I help Product Managers break into AI Leadership - no coding required! Ex-Alexa AI Principal Product Manager | Launched 1st GenAI Answers on Alexa | Top 100 Women of the Future Winner | Reforge Instructor | Speaker

Polly Allen has spent over 20 years building and leading software teams, most recently as a Principal Product Manager at Amazon Alexa, where she guided cross-functional science and engineering groups at the cutting edge of AI. She specializes in natural language applications—search, discovery, understanding, and generation—and in 2020, she led the launch of Alexa’s first automatically generated web content summaries (yes, powered by the same transformer technology behind today’s ChatGPT).

But Polly’s story isn’t just about technology. After years in the deeply technical world, she became convinced that the future of AI shouldn’t be shaped only by coders and data scientists. The biggest breakthroughs will come when product and business leaders—those closest to customers, strategy, and impact—have the tools and confidence to lead AI initiatives. That belief drives everything she does today.

Now, through her company AI Career Boost, Polly helps experienced leaders step into AI roles and lead AI product development with confidence—without needing a computer science degree. She also hosts two podcasts, AI Product Leader and AI Afterhours, where she shares insights, stories, and candid conversations about building the future of AI.

An experienced angel investor and past corporate board member, Polly has advised startups, spoken at global conferences, and earned degrees from MIT, the University of Victoria, and UBC. Above all, she’s on a mission to open the doors of AI leadership to a far wider range of thinkers and builders—and to make sure the next wave of AI innovation is driven by leaders who understand both people and technology.

👉 Join Polly’s AI Career Boost Newsletter to stay connected, get behind-the-scenes insights, and explore how you can lead in the AI era.

Polly M Allen

AI Career Boost turned three years old in December 2025. 🎂

Here are the top 3 AI leadership lessons I’ve learned working with thousands of senior product leaders trying to lead AI initiatives — from early use case selection all the way to production.

A lot of them came in quietly worried about the same thing:
“Am I already behind — or am I just missing something everyone else seems to know?”

Here’s what I’ve seen, watching who actually pulls ahead.
1️⃣ Leaders who get hands-on — even a little — end up years ahead of the ones who don’t.

Not because they’re smarter.
Because firsthand experience collapses confusion faster — especially with AI. More so than any other technology I’ve worked with in 20+ years.

2️⃣ The real difference between ‘technical’ and ‘non-technical’ leaders isn’t skill — it’s permission.
Technical leaders don’t wait to be invited.
They poke, test, break things, and learn reflexively.

3️⃣ When leaders get hands-on and bring their teams along, something shifts.
Shared prototypes.
Shared prompts.
Admin access instead of gatekeeping.

AI stops feeling intimidating — and starts feeling collaborative.
That’s the pattern I see again and again.

For leaders who were starting to feel exposed or sidelined, this is often the moment their credibility snaps back into place.

If you want to learn how to lead this way — without becoming an engineer or starting over — I’m teaching a free masterclass on Jan 20:

How senior leaders stay essential in the age of AI
👉 https://lnkd.in/g64gFChW

Grab your seat quickly - they're going fast!

I help Product and Business Leaders thrive in AI Leadership - no coding required! Ex-Alexa AI Principal Product Manager | Launched 1st GenAI Answers on Alexa | Top 100 Women of the Future

If 2025 was about learning what’s ✨possible✨, 
2026 is about making it 💪real💪.

2026 already feels like a builder’s year - for ICs and leaders alike.

Not a year of demos-for-demos’ sake.

Not a year of “AI strategy” decks that never leave the slide.

A year of:
 – shipping real things
 – testing ideas in the wild
 – turning AI curiosity into actual capability
 – building signal, not noise

The leaders who will stand out this year aren’t the ones who know everything about AI.

They’re the ones who can apply it, evaluate it, and make it useful inside real products and teams in real-world situations.

*That’s the work.*

And it’s the most exciting version of this moment.

✨Happy New Year  ✨. What are YOU building in 2026?

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Product managers & leaders, I'm begging you: DON'T LEARN CLAUDE COWORK.

I'm serious. The most dangerous thing about Cowork is that it will make you feel like you've invested in being on the leading edge - and you'll miss out on the real transformation on how work gets done that underlies the buzz about Claude Code.

