Deepak Kumar

Founder, DatavedamEdge | Ex-Twilio, Tellius, Zebra | NVIDIA Inception

Ship Production AI Agents: The ADLC Method in 30 Minutes

Spot exactly where agents plug into requirements, design, build, test, deploy and ops

Map your SDLC to agentic workflows

Make every agent action traceable - the question your CIO will ask first

Design audit-ready agent workflows

When a small local model beats a frontier API on cost, latency and data privacy

Right-size models to the job

Learn how to decide where AI adds value, how to build AI tools for your real L&D work, and how to test whether they actually improve it.

Class of September 2026 

World-leading expert in AI + L&D

University of Cambridge

Maven

KPMG

OpenAI

Aula

Dr Philippa Hardman 

Class of July 2026 

Class of October 2026

<h1>Become the person in your L&amp;D team who knows what AI should do — &amp; builds the tools that do it</h1>🏆 Join the #1-rated AI bootcamp for L&amp;D, alongside L&amp;D professionals from organisations including Google, KPMG, LinkedIn, Arizona State University and the UN. _______Most AI training teaches you how to use AI tools or make content faster.This bootcamp teaches you something more valuable: how to look at real L&amp;D work, decide where AI can genuinely improve it, and build something that does.Over 4 weeks, you'll bring your own work into the bootcamp and use it to:✨ Map where AI actually adds value — break down your workflow task by task and identify what AI should take off you 🐒, make faster ⚡, help you do better 💎 or make possible for the first time 👻.✨ Build AI specs, not just prompts — use my Four Rs framework to make AI behave like an apprentice who follows your rules, not an expert who follows its own.✨ Build and test 4 tools for your real work — one each week, with personalised feedback from me on every submission.You'll leave with four working tools, a clear picture of how AI should change your own role &amp; a method you can use for your next 100 AI builds.

"All L&D roles will be AI-enhanced roles in the very near future "

Diagnose where AI would change your impact, not just your speed

Build working AI tools which scale *your* methods & standards

Prove whether it worked, and decide on evidence, not a feeling

Learn how to "keep the judgement" and decide where the human checks belongs

Design AI for your learners, not just for yourself

Become the person your team looks to on AI

Learning Designers, IDs &amp; SMEs — with or without formal ID training: get a reliable method for what to build with AI, how &amp; why

L&amp;D Leaders &amp; Partners — you need to make defensible calls about where AI fits your team's workflow, and to raise quality, not just speed

L&amp;D Ops, Admin &amp; Reporting — many of your tasks are exactly the kind AI can own or draft; learn which ones, and how to automate them safely

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

Netflix

Yale

Hugo Bowne-Anderson

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

August 2026

December 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 is optional. All examples are in the workshop repository, so you can focus on the session and try them afterwards. To run the code live, bring an OpenAI API key with approximately $10 of credit.Very early bird: $750 USD through October 18. Early bird: $850 USD from October 19 through November 15. Full price: $950 USD from November 16.

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

Your team adopted AI. Why hasn’t delivery improved?

Staff Software Engineer

Luis Vieira

Staff engineer

Agentic Engineering 1

<blockquote>AI coding tools make implementation faster. But typing code is only one part of getting software safely from idea to production.Teams still need to turn ambiguous requests into clear plans, give agents the right context, understand how changes affect existing systems, verify that the work is safe, and decide what should reach production.As agents become more capable, the bottleneck shifts. When code is cheap to produce, context, judgment, verification, and coordination become the limiting factors.This requires new agentic engineering practices: structuring work for delegation, giving agents clear constraints, running independent workstreams in parallel, and knowing where human judgment matters most.This workshop gives you a practical method for improving software delivery with agents. You’ll learn to identify SDLC bottlenecks, use multiple cloud agents in parallel, and build the context, guardrails, proof of work, feedback loops, and evals needed to improve them over time.</blockquote>

Learn how to find delivery constraints, design trustworthy agent workflows, and improve them through evidence, feedback, and evals.

Understand the AI coding plateau

Find agent opportunities across the SDLC

Design a trustworthy agent harness

Improve agent workflows over time

Software engineers using AI coding tools looking to improve their team SDLC

Tech leads and engineering managers who want to understand how to increase engineering output using AI

Engineering leaders who adopted AI tools but aren't seeing it in delivery speed or business outcomes and want to understand why and fix it

Go from "nodding along in AI meetings" to the person who writes the spec, 
runs the eval, and owns the go/no-go decision.

Applied AI educator · for the rest of us

Karthik V

Applied AI educator · Forward Deployed delivery 

Cohort 1 — Founding (August 2026)

<pre><code>Your team just got asked to scope an AI feature. Engineering wants requirements. 
Leadership wants a demo. Your QA team wants to know how to test it. And nobody 
has a playbook.

You're a PM, BA, QA engineer, or delivery lead and you're being asked to own 
decisions that didn't exist in your job description two years ago.

The problem isn't knowledge. It's framework. User stories don't work for 
probabilistic systems. Test plans break on outputs that change every run. Sprint 
"done" criteria don't cover an eval phase. And when something ships broken, 
nobody has the vocabulary to describe what went wrong.

This course gives you the framework the AI Development Lifecycle (ADLC) and 
the production tools to apply it on your real use case.

Run a suitability assessment before engineering starts. Write a Specify document 
that constrains every agent. Build an eval program, score it in EvalForge in 30 
seconds, and make a data-backed go/no-go call. Dispatch agents with Grill Skill 
Controlled by your own guardrails. Configure governance in the Admin Console.

Leave with a complete portfolio built on your own project. No code. No ML 
background required.</code></pre>

The AI project playbook for PMs, BAs, and QA already in the room.

Run a suitability assessment before engineering starts

Write a Specify document agents can actually enforce

Design a BMAD multi-agent blueprint

Build an eval program and make a data-backed go/no-go call

Dispatch constrained agents with Grill Skill

Configure production governance and brief your VP

PMs and BAs writing requirements for AI features and finding that user stories don't work for probabilistic systems.

QA engineers and delivery leads whose test plans and sprint "done" criteria break the moment an AI feature enters the sprint.

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

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

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.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Ship Production AI Agents: The ADLC Method in 30 Minutes

What you'll learn

Why this topic matters

You'll learn from

Deepak Kumar