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// Talk · Patricia Juárez Muñoz

How to Ship Ideas
with AI

A practical map for validating, building, and launching products in the AI era, with judgment, quality, and team practice intact.

Validate → Spec → Build → Ship
Agents · evals · security
Big-tech practices
// Who's speaking

Patricia & WonderLead

Patricia Juárez Muñoz, Staff AI Product Engineer at AWS (18+ years in tech). Mentored 50+ professionals, ran AI-literacy programs, EQ Champion, O'Reilly Tech Lead Multipliers contributor.

WonderLead in Tech helps people and teams grow, build, and lead as AI changes how work gets done. Humans first, hype never. Coaching, workshops, AI Builder Stack, REPLAN, and the Practitioner Programme.

Humans first
Ship with judgment
Community + practice
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Script
About

"Quick context so you know who's talking. I build AI products at AWS, and WonderLead is where I teach the human + AI craft of shipping, not tool hype."

// The shift

Everyone is expected
to ship with AI.
Almost nobody was taught how.

1
The era changed faster than the playbooksGeneration, coding agents, and copilots are everywhere, but there's still no clear standard for how to execute with them.
2
Companies often expect teams to "just figure it out"Few give secure defaults, review bars, or shared workflows. People invent private habits, so quality and risk vary wildly.
3
Learning alone hits a ceilingTutorials show tools. They rarely give you a real team, a real product, and a real review where AI is applied end-to-end.
4
Job seekers feel that gap hardestHard to practise cross-functional AI shipping without a team environment that looks like the job.
// Plain language

Two ways people use AI
to build products

Ad-hoc
Ask a chat. Copy-paste. Hope. Save more tools. Ship rarely, or ship something you can't explain or trust.
Systematic
Start from the customer. Write a clear plan. Use AI for speed. Check quality. Keep a human responsible for the bar.

Today is about the systematic path. It's the same loop teams practise in WonderLead's hands-on programmes.

// Before generative AI

How products shipped
before agents

Discover
Research, interviews, problem framing, mostly human cycles.
Specify
PRDs, designs, tickets. Writing was slow; clarity was the bottleneck.
Build
Engineers / designers / PMs in parallel. Specialists owned their lane.
Test & launch
QA, metrics, GTM. Roles stayed relatively separate.

That model still matters. AI didn't remove the steps. It compressed the time between them and blurred who can touch each step.

// Vocabulary · scan the dictionary anytime

Generation vs agents
a quick map

Chat / generation
You ask → model replies (text, code, image). One turn or a thread. You drive every step.
Copilot / IDE assist
AI suggests inside your editor. Great for speed, but you still merge and own quality.
Coding / tool agent
AI can run tools (edit files, browse, call APIs) toward a goal. Needs a clear job + review.
Multi-agent / A2A
Several agents collaborate (research → spec → build → eval). Humans set goals and approve.
QR code for Vibe Coding Dictionary
Vibe Coding Dictionary60+ terms · plain language · ai.wonderlead.tech/workshops/vibe-dictionary
// What changed for teams

Roles still matter.
The edges blur.

With agents, one person can touch more of the product loop. You still specialise. You just stop saying "that's not my job."

PM
Agents draft options & docs. You prioritise and defend the story.
Engineer
Agents write code. You own architecture, security, and merges.
Design / UX
Agents explore directions. You choose, critique, research truth.
Strategy / TPM
Agents synthesise signals. You position, sequence, and land launch.
// Agent hierarchy

Don't give one agent
the whole company.

You
Bar holderGoals, approvals, security, customer promises. Always above the stack
Orchestrator
OrchestratorBreaks work into jobs, routes to specialists, collects results
Specialists
One job eachResearch · Spec · Code · Design · Eval. Independent tasks, clear inputs/outputs
Tools & models
CapabilitiesRight model + right tool for the job, not one default for everything

Why independent tasks? Mixed jobs in one chat pollute context, hide errors, and make review impossible. Separate agents = reviewable contracts.

// Architectures · pick on purpose

Common agent setups
+ right tools

Single agent
You
↓
One agent

Best for small, bounded tasks. Dangerous for end-to-end product work.

Pipeline
Research
↓
Spec
↓
Build

Sequential handoffs. Easy to review each stage.

A2A / multi-agent
Research
Spec
Code
Eval
↔ orchestrator ↔ you

Agents talk to agents (A2A). Needs clear roles + human gates.

QR code for AI Builder Stack
AI Builder StackFind the right models & tools for your stage · ai.wonderlead.tech/builder-stack
// Context hygiene

Give each agent
its own session.

1
One job per sessionDon't mix customer research and production refactors in the same thread.
2
Fresh context for high-stakes workSecurity, privacy, launch copy. Start clean so old noise doesn't leak in.
3
Hand off artefacts, not vibesPass a spec, a checklist, or eval results to the next agent, not "continue somehow."
4
Sandbox vs productionExperiment freely in a throwaway session. Promote only what survived review.
// Weekly operating loop

Agents execute. You think.
The bar holds.

01

Agents execute

Draft, synthesise, generate, first-pass checks: the volume work.

02

You think

Is this right? Safe? What would break? Fast ≠ correct.

03

Bar holds

Approve, revise, or kill. Standards don't drop because AI was confident.

// Public big-tech practices

How the best product orgs
still ship.