As a former developer, I've been obsessed with Claude Code for non-coding tasks for at least 3 months, and I've been trying out my workflows in Cowork. Of course it will grow - but in it's current form, it doesn't change the way you work.

The MENTAL SHIFT you go when using Claude Code, from doing work to designing repeatable workflows and systems, is the most important shift you can make right now to understand how Agentic AI will change the face of digital work. 

I'll be breaking down the important differences and how this work relates concretely to product management and leadership tasks in our next AI Career Boost newsletter. Follow for more! 🔔 

AND we'll be diving into this in my next Masterclass, "The Path to AI Product Leadership", happening tomorrow.

Seats are limited!-> https://lnkd.in/g64gFChW

Claude Cowork vs. Claude Code: Why Product Leaders Shouldn't Get Stuck at Cowork

A lot of senior leaders are quietly terrified they’re about to be forced back into IC work.  Especially because of AI.

I hear this a lot right now:

“If I’m not technical enough, am I going to be sidelined — or worse, cut?”

Here’s what’s real: You don’t need to become an engineer. BUT: you also can’t stay purely high-level anymore. This is counter-intuitive for a lot of leaders: you spent years getting out of the execution weeds, right?

In the AI era, leaders lose credibility when they can’t tell what’s real.

The leaders who stay influential aren’t the most technical — they’re the ones who’ve gotten hands-on just enough to:
• ask better questions
• spot bullshit
• understand real constraints
• and translate tradeoffs for their teams

You don’t need years.
You need the right kind of exposure.

I’m breaking this down live on Jan 20 in a free session for senior leaders on staying essential in the age of AI. If you’re ready to find the right balance between leading and building - grab your seat early! 

Https://https://lnkd.in/g64gFChW

graphical user interface, text, application, chat or text message

Excited to be keynoting at the DataConnect Conference West in May!

Please welcome our first Keynote Speaker for DataConnect Conference West 🎤

Polly M Allen, AI Product Coach and Advisor, Founder of AI Career Boost, and a leading voice in making AI careers more accessible. Polly brings deep expertise at the intersection of AI, product, and career growth, and we can’t wait for her to share insights that will inspire, challenge, and empower our community.

This is just the beginning. Stay tuned for speakers.

Your CEO's teenager shipped a working demo over winter break. 

A year ago, "vibe coding" wasn't even a term. Now Replit, Lovable, and Cursor are baseline. And this month, Claude Cowork put serious AI agent power into a simple chat interface anyone can use. 

When everyone can prototype, the credibility gap moves. It's no longer "can you demo?" It's "can you ship?" 

In this week's newsletter, I break down: 
→ The 4 types of prototypes (and why confusing them kills your credibility) 
→ Which tools to use for each 
→ How to articulate the path from idea to production 

(This is based on my favorite tools & stack. What did I miss? What do you prefer and why?)

Plus insights from Sudha Kumar on why your data probably isn't AI-ready.

A year ago, "vibe coding" wasn't even a term - it wasn't coined until February 2025. Now your CEO's teenager is shipping demos over winter break. Repl

Open article: Everyone Can Build a Prototype Now. That's the Problem. by Polly M Allen • 4 min read

Everyone Can Build a Prototype Now. That's the Problem.

I see senior product leaders make the same AI mistakes over and over — and they’re rarely technical. They’re leadership mistakes.

Here are 5 that quietly erode credibility in the AI era:
• Treating strategy documents as proof of execution
 • Bringing a deck to a demo fight
 • Not knowing what kind of prototype to ask for
 • Delegating too many decisions because AI feels “too technical”
 • Ignoring ROI and total cost of ownership beyond the prototype

AI didn’t make leadership harder.
It made execution visible.

The leaders who are winning funding and trust aren’t coding,
they’re showing teams how they expect work to happen.

Slides explain intent.
Prototypes demonstrate judgment.

I break down why these mistakes keep costing leaders credibility, and how to avoid them in the full video here: https://lnkd.in/gmDAqXgF

👉 Join the free masterclass on leading AI from prototype to production. Link in comments.

#AILeadership #ProductLeadership #ExecutiveLeadership #DigitalTransformation

Attention Senior Product Leaders and Executives!

Reimagine your role and stay ahead in the AI era. FREE Masterclass with an Industry Expert in AI Product Leadership.

