Sathyan Nandagopal

AI Product Leader & Advisor. Shipped 6 AI products; built '5D Method' & Aegis

AI Agents That Won't Wreck Production

Learn the two-layer model—standing rules vs. hard code blocks—that keeps a coding agent in line where a prompt can't

Architecture beats a sharper prompt

See an agent try to delete a live secret file and get blocked by an exit code before it runs—harness, not hope

Watch a deterministic guardrail stop an agent

Get a starter template you can point your coding agent at to add the same hard block in about ten minutes

Ship the same block in your own setup tonight

By the end of this course, you will know how to evaluate an enterprise AI agent from first review to deployment decision.

Ph.D. Scholar, CISSP, CISA, AAIA™, CDPSE

Cyber Pros Training

Centene

ISACA

Trenton Health Team

Western Governors University

François B. Arthanas

Founder of CyberProsAI | Agentic AI Governance Advisor | CISSP, CISA, AAIA™

For professionals who need to govern, secure, test, monitor & prove trust in enterprise AI agents.

Founding Cohort 

August 2026 Cohort

"Can we trust this AI agent?"That question is landing on someone's desk in your organization right now - from the board, legal, privacy, and customer's security team. This course makes sure that when it lands on yours, you have the evidence to answer it.AI agents don't fail like software. An agent that reads data, calls tools, and triggers workflows fails like an employee with system access and no manager. Legal wants proof it can't leak PII. Security wants proof it can't be prompt-injected. Finance wants proof it can't issue an unauthorized refund. A policy answers none of that. Evidence does.Across 8 weeks, you will run a complete, realistic enterprise AI agent review - one layer per week: scope, privacy, security, safety, reliability, accountability, monitoring. You will break the agent with adversarial tests, design the controls, and document the evidence. You'll finish with an 8-artifact Trust Evidence Pack on your public GitHub and defend your deployment decision before a mock executive review board.No coding or ML background needed. Rated 5.0 by students.Don't have an opinion. Have evidence.

Everyone builds AI agents. Few can prove they're safe. Become the one who can.

Scope enterprise AI agents before they become uncontrolled risk

Turn AI agent risks into controls, tests, and evidence

Test AI agents for the failures companies fear most

Build the Trust Evidence Pack enterprises need before launch

Defend an AI agent deployment decision

You do not need:

Coding skills, 

Machine learning experience, 

AI researcher,

Advanced Cybersecurity background

There are NO technical prerequisites

Basic familiarity with GRC concepts risk management, internal controls, compliance frameworks, audit fundamentals

You should have

This course is for the person who will be asked: “Can we trust this AI agent?”

You are a strong fit if your role touches AI Governance, risk, security, privacy, product, vendor review, compliance, audit, and legal.

You are also a strong fit if you know AI agents are coming into your organization and you want to become the person who can evaluate them.

Your agents are already acting. Govern what they do, who is accountable, and how to prove it when something goes wrong.

AI Governance Advisor | Founder, INTLDA

Pfizer

Accenture

Cornell University

S&P Global

Uvika Sharma

Founder, INTLDA | AI Strategist, Governance Advisor | Educator | Keynote Speaker

AI Governance Auditor | AIGP | ISO 42001

Deloitte

Wells Fargo

Evan Benjamin

AI Governance Advisor, INTLDA | AIGP | ISO 42001 Auditor | Algorithmic Auditor

Cohort 1

Build the governance system your organization needs before your next board meeting, your next regulator, or your next incident.AI agents are not coming. They are already inside your organization, making decisions about customers, vendors, employees, and compliance without a human in the loop.Most leaders know this. What they do not have is a governance system built for it. The frameworks organizations rely on were designed for AI that assists. Agentic AI acts. That is a different problem, and it needs a different answer.This course is that answer.Over four weeks, you will work through GUIDED, INTLDA's proprietary framework for governing agentic AI. Six actions. One governance system. Built by you, for your organization, during the course. You will not leave with a certificate and a folder of notes. You will leave with six governance artifacts you can use the week after each session ends.Learn from trusted advisors Uvika Sharma and Evan Benjamin. Together they teach governance as a practice, not a subject. What you build in this course is the beginning of a real governance program, not a hypothetical.

Your AI agents are already acting. Nobody defined what they are allowed to do.

