AI Product Leader & agentic builder

Sathyan Nandagopal

AI Product Leader & Advisor. 6 AI products; '5D Method' ; IDW 2026 Speaker

AI Agents That Won't Wreck Production

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

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

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.

Build and reason about enterprise agents grounded in your team's knowledge base

Agentic Search Expert

Shopify.com

Reddit

Wikipedia

LexisNexis

Apple

Doug Turnbull

Led teams at Shopify, Reddit, Wikipedia

AI Builder/Educator (6 Million Students)

Google

Instagram

OpenAI

Netflix

Yale

Hugo Bowne-Anderson

AI & data engineer, consultant, educator of 6+ million students (ex-Yale)

August 2026

Your company sits on endless reports, documents, and files. Agents leverage your company’s rich volumes of data to support customers and make business decisions.In this course agentic search expert Doug Turnbull partners with AI expert Hugo Bowne-Anderson to create agents that actually work.At the foundation of production agents sit an LLM, a reasoning loop, an agent harness, and a layer of context engineering + retrieval.Research consistently shows AI answer quality depends more on the underlying search than any other factor. We’ll go from the ground up - building an agent from scratch, giving you what you need to make them actually useful with accurate search + retrieval.You will leave with a working e-commerce agent, a retrieval pipeline, a starter eval set, and a deployment-ready architecture you can adapt to your own company data.Coding along live is optional. All examples will be available in the workshop repository, and we recommend focusing on the session and trying the exercises afterwards. If you’d like to run the code live, please bring an OpenAI API key with approximately $10 of credit.All participants will also receive $500 in cloud + compute credits from Modal.

Real agents don’t just chat: they find, organize, and act.


Build agents from first principles

Ground agents in enterprise knowledge

Engineer reliable agent harnesses

Evaluate, automate, and deploy agents

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Search technologists who connect enterprise knowledge with AI systems with RAG + Agentic Serach

See your venture clearly, find what is holding it back, and choose the next move—so AI creates leverage instead of more work.

Systems Architect · Founder, Sysdom AI

THE WORLD BANK GROUP

Carnegie Mellon University

Han Kay

Systems Architect · Carnegie Mellon (MISM) · Triple Master's (MBA, MS) · 30+ yrs

AI gives founders two possible futures. It can multiply company capacity—or multiply the coordination work returning to the founder. If you're building an AI-native or agentic company, adding agents is the easy part. The harder problem is turning their output into leverage without becoming the integration layer for every handoff, decision, and next move. The missing layer is the system the agents operate inside. That is Systems Intelligence.This four-week working lab turns Systems Intelligence into founder practice. Across four focused 90-minute sessions, you will map your venture, identify the active constraint beneath the surrounding noise, commit one bounded next move, and complete a founder-directed operating cycle.Sysdom.ai is the included, supported working environment. It organizes venture context across the Product/Service, Customer, Cash, and Skills Engines, preserves evidence, and records the chosen move. You remain responsible for direction, consequential decisions, and approval boundaries.Leave with a clearer venture state, explicit decision boundaries, one completed cycle, and a repeatable way to increase agent capacity without increasing founder coordination.

AI amplifies everything. Including what’s broken.

See your venture as one system

Build an evidence-backed venture map

Find the constraint behind the noise

Choose one bounded next move

Set founder and agent decision boundaries

Start a guided operating cycle

Bring research, a prototype, operating signals, prior decisions, or a serious plan. No website, revenue, or technical setup required.

Bring one real venture—and some evidence

Founders building a venture who feel complexity outpacing their mental model. You have pieces but no unified operating system.

Founders who hit their first real bottleneck and realized working harder isn't solving the structural problem underneath.

Founders whose AI tools and agents produce more output while every important decision and handoff still routes back through them.

Govern the HR AI tools you already use. Know what's high-risk, build a governance map, and question vendors before you sign anything.

Head of Talent , ex- Novartis | NYU Alum

Novartis

New York University

Aarti Mudaliar

Head of Talent, Novartis | NYU Alumni | Microsoft, AWS & Google certified

Founder, INTLDA | AI Governance Advisor

Uvika Sharma 

Founder, INTLDA | AI Governance Advisor | Keynote Speaker | Cornell Alum

You didn't build the AI. You're still liable for it.McDonald's exposed 64 million applicant records. Samsung lost 270,000 customer records through a vendor's unrotated credentials. Air Canada was held liable for what its own chatbot promised a customer. None of these were AI failures. They were governance failures, and the company answered for it, not the vendor.Under the EU AI Act, NYC Local Law 144, and Colorado's new AI law, you're the deployer of record for every HR tool your team runs, whether you built it or bought it. Most HR leaders confuse responsible AI with AI governance. Responsible AI is the standard: fair, explainable, accountable. Governance is the proof: who signs off, what gets logged, what you hand a regulator when a decision is challenged.This course builds that proof live, on tools your team already uses. You leave with three real artifacts: a risk-tiered inventory, a one-page governance map, a vendor accountability assessment. Built by you, in the room, led by two practitioners with 38 combined years in HR operations and AI governance. Not a certificate. Not a template. A file you can hand your CHRO or General Counsel on Monday.

You didn't build the AI. You're still liable for it. Here's the proof you need.

Know which of your HR AI tools are high-risk, and which aren't

Understand the difference between Responsible AI and AI Governance

Build a one-page governance map for one tool you actually use

Know what your vendors aren't telling you

Understand what the law actually requires of HR right now

Leave with three connected artifacts for one real tool

You're a Senior HR Leader who has been handed an AI mandate with no roadmap and you're expected to have a governance point of view on AI

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

Pre-session Chat and Waiting for Attendees

Introduction to AI Governance and the Danger of Unrestricted Agents

Speaker Introduction and Core Concept: Prompt vs. Harness

Step 1: Context Loaded as a Log (Defining Rules in Versioned Files)

Step 2: Personas (Setting Boundaries and Prohibited Actions)

Step 3: Governed Loops (Managing Iterations and Exit Criteria)

Step 4: Quality Gate (Defining Success Rubrics and Evaluators)

Step 5: Mechanical Hard Block (Preventing Destructive Actions with Live Demo)

Methodology Recap, Repository Access, and Upcoming Sessions

Q&A Session

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.

AI Agents That Won't Wreck Production

What you'll learn

Why this topic matters

You'll learn from

Sathyan Nandagopal