Here’s what you'll learn:
• How to lead AI initiatives in 2026 effectively - no coding background required.
• Ways to spot high-impact opportunities and avoid the hype.
• How to stand out as an AI leader in any organization or venture.

📅 Thursday, January 29th at 12PM PDT

Don’t miss this chance to elevate your AI leadership journey. Space is limited. Reserve your spot now!

👉 https://lnkd.in/gbf4yX7x 👈

#AI #ArtificialIntelligence #LearnAI #Free #AIProductLeadership #FREEMasterclass

I see you. You have AI product ideas.

You also have back-to-back meetings, a demanding job, and maybe 2 hours of actual energy left at the end of the day.

So the ideas stay ideas. Another week passes. You watch someone else launch the thing you've been thinking about for months.

Here's what changed for me: I stopped waiting for the "perfect time" and started with a framework that actually works.

Tomorrow, Yuying Chen-Wynn and I are teaching it live — for free.

In 45 minutes, you'll:
→ See real case studies of $100K AI products (built by non-technical people)
→ Validate YOUR best idea using our 3-part framework
→ Watch AI tools build actual products in real-time

The window for this opportunity won't stay open forever. The tools that exist today didn't exist 2 years ago.

Nearly 300 people have already signed up.

Thu, Jan 15 @ 12pm PST | Free | Virtual
Grab your seat -> https://lnkd.in/gbRXsuGc

I spent years building AI products inside Amazon — and now I help senior product leaders stay indispensable in the age of AI.

You don’t need to become an engineer.
You do need enough hands-on fluency to ask the right questions, challenge bad assumptions, and lead when things get messy.

If you’re feeling sidelined or second-guessed in AI conversations, it’s not because you don’t belong.

It’s because the bar quietly moved — and no one showed you how to cross it without starting over.

I’m teaching exactly how to do that in a free masterclass, “How Senior Product Leaders are Staying Essential in the Age of AI”. Grab your seat now -> link in comments bellow. #ProductManagement #AIcareers #aiproductmanagement #aileadership #executiveeducation

Product managers & leaders, if you're thinking about investing time to learn Claude Cowork, PLEASE read this first.

I'm begging you: DON'T LEARN CLAUDE COWORK. Invest in trying Claude Code instead.

Cowork is genuinely useful. It's a great executive assistant. But if you stay there, you're missing the bigger shift.

Cowork makes you an AI user. Claude Code makes you an AI builder.

That's not a small difference. That's the difference between delegating a task and delegating a system. It's keeping you in the same mindset that keeps so many aspiring AI enthusiasts in the 'still just using ChatGPT for things' camp.

Cowork will help you work faster. Claude Code will change how you work.

The mental shift from "doing work" to "designing systems that do work" is THE most important shift you can make right now. It's how Agentic AI will change digital work - for everyone.

I broke down 4 scenarios showing exactly where the gaps appear, why they matter for product leaders, and how to get started. 

https://lnkd.in/eEySajBm

I hope you'll believe me, and thank me later. 
I hope you'll share for anyone in your circle who wants to be vastly ahead of the Claude Cowork crowd in 2-3 months' time. 
And I hope you'll help me spread this, because of how much it matters to product leaders trying to stay relevant today. Don't get stuck in the non-builder trap.

(And because it's at least one post today NOT about Moltbot!)

Claude Cowork vs. Claude Code for Product Leaders

Unlock the full potential of AI in your product management leadership journey. 

Here’s what you'll learn:
🟣 What does it really mean to lead AI initiatives in 2026?
🟣 How do you identify high-impact AI opportunities and avoid overhyped distractions?
🟣 What’s the fastest way to become the go-to AI leader in your organization – or build projects that truly stand out?

Join Polly Allen, the trailblazer behind the first generative AI answers on Amazon Alexa in 2020, for a free webinar.

📅 Thursday, January 29th at 12PM PDT

⏳ Spots are filling fast — grab yours now! ⬇️
🔗 https://lnkd.in/gbf4yX7x

#AI #ArtificialIntelligence #LearnAI #Free #AIProductLeadership #FREEMasterclass

Open article: Registration Page - The Path to AI Product Leadership WEBINAR 2026 by aicareerboost.com

Registration Page - The Path to AI Product Leadership WEBINAR 2026

Claude Cowork is powerful. But it's no Claude Code.