Working Backwards
Start from the customer experience, then work back to what you build.
Write, don't slide
Narratives force clarity. Fuzzy thinking shows up in prose.
Small team, clear owner
Few people, high ownership. Someone decides.
Bar Raiser
Independent review. Approve or not yet. No grade inflation.
Bias for action
A good decision now beats a perfect decision never.
Spec before agents
Write what "done" means, then let AI execute against it.
// End-to-end product loop

From idea to launch in four steps

1
ValidateWho is the customer? Why are they in pain? What is the solution? How will we solve it? What defines success?
2
SpecWrite the plan agents can follow: PRFAQ / PRD, acceptance criteria, and reusable prompt playbooks (versioned instructions, like code for how you use AI).
3
BuildSpecialist agents in clean sessions + human review. Includes architecture, UX, security & compliance, and tests.
4
ShipLaunch a focused P1 (priority-1 scope), with metrics, GTM, and a deadline. A demo alone is not a launch.
// Inside the Build step

Security & compliance
are part of building.

1
No sensitive data in prompts by defaultCustomer PII, secrets, credentials stay out unless policy and controls allow it.
2
Human review on security-critical pathsAuth, payments, privacy, access control. Agents draft; humans approve merges.
3
Memory & browser hygieneKnow what is stored and shared. Turn memory off for confidential work.
4
Legal / privacy gates before real usersTerms, consent, attribution before AI output faces strangers.
// Quality before you call it done

Don't ship AI output
you haven't checked.

"AI slop" = confident-looking junk: wrong, vague, unsafe, or inconsistent. It shows up when you move fast without checks.

What is an eval?
A repeatable check on AI output against a standard. Example: "Does the FAQ answer match our policy?" scored yes/no on 20 cases.
Unit tests
Still for code: one function, one behaviour. AI-written code needs them more, not less.
End-to-end tests
User path works: signup → action → result. Catches integration breaks agents introduce.
Human-in-the-loop
You approve high-impact steps. Agents propose; humans decide.

Rule: run checks (evals + tests + review) before you merge or launch, not after users find the mess find the mess.

// Your personal stack

Models + tools + habits

Right models
Pick model by task: deep reasoning vs fast drafts vs code vs image. Don't use one model for everything.
Right tools & libraries
IDE agents, design tools, eval harnesses, deploy. The Builder Stack helps you choose for your use case.
Working Backwards
Customer and success metrics before prompts.
Agent orchestration
Hierarchy · clean sessions · handoffs.
Eval + test loops
Evals for AI quality · unit/E2E for product truth.
Security judgment
Data hygiene · review gates · compliance in Build.
// Things you can use this week

Concrete tips & starters

Design
Stitch / Claude design agents generate directions fast. You choose. Pair with a design critique checklist.
Development
obra/superpowers gives you agentic skills + guardrails so agents follow a method, not free-form chaos.
Self-check
Power User assessment shows whether you're guessing or actually proficient (WonderLead AI Self-Awareness).
Agent template
Write a one-page agent brief: goal · tools allowed · inputs · outputs · stop conditions · human approval points.
Dictionary
Vibe Coding Dictionary for shared language for the room (QR earlier).
Stack finder
Builder Stack quiz for the right models & tools for your stage (QR earlier).
Script
Tips

"If you meant a specific Succulus / design skill by name, drop it in chat and I'll point people there. Default design path we use: Stitch + critique."

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// Where you practise this for real

A 12-week team that
ships with you.

WonderLead Practitioner Programme. You work AI-natively inside a cross-functional pod. Real product. Coach who held the bar at AWS. Artefacts drafted with agents, defended by you.

12 weeks
Pods of 6–8
2–3 h / week
Bar Raiser reviews
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// The 12-week journey

Three phases. One launch.

Wks 1–4
Discovery & DefinitionCustomer · PRFAQ · prototype · launch verdict
Wks 5–9
Strategy, Design & BuildArchitecture · PRD · MVP · usability · security & tests
Wks 10–12
Launch & GraduateMetrics · GTM · 6-pager · Bar Raiser · portfolio
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// Choose how you show up

Primary role.
Extra hats welcome.

PM
Owns PRFAQ & PRD. Defends the narrative in review.
Engineer
AI-assisted MVP. Architecture you can explain.
Designer
System + hi-fi. Critique over generation spam.
UX Research
Real interviews. Agent-themed. Human decisions.
Strategist / TPM
Competitive, GTM, delivery spine.
Mentor
Seniors join free. You hold the bar, coach without taking the work.
Manager / Leader
Practise leading an AI-native team: reviews, standards, people.
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// What you leave with

Shipped proof

1
Portfolio artefacts that survived reviewPRFAQ · PRD · MVP · research · 6-pager · launch narrative
2
An AI-native stack you keepModels, tools, agent habits, evals, security judgment
3
Cross-functional shipping experienceDecisions under a real bar, with a real team
4
A coach + peers who won't let standards slipWeekly live session · async deliverable · peer review
// Next step

Join the
cohort.

Practise the full loop inside a real product team: agents, sessions, evals, security in Build, metrics and GTM in Ship.

Apply for Practitioner
Mentors join free
Limited pod size
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Primary CTA

Practitioner Programme

site.wonderlead.tech/practitioner

Apply now →
// Remember

Ship the idea.
Keep the judgment.

Agents execute. You think. The bar holds.

Patricia Juárez Muñoz
WonderLead
site.wonderlead.tech
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