Rather than a disconnected exercise per topic, the programme grows a single realistic example that deepens each day — documents, then decisions, then actions, and finally a workflow that runs by itself. The scenario is set to your own domain during scoping, and Day 3 moves onto each participant’s real work.
What Each Participant Can Do Afterwards
Every outcome is something they will have built themselves, not watched someone demonstrate
- Tell AI, generative AI, AI agents and agentic AI apart, using an example from their own work.
- Use RAG to get grounded, cited answers from their own documents.
- Write instructions that keep an AI agent inside its reference data instead of guessing.
- Build and chain several AI agents that hand work to one another.
- Make an automation write to a real system of record, not just print an answer.
- Add branching so a workflow handles the exceptional case, not only the happy path.
- Trigger a workflow on a schedule, a form, an email or a webhook — with nobody watching.
- Put a human approval step in front of anything consequential.
Curriculum — Day by Day
Timed to an eight-hour day with 90 minutes of breaks — 390 minutes of instruction per day, roughly 75% hands-on. Concepts appear only where they are needed to make the next build work.
Grounded answers — GenAI and RAG, hands-on
60 + 90 + 105 + 135 = 390 minOrientation, and the confident wrong answer
AI, generative AI, AI agents and agentic AI told apart on an example from your own operation, then why a fluent answer is not a correct one.
Prompt and context engineering
Role, context, constraints, output shape and examples — turning a vague request into one that returns the same usable result every time.
RAG — answers grounded in your own documents
Retrieval-Augmented Generation with no code, in NotebookLM: upload a document set, read the citations, see the honest “not in the sources” refusal, then add the missing document and watch the answer change.
n8n foundations, and your first AI agent
Workflows, nodes, triggers and saved credentials. The AI agent node and the LLM behind it. Everyone builds a working single-purpose agent that refuses out-of-scope questions.
AI agents that work together
90 + 120 + 105 + 75 = 390 minA second agent, and structured output
Why fixed fields matter the moment anything downstream consumes the answer, and how free text quietly corrupts a spreadsheet or a ticket queue.
Agent orchestration — handing work along a chain
Chaining AI agents on one canvas and passing data between them, including the referencing details that catch everyone the first time.
Conditional logic and branching
Conditions, filters and routing — urgent items down one path, routine down another. The biggest single difference between a demo and something a team relies on.
Taking a real action
Connecting to a system of record and writing to it — access, field mapping, and why a durable log beats a notification email.
Automation that runs by itself
90 + 90 + 105 + 105 = 390 minTriggers — starting without a human
Running on a schedule, on a submitted form, on an incoming email or message, or on a webhook from another system. The step that turns an assistant into an automation.
Your own RAG pipeline inside the workflow
The grounded answering from Day 1, rebuilt inside n8n with a vector store, so it runs on your documents and your schedule inside your own automation.
Approval, failure, cost and data safety
Human-in-the-loop approval before anything commits. Partial runs, duplicates and silent failures. Usage quotas and running cost. What must never be put into a hosted tool.
Build your own, and know where to stop
Supported build time on a real task from each participant’s role, closing on the four lines — volume, auditability, latency and data sensitivity — where a workflow belongs with engineering.
Outcomes, Audience and Technologies
Each participant leaves with
- A grounded RAG workspace built on their own team’s documents
- A multi-agent workflow running end to end that writes a real record
- One automation for their own recurring task, on a trigger, with approval and error handling
- A written manual with every step and instruction, so it can all be rebuilt independently
The organisation gains
- Automation capability outside the engineering queue — the people closest to a process can improve it
- Working automations on day three, not a report recommending them
- A shared understanding of what is safe to put into an AI tool and what is not
- A clear boundary for escalating to engineering, and a handover they will accept
Who this is for
People who do not write code — operations, business analysis, finance, HR, customer support, quality, project management and team leadership. Comfort with a browser, email and spreadsheets is all that is assumed.
It suits organisations that want automation capability distributed across the business rather than concentrated in engineering.
Technologies and topics covered
Low-code AI · no-code automation · n8n · NotebookLM · RAG (Retrieval-Augmented Generation) · AI agents · agentic AI · LLMs (large language models) · GenAI (generative AI) · prompt engineering · context engineering · grounded answers and citations · hallucination control · structured output · agent orchestration · multi-agent workflows · vector store · document search · conditional logic and branching · webhooks · scheduled triggers · human-in-the-loop approval · error handling · API credentials · spreadsheet and system-of-record integration · workflow monitoring · usage quotas and cost · AI data safety and governance
Delivery
On-Site Corporate
- Three consecutive days at your offices
- Hands-on groups of five to six
- A second facilitator for larger cohorts
- Worldwide — Asia-Pacific, the Middle East, Europe and the Americas
Live Online
- Six half-days across two to three weeks
- Same content and same labs, with better attendance and retention
- Breakout rooms for every lab
- Any time zone
Practicalities
- Nothing to install — everything runs in the browser
- Delivered in English, paced for cohorts working in a second language
- Labs run on your own documents and processes where access allows
- A short readiness call and a simple setup checklist before delivery
Frequently Asked Questions
Do participants need to know how to code?
No. The entire programme is low-code and no-code, built in n8n and NotebookLM in the browser. Comfort with a browser, email and spreadsheets is all that is assumed.
How many days is the low-code AI automation training?
Three days, timed to an eight-hour day with 90 minutes of breaks - 390 minutes of instruction per day, roughly 75% of it hands-on.
What is n8n, and why use it?
n8n is a visual workflow automation tool that runs in the browser. It lets non-programmers build AI agents, connect them to business systems, and run automations on a schedule or a trigger - without writing code or managing servers.
Does this cover RAG?
Yes. Day 1 covers retrieval-augmented generation hands-on in NotebookLM, so participants experience grounded, cited answers over their own documents. Day 3 rebuilds that as a RAG pipeline inside their own n8n workflow using a vector store.
Who should attend?
People who do not write code - operations, business analysis, finance, HR, customer support, quality, project management and team leadership. It suits organisations that want automation capability distributed across the business rather than concentrated in engineering.
Can it be delivered remotely?
Yes. Remote delivery runs as six half-days across two to three weeks rather than three consecutive full days - the same content and labs, with better attendance and retention.
What does each participant leave with?
A grounded RAG workspace on their own documents, a multi-agent workflow that writes a real record, one automation built for their own recurring task running on a trigger with approval and error handling, and a written manual to rebuild it all.
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