I've been obsessed with using Claude Code for non-software workflows since November. As a former developer, a lot of things just clicked: managing cascading context in folders, overt context clearing/management/compacting, creating repeatable shortcut commands on the fly as you discover you're doing things more than a few times, easy selective sync to git hub for version control for reversions if you go off on a tangent you don't like later.

I like Cowork and frankly, I'm not in love with working in the terminal -  but I miss too many things.

(Also, how much did Microsoft and Apple fumble the ball, if we're all using Claude Cowork to organize our desktop folders instead of something they should have built??)

Agree? Disagree? What's your favorite use case so far for either Claude Code or Claude Cowork?

It’s easy to overlook a prototype. It is hard to guide it to production.

That execution capability is what separates a standard PM from an indispensable AI leader.

You don’t need to code to do it; you need the right strategic framework.

Join Polly Allen (former Amazon Alexa Principal PM behind its first generative AI launch) to learn how to lead this transition.

We’ll cover:
🟣 The "Prototype to Production" Gap: How to guide teams from the sandbox to the market.
🟣 The Right Altitude: How to lead AI initiatives without getting lost in the technical weeds.
🟣 The Indispensable Factor: Moving from managing features to orchestrating AI business value.

📅 Tue., Jan. 20 at 12pm PT | 3pm ET. Space is limited. Reserve your spot now.

🔗https://lnkd.in/gASfwZWc 👈

#AI #AILeadership #ProductLeadership #AICareer #LeadershipDevelopment

If you’re building with AI but not actively shaping how it learns, you’re falling behind faster than you think.

In this clip, Sudha Kumar explains why data definitions, rules, and real-world context matter more than flashy demos. AI reflects the decisions you encode into it — good or bad.

This is how serious product leaders stay competitive: less hype, more intention.

🎬 Watch the full episode here: https://lnkd.in/gqaxD78V

#AIProductManagement #AILeadership #ProductLeadership #EnterpriseAI #DataStrategy

The most dangerous thing in tech right now? A majority of product leaders feel "confident" about AI... while missing the revolution happening under their noses.

I'm seeing too many orgs celebrating their "AI transformation" while shipping chatbots driven by top-down requests and cost-cutting automation. We're optimizing the wrong things, asking the wrong questions, and dangerously overconfident about our readiness to lead in the AI era.

This isn't about learning prompt engineering or keeping up with the latest LLM releases. It's about fundamentally reimagining what it means to create value when AI is a core capability — not just a feature.

Three questions every product leader should be asking:
🤔 What if AI isn't just another tool in our stack, but a complete reinvention of how users interact with technology?
🤔 What if the "product surface" isn't a UI anymore, but an intelligent agent that works alongside users?
🤔 What if our role isn't to define features, but to architect intelligence that augments human capability?

Join Polly Allen, the trailblazer behind the first generative AI answers on Amazon Alexa in 2020, for a free webinar as she guides you through the evolving landscape of AI product leadership. 

📅 Thursday, January 29th at 12PM PDT

Space is limited. Reserve your spot now.
🔗https://lnkd.in/gbf4yX7x 👈

#AI #ArtificialIntelligence #LearnAI #Free #AIProductLeadership #FREEMasterclass

If AI makes you uncomfortable, that’s your signal not to avoid it, but to engage with it.

In this episode, we unpack why fear pushes thoughtful leaders away from the table… and why that’s the real risk. The people shaping AI shouldn’t only be the loudest or fastest movers.

Your perspective matters more than you think.

🎧 Full episode: https://lnkd.in/gs9fRyTJ

#AILeadership
#TechLeaders
#FutureOfWork
#ProductManagement
#AIAtWork

We, collectively, are too quiet about this: your personal data is TOO easily weaponized, against you. Right now. No additional advancements or AI leaps required.

I hope 2026 can be the year consumers finally start valuing trust, good actors and the companies willing to stand up and fight for basic rights. For all of us building software: this article is a great place to start digging in, and asks an important question:
"What are you willing to say no to?"

Thanks Ariba Jahan!

Head of Transformation @ Anomaly ✦ Helping brands build for what’s next ✦ product, tech, design & consumer experience ✦ Creator & Author of Unmissables ✦ Toddler Mom

"Can you do facial?" 
One Border Patrol agent to another, standing over a 16-year-old on a bike near his high school in Aurora. No ID. No warrant. Just a phone pointed at his face. 

