This course develops the knowledge and skills required to design and build enterprise-oriented Generative AI applications, intelligent agents, and coordinated multi-agent systems using n8n and OpenAI. It covers advanced prompt and context engineering, tool integration, conversational memory, retrieval-augmented generation (RAG), workflow orchestration, human approval controls, and production readiness.
Participants learn to connect AI agents with knowledge sources, REST APIs, databases, and enterprise workflows while incorporating validation, security controls, traceability, and failure recovery. The course culminates in an integrated capstone project that combines knowledge retrieval, specialized agents, controlled execution, and audit logging.
Duration 5 Days – 35 hrs.
Objectives
- Distinguish Generative AI applications, deterministic workflows, and agentic architectures.
- Develop reliable prompts, structured outputs, and context-management strategies.
- Build n8n AI agents using OpenAI models, external tools, and conversational memory.
- Implement RAG pipelines using document ingestion, embeddings, vector storage, metadata filtering, and source attribution.
- Integrate AI agents with REST APIs, databases, and enterprise systems.
- Coordinate specialized agents through delegation, routing, shared state, and human approval.
- Apply input validation, tool permissions, injection defenses, error recovery, and audit logging.
- Improve agent reliability, retrieval quality, latency, and resource usage.
- Design and build an integrated enterprise Agentic AI solution.
Target Audience
- AI and machine learning engineers developing LLM-powered applications.
- Software developers and integration engineers building AI-enabled solutions.
- Automation developers and n8n practitioners extending workflows with intelligent agents.
- Data engineers and data scientists implementing enterprise knowledge systems.
- Solution architects and technical leads designing agentic and multi-agent architectures.
- Technical consultants and IT professionals implementing enterprise AI automation.
Prerequisites
- Foundational understanding of AI, Generative AI, and large language models.
- Basic knowledge of prompt engineering.
- Basic experience with n8n or a comparable workflow automation platform.
- Familiarity with REST APIs, authentication, and JSON.
- Basic understanding of databases and data integration.
- Basic programming knowledge is helpful but not required; course development emphasizes low-code visual workflows.
Course Outline
Day 1 — Advanced Generative AI and AI Agent Foundations
Module 1 — Advanced AI and Generative AI Concepts
- Traditional AI, Generative AI, and Agentic AI.
- LLMs, transformers, tokens, context windows, and embeddings.
- Reasoning capabilities, hallucinations, and model limitations.
- Enterprise agent architecture and use cases.
Module 2 — Advanced Prompt and Context Engineering
- Instruction hierarchy; zero-shot and few-shot techniques.
- Prompt chaining, decomposition, and dynamic context.
- JSON structured outputs and schema validation.
- Prompt injection awareness and prompt evaluation.
Module 3 — OpenAI Integration with n8n
- API credentials and model selection.
- Chat Trigger, OpenAI model, and AI Agent nodes.
- Conditional routing and structured response handling.
- Error handling, rate limits, and token usage.
Lab 1 — Advanced AI Business Assistant
Build a chat-triggered workflow that classifies business requests, extracts fields into JSON, routes requests, and handles ambiguous inputs.
Expected Outcome: A working, testable workflow demonstrated to the class.
Day 2 — Intelligent Agents, Tools, and Memory
Module 4 — Agent Execution Architecture
- Agent goals, instructions, tool selection, and execution lifecycle.
- Task decomposition and single-agent design.
- Execution boundaries, loop prevention, and termination.
- Tool failures and fallback strategies.
Module 5 — Enterprise Tool Integration
- HTTP Request tools and REST APIs.
- Authentication, dynamic parameters, and JSON payloads.
- Database and reusable subworkflow tools.
- Input validation, retries, and safe actions.
Module 6 — Conversational Memory
- Session IDs and short-term versus persistent memory.
- Conversation history and context-window management.
- Memory summarization and retrieval.
- Sensitive-data handling and memory quality.