Know what agents are running right now

Define what each agent can and cannot do

Name who is accountable for agent harm

Build an audit trail regulators can read

Build your 90-day governance roadmap

Executives and senior leaders responsible for AI in their organization who need governance that works, not a policy that sits in a drawer

Chief AI, data, and risk officers building enterprise governance programs who need a framework built for agents, not just AI tools.

GCs and compliance officers who need to know what their agents can do, who owns the risk, and how to prove it to a regulator.

Go from building agent demos to shipping production-grade agent systems with the eval rigor, guardrails, and judgment real teams rely on.

GP, Musa Capital · ex-Microsoft operator

Microsoft

LinkedIn

Productiv

Musa Venture Collective

Allen Smith

General Partner, Musa Capital (ex-Microsoft, NFL, Stanford)

Cohort 1 — Summer 2026

There's a canyon between an agent that works in your notebook and one that survives real users, real edge cases, and real money on the line. You've felt it: the demo lands, everyone's excited — then it breaks the moment it hits production, and nobody quite knows why.Crossing that canyon is the single most valuable skill in AI right now. And almost nobody teaches it, because almost nobody has shipped it.Over six weeks, you'll build a production-grade agent system end to end — architecture, tool orchestration, memory, evaluation harnesses, guardrails, deployment. Not a toy. Your own real project, the kind you can demo to a hiring manager, a CTO, or an investor.What makes this different: you're learning from an active investor and former big-tech operator who builds agentic systems as part of how Musa Capital develops its thesis. Not theory from someone who read the papers — the patterns that separate funded companies from prototypes.You'll leave with a system that ships, an eval discipline most teams never build, and the judgment to know what's actually production-ready.

Go from AI demos that impress to agent systems that survive production

Design agent architectures that survive real-world complexity

Build reliable tool use and orchestration

Implement memory and state for long-horizon tasks

Build eval harnesses that catch failures before users do

Add guardrails, governance, and human-in-the-loop control

Deploy, monitor, and position agentic products

Technical Founders — shipping agentic features and need production patterns that scale beyond the demo.

Engineers &amp; Eng Leads — building with LLMs at companies and need governance, eval, and deployment rigor.

Technical PMs — scoping production-grade agents and need to speak the language of what's actually possible.

Everyone is learning to build AI agents. Few can prove one is safe to deploy.

Founder | Agentic AI Governance Advisor

Learn how to govern agentic AI systems before they create risk, fail audits, or get your org in trouble.

AI agents are not chatbots. A chatbot answers a question. An AI agent retrieves customer records, calls tools, triggers workflows, issues refunds, sends emails, and acts sometimes before a human ever reviews the output.That means the question landing on YOUR desk is different:Can this agent leak PII? → Can it be prompt-injected through a poisoned document? → Can it call the wrong tool or the right tool with the wrong authority? → Can it issue an unauthorized refund? → Who owns the risk? And what evidence proves the controls actually work?Most professionals freeze at this question because they've only ever seen AI governance as policy slides and framework acronyms. They've never actually run an agent risk review end to end.In this one-day sprint, you will. You'll take a realistic enterprise AI agent through the complete practitioner loop scope → risk → control → test → evidence → decision and finish the day having written and defended a real deployment recommendation.You will watch an AI agent get broken live. Then you'll learn exactly how to make sure it never happens on your watch.

In one day, you'll run a real AI agent risk review start to finish and leave able to do it again at work, with the templates to prove it.

Scope a real enterprise AI agen a support agent that can access customer data

Break it: Run a live red-team session against the agent (no coding required)

Turn your findings into a one-page Trust Snapshot leadership can act on

Make the call and defend it in a live mock review board

You do not need to be an engineer. You do not need to know how to code. You do not need advanced AI expertise.

There are no strict prerequisites for this course

If you are curious about AI agents and want a practical way to think about oversight, this workshop will meet you where you are and move you

What you do need is an interest in understanding how AI is changing governance

This workshop is for professionals who know AI agents are becoming important and do not want to get left behind.

It is built for GRC, IT, cyber, privacy, legal &amp; consultants who need to understand how agentic AI changes the governance conversation

You should join this workshop if you want to understand how AI agents create risk &amp; how to contribute meaningful value inside your org

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Securing software systems — application security, threat modeling, authentication, and infrastructure protection.

Establishing policies, ethics frameworks, and oversight for responsible AI use in organizations.