Most PMs first encountered AI as a prompt box. The next wave is different. An agentic workflow takes a goal, makes a plan, calls tools, inspects the result, and decides what to do next. It is closer to designing a small employee than writing a prompt. The platforms have caught up: n8n has become the de facto orchestration layer for non-engineering builders (230,000 active users, 186k GitHub stars, MCP support, native AI Agent nodes), Claude Code and Codex run inside n8n workflows for technical reasoning, and Anthropic's Model Context Protocol now has over 8 million server downloads. The PMs who learn to specify and build agentic workflows can ship operational improvements that used to require an engineering ticket. The ones who do not are still asking ChatGPT for help.
The course is also honest about the failure mode. Gartner has warned that over 40% of agentic AI projects will be cancelled by the end of 2027 due to unclear ROI. The reason is rarely the technology. It is missing guardrails, missing evaluation, missing governance. This course teaches the PM how to build agentic workflows that survive contact with production.
Course objectives
Target audienceProduct managers across B2B and consumer who want to extend their range from specifying features to specifying agentic systems. Operations and ops-leaning PMs are an especially strong fit. No coding experience required, but participants should be comfortable with structured thinking, branching logic, and reading JSON.
Prerequisites
Agentic workflows: what they are, what they are not. Plain definition: an agent takes a goal, makes a plan, calls tools, inspects results, and adapts. The architectural choices: single agent, multi-agent swarm, deterministic-plus-AI hybrid, planning-and-execute. The decision rule: give the system the smallest amount of freedom that still delivers the outcome. Walk through three real-world workflows and classify each.
The PM's job in agentic systems. The product manager owns the goal, the success criteria, the human-in-the-loop checkpoints, the rollback plan, and the evaluation. The product manager does not need to write the orchestration code, but does need to specify it precisely enough that another PM (or the engineer the workflow gets handed to) can read it and understand the design intent.
Lab: each participant writes a one-page spec for one agentic workflow they want to build.
Building in n8n. Triggers, nodes, AI Agent node, tool calls, memory, MCP integration. The visual canvas as a literal version of the architecture. Lab: each participant builds the workflow they specified in Module 2, end to end, in n8n. The lab uses a real workflow from the participant's actual work, not a toy example.
Tool design and structured outputs. Tools are the agent's hands. Tool inputs should be strict, validated, and well described. Structured outputs (JSON schemas) prevent schema drift, which is the top cause of broken automations. Anthropic and OpenAI both ship structured-output enforcement out of the box. Lab: each participant adds schema validation to their workflow and watches one error get caught that would have slipped through otherwise.
Guardrails, human-in-the-loop, and evaluation. Three risk tiers: low-risk actions run automatically, medium-risk actions get sampled for review, high-risk actions require human approval. n8n's built-in approval node. How to write an evaluation that runs every time the workflow ships a change. The audit log every regulated environment will eventually ask for. Lab: add at least one human-in-the-loop checkpoint and one evaluation step to the workflow.
Governance, rollback, and handoff to engineering. Workflows that touch production systems need a rollback procedure. Workflows that handle customer data need a governance policy. Workflows that work need to be hardened for engineering ownership. The honest truth about when an n8n workflow stops being good enough and needs to become real software. Lab: each participant writes the governance one-pager, the rollback plan, and the engineering handoff document for their workflow.
Duration: 1 day
Price: 10 900 NOK
Language: English
Format: Classroom, virtual classroom, or in-company
Hva er Agentic Engineering for Product Managers?
Dette kurset gir produktledere en praktisk innføring i agentbaserte AI-systemer og hvordan slike løsninger påvirker produktutvikling, arbeidsflyt og strategi.
Hvem passer kurset for?
Kurset passer for produktledere, Product Owners, prosjektledere og andre som jobber med digitale produkter og ønsker å forstå hvordan AI-agenter kan brukes i moderne produkter og tjenester.
Trenger jeg teknisk bakgrunn for å delta?
Nei, kurset er laget for produktroller og fokuserer på forståelse, samarbeid og produktstrategi fremfor teknisk implementering.
Er kurset praktisk rettet?
Ja, kurset bruker praktiske eksempler og realistiske scenarioer for å vise hvordan agentbaserte AI-løsninger kan brukes i produktutvikling og organisasjoner.
Hva lærer jeg som er nyttig i praksis?
Du lærer hvordan AI-agenter fungerer, hvordan de påvirker produktstrategi og arbeidsprosesser, og hvordan produktledere kan samarbeide med utviklingsteam for å bygge AI-drevne produkter. Kurset dekker også hvordan AI-assistenter og agentiske arbeidsflyter påvirker moderne programvareutvikling og produktarbeid.
