Corporate Agentic AI with n8n Training for Qualcomm
Summary
Case study: a 3-day corporate agentic AI with n8n training for 15 Qualcomm operations staff from 2–4 June 2026 — covering automation, AI agents, webhooks, RAG and multi-agent systems, with six working artefacts shipped in class.
From 2 to 4 June 2026, Tertiary Infotech Academy delivered a three-day corporate agentic AI with n8n training to 15 operations staff at Qualcomm. The cohort built six working artefacts — a QR-driven event form, an AI agent, a webhook-triggered site, a retrieval-augmented chatbot, a multi-agent system, and an image-upload mini project. The class was deliberately hands-on, with every concept pushed to a working n8n workflow before moving on. Book a corporate n8n training for your team →
Why an operations team needed agentic AI training
Qualcomm's operations function sits at the intersection of supplier coordination, internal reporting, and event logistics — the kind of work where repetitive form-filling, status chasing and copy-paste between SaaS tools quietly eats hours each week. The brief was direct: equip the team to design and run their own automations, with AI agents in the loop where rules-based logic stops scaling. The class size was capped at 15 so every participant could ship workflows themselves, not just watch a demo.
n8n was chosen as the platform for three reasons. It is open-source and self-hostable, which matters for a semiconductor business that handles sensitive supplier and engineering data. It speaks every common SaaS API through a node library, so the team is not boxed into one vendor's ecosystem. And it now ships first-class agent and RAG nodes that wrap Anthropic, OpenAI and local models behind the same orchestration layer — exactly the shape of stack an operations team can govern.
What "good" looks like for a 3-day hands-on cohort
Corporate AI training fails when it stays at the slide level. A workable benchmark for a three-day class is six working artefacts shipped, one decisive shift per half-day, and every participant leaving with a workflow they built themselves. The Qualcomm cohort hit that bar — the agenda below reflects what was actually run and deployed in class, not a brochure outline.
Day 1 — n8n automation fundamentals and the first AI agent
The opening session covered n8n's core abstractions — triggers, nodes, items, expressions — and went straight into a real build. Each participant constructed a bowling event registration workflow that exposed an n8n Form trigger, generated a QR code participants could scan from their phone, validated incoming submissions, and wrote the result to a Google Sheet with a confirmation email back to the registrant. A simple build, but it surfaces almost every n8n primitive in one workflow.
The afternoon introduced AI agents. The cohort built the first-agent activity using n8n's AI Agent node — a tool-using assistant wired to a calculator, a current-date utility, and a structured-output parser. The lesson is not that an LLM can answer questions; it is that an agent decides, calls tools, and routes results back into a workflow you control. That distinction is what separates agentic AI from a chatbot wrapper.
Day 2 — Webhooks and Retrieval-Augmented Generation
Day 2 opened on the inverse of Day 1's Form trigger: webhooks. Each participant built a small static website with a contact form and pointed it at an n8n Webhook URL — the site triggers the workflow, the workflow returns a JSON response, the page renders it. The exercise teaches request signing, idempotency keys, and how to keep secrets out of the front end. It is also the same pattern used in production by every lead-capture and "talk to AI" widget on the public internet.
The afternoon was given to Retrieval-Augmented Generation. The cohort ingested PDF documents, chunked and embedded them using n8n's vector store nodes, and built a Q&A workflow that retrieves relevant passages before the LLM answers. We anchored the theory on the agentic RAG vs classical RAG distinction — when a retrieve-then-answer chain is enough, and when you genuinely need an agent in the loop. For an operations team, the answer is usually "classical first, agentic only when the question is multi-hop".
Day 3 — Multi-agent systems and a mini project
The final day stepped up to multi-agent systems. We worked through a supervisor-and-specialists pattern — a coordinating agent that dispatches sub-tasks to specialised agents (a researcher, a writer, a critic) and assembles their outputs. The cohort wired this up in n8n with the AI Agent node nested inside another AI Agent node, using the lower-level model as a tool. It is the cleanest way to internalise why orchestration matters: any single LLM call is brittle; a small team of role-bound agents is far more controllable.
