
• Error Handling
• Testing AI Workflows

Day 2 — Building AI Agents with n8n

Module 5 — AI Agent Fundamentals in n8n 

• Understanding AI Agents in n8n
• Agent Architecture
• AI Agent Node
• Chat Models
• Agent Instructions
• User Input
• Agent Output
• Agent Execution
• Agent Decision-Making
• Agent Tools
• Agent Memory
• Testing and Debugging Agents

 Module 6 — Designing Effective Agent Instructions 

• Defining Agent Roles
• Defining Agent Goals
• Task Boundaries
• System Instructions
• Tool Selection Guidance
• Knowledge Selection Guidance
• Response Rules
• Constraints
• Handling Ambiguous Requests
• Escalation Rules
• Prompt and Instruction Design
• Common Agent Instruction Mistakes

 Module 7 — Conversation and Workflow Design 

• Conversational Workflows
• Chat Triggers
• User Input
• Workflow Branching
• Conditions
• Switch Logic
• Variables
• Expressions
• Data Transformation
• Workflow State
• Sub-Workflows
• Reusable Workflows
• Error Workflows
• Designing Maintainable Agent Workflows

 Module 8 — Agent Memory 

• Understanding Agent Memory
• Short-Term Memory
• Conversation Memory
• Session-Based Memory
• Persistent Memory
• Memory Storage
• Context Management
• Conversation History
• Memory Retrieval
• Context Window Management
• Memory vs. Knowledge
• Memory Security and Privacy

Day 3 — Tools, APIs, RAG and Knowledge

Module 9 — Giving AI Agents Tools 

• Understanding Agent Tools
• Tools vs. Knowledge
• Tool Inputs and Outputs
• Tool Descriptions
• Tool Selection
• n8n Workflow Tools
• HTTP Request Tools
• Database Tools
• Application Integrations
• Custom Tools
• Tool Permissions
• Tool Error Handling
• Designing Safe Agent Actions

 Module 10 — REST API Integration 

• REST API Fundamentals
• HTTP Methods
• GET, POST, PUT, PATCH and DELETE
• JSON Requests and Responses
• HTTP Headers
• Query Parameters
• Authentication
• API Keys
• Bearer Tokens
• OAuth Concepts
• HTTP Request Node
• Dynamic API Requests
• Processing API Responses
• API Error Handling
• Rate Limits
• Integrating Enterprise APIs with AI Agents

 Module 11 — Retrieval-Augmented Generation (RAG) 

• Introduction to RAG
• Why RAG is Used
• RAG vs. Model Knowledge
• RAG vs. Fine-Tuning
• Documents and Knowledge Sources
• Document Loading
• Text Chunking
• Embeddings
• Vector Representation
• Vector Stores
• Semantic Search
• Similarity Search
• Metadata
• Retrieval
• Grounding AI Responses
• Source Attribution
• Reducing Hallucinations

 Module 12 — Building Knowledge-Enabled Agents 

• Connecting Knowledge to Agents
• Document-Based Knowledge
• Website Content
• Database Content
• Enterprise Data
• Vector Store Integration
• Knowledge Retrieval
• Retrieval Tools
• Query Transformation
• Retrieval Quality
• Combining Knowledge and Tools
• Knowledge Security

Day 4 — Advanced Agentic Workflows, MCP and Autonomous Agents

Module 13 — Agentic Workflow Orchestration 

• Understanding Agentic Workflows
• Deterministic vs. AI-Driven Workflows
• Sequential Workflows
• Conditional Workflows
• Parallel Processing
• Dynamic Decision-Making
• Task Decomposition
• Multi-Step Execution
• Tool Chaining
• Sub-Workflows
• Retry Strategies
• Error Recovery
• Agent Guardrails
• Workflow Observability

 Module 14 — Planning and Reasoning 

• Understanding Agent Planning
• Goal Decomposition
• Task Planning
• Dynamic Tool Selection
• Multi-Step Reasoning
• Execution Planning
• Replanning
• Reflection
• Self-Correction
• Stop Conditions
• Preventing Infinite Agent Loops
• Controlling Agent Autonomy
• Balancing Deterministic Logic and AI Decisions

 Module 15 — Model Context Protocol (MCP) 

• Introduction to Model Context Protocol
• Why MCP Is Used
• MCP Architecture
• MCP Clients
• MCP Servers
• MCP Tools
• MCP Resources
• MCP Prompts
• Tool Discovery
• Connecting AI Agents to External Systems
• MCP vs. REST APIs
• MCP vs. Native n8n Integrations
• MCP Security
• Using MCP with Agentic Workflows

 Module 16 — Autonomous and Event-Driven Agents 

• Introduction to Autonomous Agents
• Conversational vs. Autonomous Agents
• Event-Driven Architecture
• Webhook Triggers
• Schedule Triggers
• Application Events
• Database Events
• Background Workflows
• Autonomous Decision-Making
• Autonomous Actions
• Decision Boundaries
• Approval Requirements
• Guardrails
• Auditability
• Designing Safe Autonomous Agents

 

Day 5 — Multi-Agent Systems, Security, Evaluation and Production

Module 17 — Multi-Agent Systems 

• Introduction to Multi-Agent Systems
• Single-Agent vs. Multi-Agent Architecture
• Agent Specialization
• Supervisor Agents
• Worker Agents
• Agent Delegation
• Agent Handoffs
• Agent Routing
• Agent-to-Agent Communication
• Shared Context
• Shared Knowledge
• Shared Tools
• Multi-Agent Workflow Patterns
• Agent Coordination
• When to Use Multi-Agent Systems
• When Not to Use Multi-Agent Systems 

Module 18 — Human-in-the-Loop 

• Human-in-the-Loop Concepts
• Approval Workflows
• Approval Gates
• Risk Classification
• Confidence and Decision Boundaries
• Escalation
• Sensitive Operations
• Human Review
• Business Rules
• Agent Autonomy Boundaries
• Balancing Automation and Human Control

 Module 19 — Agent Security and Governance 

• AI Agent Security
• Authentication
• Authorization
• API Security
• Credential Management
• Secrets Management
• Principle of Least Privilege
• Tool Permissions
• Data Privacy
• Prompt Injection
• Indirect Prompt Injection
• Data Leakage
• Tool Abuse
• Excessive Agency
• Protecting Sensitive Actions
• Input Validation
• Output Validation
• Audit Logging
• Agent Governance
• Secure Agent Design

 Module 20 — Testing, Evaluation and Monitoring 

• Testing AI Agents
• Agent Test Cases
• Prompt Testing
• Response Evaluation
• Tool Selection Testing
• Knowledge Accuracy Testing
• RAG Evaluation
• Hallucination Testing
• Workflow Testing
• Edge-Case Testing
• Error Simulation
• Execution Logs
• Workflow Monitoring
• Token Usage
• Latency
• Cost Monitoring
• Agent Performance
• Continuous Improvement

 Module 21 — Production Deployment 

• Preparing n8n Workflows for Production
• n8n Cloud Deployment
• Self-Hosted Deployment
• Docker Deployment
• Environment Configuration
• Development, Test and Production Environments
• Credential Management
• Environment Variables
• Webhook Security
• Workflow Versioning
• Error Handling
• Retry Strategies
• Logging
• Monitoring
• Scalability
• Reliability
• Backup and Recovery
• Production Security
• AI Cost Management
• Enterprise Deployment Best Practices