Lab 2 — Enterprise Tool-Enabled AI Agent
Create a customer-service agent with conversational memory, mock CRM lookup, order-status tools, and confirmation before record updates.
Expected Outcome: A working, testable workflow demonstrated to the class.
Day 3 — Retrieval-Augmented Generation and Enterprise Knowledge
Module 7 — RAG Foundations
- RAG versus fine-tuning; ingestion and document preparation.
- Chunking, overlap, embeddings, and similarity.
- Retrieval relevance and grounded generation.
- Handling missing or contradictory evidence.
Module 8 — Vector Search
- Vector database integration with n8n.
- Metadata storage and filtering.
- Semantic retrieval and reindexing.
- Document freshness and versioning.
Module 9 — Knowledge-Aware Agents
- RAG retrieval as an agent tool.
- Combining retrieved context with conversation history.
- Source attribution and evidence-based answers.
- Retrieval and answer-quality evaluation.
Lab 3 — Enterprise Knowledge Assistant
Ingest sample policy documents, embed and store chunks, connect retrieval to an AI agent, and answer questions with source references.
Expected Outcome: A working, testable workflow demonstrated to the class.
Day 4 — Multi-Agent Systems and Enterprise Orchestration
Module 10 — Multi-Agent Architectures
- Single-agent versus multi-agent tradeoffs.
- Supervisor-worker and specialist-agent patterns.
- Delegation, routing, shared state, and coordination.
- Cost, latency, and conflicting-action risks.
Module 11 — Advanced n8n Orchestration
- Modular workflows and subworkflow tools.
- Conditional and asynchronous execution.
- Retries, idempotency, and error recovery.
- Workflow state and execution traceability.
Module 12 — Human Approval and Governance
- Approval gates for sensitive operations.
- Separation of decision and execution.
- Tool permissions, input validation, and injection defense.
- Audit logging and monitoring.
Lab 4 — Approval-Controlled Multi-Agent Workflow
Coordinate request-analysis, knowledge-retrieval, and decision-support agents; route approved requests to a separate execution workflow and log outcomes.
Expected Outcome: A working, testable workflow demonstrated to the class.
Day 5 — Production Readiness, Evaluation, and Capstone
Module 13 — Production Architecture
- Credentials, environment configuration, and webhooks.
- Docker deployment and persistence.
- Concurrency, rate limits, and queue concepts.
- Backup and recovery planning.
Module 14 — Evaluation and Optimization
- Scenario-based and tool-selection testing.
- RAG relevance, grounding, and task success.
- Latency, tokens, cost, and error metrics.
- Execution logs and continuous improvement.
Module 15 — Advanced Design Patterns
- Event-driven and approval-controlled agents.
- Agentic RAG and supervisor-worker systems.
- Reusable workflow components.
- Guardrails and maintainability.
Lab 5 — Enterprise Agentic AI Capstone
Deliver an integrated request-management solution with a chat interface, supervisor, knowledge retrieval, business analysis, human approval, execution tools, and audit logging.
Expected Outcome: A working, testable workflow demonstrated to the class.
Capstone Project: Enterprise AI Service Operations and Request Management System
Participants design a user-facing workflow that classifies requests, consults a knowledge base, recommends actions, obtains human approval for sensitive operations, invokes an authorized API tool, and returns a traceable result.
Required components:
- Chat interface and session context.
- Supervisor and specialist-agent workflows.
- RAG-powered knowledge retrieval.
- Mock CRM or enterprise REST API integration.
- Human approval gate and separate execution workflow.
- Input validation, error handling, and audit records.
- Scenario-based testing and operational metrics.
Recommended Technology Stack
| COMPONENT | TECHNOLOGY |
| Orchestration | n8n |
| LLM and embeddings | OpenAI API |
| Vector storage | Qdrant or Supabase Vector Store |
| Persistent data | PostgreSQL |
| API testing | Postman |
| Deployment | Docker / Docker Compose |
| Data interchange | REST / JSON |