The day closed with the image-upload mini project — a workflow that accepts image uploads through a form, runs them through a vision model for tagging and classification, writes the metadata to a database, and routes the file to an appropriate folder. Hands-on, end-to-end, and the kind of small-but-real automation an ops team can deploy in their own environment the following Monday.
Day-by-day comparison
| Day | Concept | Artefact shipped | What "graduates" can do next |
|---|---|---|---|
| Day 1 AM | n8n primitives + Form trigger | Bowling event form with QR submit | Replace any manual data-entry form with a self-serve workflow |
| Day 1 PM | AI Agent node + tool use | Tool-using assistant | Wrap rules-based logic with an LLM that decides which tool to call |
| Day 2 AM | Webhook trigger from external site | Static site that triggers n8n | Embed AI workflows behind any internal or public web page |
| Day 2 PM | Vector store + RAG | Document Q&A chatbot | Stand up internal knowledge-base assistants over SOPs and PDFs |
| Day 3 AM | Multi-agent orchestration | Supervisor + specialist agents | Decompose complex tasks across role-bound agents instead of one prompt |
| Day 3 PM | End-to-end mini project | Image upload + classification pipeline | Deploy a complete workflow with form, model, storage, and routing |
How we run corporate agentic AI training
Three things keep these cohorts effective. First, cohort size is capped — 15 was the limit at Qualcomm, and we will push back on requests for 30+ because the hands-on ratio collapses. Second, every concept is shipped before the next concept is taught — if a participant has not got the previous workflow running, we pause. Third, the course materials are public on GitHub so participants can re-run every activity after the course and bring colleagues up to speed.
The public version of this curriculum is the WSQ Agentic AI Automation with n8n programme, a SkillsFuture-fundable course delivered through our sister academy. For corporate cohorts we adapt the syllabus to the team's actual SaaS stack — replacing the Google Sheets sinks with their CRM, ERP or ticketing system, and using their own document corpus for the RAG segment. The shape of the three days stays the same; the data and integrations are bespoke.
If your team's pain is closer to bespoke agentic deployments than scheduled training, our AI agent deployment and AI solutions services pick up where the training ends. Book a 30-minute walkthrough of a sample agenda →
FAQ
Does the team need to be technical to take this training?
Not in the traditional sense. n8n is a visual workflow tool — participants drag nodes onto a canvas, fill in fields, and read JSON. We have run this curriculum for ops, compliance, marketing and finance teams. The one skill that helps is a willingness to read and edit small expressions (e.g. {{ $json.email }}). No prior coding background is assumed.
Can the workflows be deployed inside our network?
Yes. n8n is open-source and self-hostable on Docker, Kubernetes or any Linux VM. For regulated industries we recommend the self-hosted route so credentials, embeddings and document chunks never leave your network. n8n Cloud is also an option for less sensitive workloads. We cover both deployment models in the course.
Can we use our internal LLM endpoint instead of OpenAI or Anthropic?
Yes — n8n's AI Agent node speaks to any OpenAI-compatible endpoint, which covers most internal gateways and self-hosted models served through tools like vLLM or Ollama. We will configure the class against your endpoint if you provide credentials before the course.
How is this different from a one-day "intro to AI" workshop?
A one-day workshop is enough to demystify the technology, but not enough to leave participants able to ship. Three days lets us cover agents, webhooks, RAG, and multi-agent patterns at depth, and gives participants enough repetitions to internalise the workflow-building muscle. The Qualcomm cohort built six artefacts; a one-day class would have shipped at most two.
Is this WSQ-claimable?
The public version of the course is WSQ-fundable through the WSQ Agentic AI Automation with n8n listing. For bespoke corporate cohorts we can route the engagement through the WSQ channel if eligibility criteria are met; otherwise it runs as a direct corporate engagement. We will advise based on your team's profile.
What to do next
- Read the related case studies — our UOB n8n training and NTU n8n workshop for adjacent industries.
- Learn on your own time — work through the public WSQ Agentic AI Automation with n8n course or browse the broader AI courses catalogue.
- Buy — if you want this three-day cohort run for your team, request a quote for corporate agentic AI training.