Identify high-value AI agent use cases
Separate useful automation opportunities from distracting experiments and prioritize work by value, risk, and feasibility.
Nova Core course
Agentic AI for Founders
A practical 10-week course for founders, operators, and lean teams that want to use agentic AI to move faster, reduce manual work, and scale with stronger controls.
What participants will learn
The course connects AI concepts to real business execution: workflow selection, tooling, governance, pilots, measurement, and scaling.
Design safer agent workflows
Define approval gates, tool boundaries, data access, escalation rules, and quality checks before deployment.
Launch a practical pilot
Build a realistic pilot plan with metrics, ownership, training, documentation, and a roadmap for expansion.
10-week curriculum
Week 1 Agentic AI foundations
Lecture: Understand what makes an AI system agentic: goals, planning, memory, tool use, autonomy, constraints, and human supervision.
Lab: Map one business workflow and identify where an agent could safely assist, automate, or escalate.
Week 2 Use cases and opportunity selection
Lecture: Evaluate agentic AI opportunities across sales, operations, customer support, research, finance, product, and internal knowledge work.
Lab: Score three candidate use cases by value, feasibility, risk, and data readiness.
Week 3 Prompting, roles, and task design
Lecture: Design role instructions, task boundaries, input requirements, output formats, and review checkpoints for agent workflows.
Lab: Create a reusable prompt brief for a real recurring task in your company.
Week 4 Tools, APIs, and workflow integration
Lecture: Learn how agents interact with tools, documents, spreadsheets, CRMs, websites, and internal systems.
Lab: Draft a tool-access plan that defines what an agent can read, write, and request approval for.
Week 5 Data, context, and knowledge bases
Lecture: Prepare useful business context through files, structured knowledge, retrieval, examples, policies, and approved source material.
Lab: Build a lightweight knowledge pack for one internal process.
Week 6 Human-in-the-loop controls
Lecture: Set approval gates, quality checks, fallback paths, audit trails, and escalation rules for sensitive work.
Lab: Design a review workflow for an agent that supports customer-facing or revenue-impacting tasks.
Week 7 Risk, security, and governance
Lecture: Identify risks around privacy, confidential data, hallucinations, unauthorized actions, compliance, and vendor lock-in.
Lab: Create a simple agentic AI risk register and mitigation checklist.
Week 8 Pilot design and measurement
Lecture: Define success metrics, baseline performance, adoption requirements, cost tracking, and pilot scope.
Lab: Write a 30-day pilot plan with measurable business outcomes.
Week 9 Productizing agent workflows
Lecture: Move from experiments to repeatable systems: documentation, ownership, QA, training, and operating procedures.
Lab: Turn your pilot into a standard operating procedure and launch checklist.
Week 10 Scaling and roadmap planning
Lecture: Prioritize the next wave of agentic AI opportunities and define a practical implementation roadmap.
Lab: Present a final agentic AI roadmap with use cases, risks, metrics, and next steps.
Assessment and deliverables
| Deliverable | Purpose | Outcome |
|---|---|---|
| Use case scorecard | Choose the right first AI agent opportunity. | Prioritized workflow shortlist. |
| Risk and governance checklist | Reduce operational, privacy, and quality risks. | Clear controls before testing. |
| 30-day pilot plan | Move from concept to measurable execution. | A launch-ready pilot roadmap. |