That video went viral last year. But here's what the coverage missed: the infrastructure that made that moment possible wasn't built by the government. 

It was built by the apps on your phone. 

I went down a rabbit hole reading FTC enforcement actions, congressional testimony, investigative journalism, and academic research. The picture that emerged: 

→ 750+ data brokers registered in the U.S. (only 4 states even require registration) 
→ LexisNexis holds 282 million identity profiles. ICE searched them 1.2 million times in 7 months. 
→ For $160, journalists bought a week of location data on 600+ Planned Parenthood clinics 
→ Three weeks after the FTC banned Gravy Analytics from selling location data, they got hacked. 17 terabytes stolen. 30 million records exposed. The distance between your period tracker and a prosecutor is maybe 3-4 handoffs. None of them require your permission. 

I've built products that use location data. I've designed experiences that ask about your mood, goals, interests. Used well, this data makes products better. I know what that looks like. 
I also know what it looks like when the same infrastructure gets pointed somewhere else, without consent. 

I wrote this piece for anyone who builds products, designs experiences, or makes decisions about data collection.  

What are you willing to say no to? 

Read here: https://lnkd.in/e9XWEU6V

Most businesses asking “Why don’t I show up in ChatGPT?” are missing one thing: structure.

In this AI Product Leader episode, Maggie Mae breaks down the 3-part schema stack that actually gets businesses cited by AI tools:
• Off-page signals (directories + reviews)
• On-page clarity (classic SEO still matters)
• Technical schema that AI can read

This isn’t theory — it’s showing real results in under an hour for some teams.

If you care about AI visibility, this is required listening.

🎙️ Full episode: https://lnkd.in/gsMP9AWN

#AIProductManagement #AILeadership #GenerativeAI #ProductLeaders #AIStrategy #SearchVisibility

“AI will confidently walk you off a cliff, and that’s how you learn.”

Maggie Mae shares why waiting until you “feel ready” to use AI is the wrong move.

If you’re a PM, founder, or leader:
• Prototyping is low risk
• Curiosity beats credentials
• Breaking things is part of the deal

The fastest learners aren’t perfect — they’re willing to try.

🎧 Full episode: https://lnkd.in/gpgppeE9

#AIProductManagement #AILeadership #GenerativeAI #ProductLeaders #AIStrategy #SearchVisibility

AI Product Builder @ NanoKernel | Generative AI, AI Agents, AIoT, Responsible AI, AI Product Management | Ex-Apple, Accenture, Cognizant, Verizon, AT&T | I help companies build standout Next-Gen AI Solutions

I help visionary companies build standout AI Products. 

I am seeking a Senior IC AI Product Manager role at established AI-focused companies. 

I am a transformative Product and IT Executive, blending innovation with leadership across every role I've embraced. My journey from the technical trenches to the strategic echelons of Product Leadership, Solutions Architecture, and Enterprise Data Strategy has been fueled by an insatiable drive for growth, an unwavering focus on customer success, and a passion for adapting and thriving in diverse organizational landscapes.

From Fortune 50 giants to agile startups, my path has been marked by over 8 groundbreaking product launches and more than 20 transformative project deliveries. At the heart of these achievements lies a deep-seated belief in the power of innovative thinking, strategic execution, and the magic of collaboration across domains and disciplines. 

My mantra? Value creation, operational excellence, and nurturing talent are the cornerstones of my transformative leadership. 

My commitment to thought leadership has led me to author several books on AI and Data, secure several patents, and contribute to the knowledge pool with articles and papers featured in renowned journals, blogs, and media channels. Dive into my insights on LinkedIn and Medium at https://harsha-srivatsa.medium.com

Education is an ongoing journey for me, enriched by formal degrees in Engineering, Math, Computer Science, and Management, alongside continuous learning from prestigious institutions like MIT, Stanford, Kellogg, and IDEOU. This foundation empowers me to lead at the intersection of technology and business, navigating the complexities of AI, IoT, Cloud Platforms, and beyond with ease and insight.

My commitment extends beyond the professional realm. As a High School Educator with Microsoft's TEALS Program, a Master Gardener certified by University of California, a mentor for immigrants through Upwardly Global, and a volunteer with the Coast Guard Auxiliary, I believe in giving back, sharing knowledge, and supporting communities. I am a believer in the transformative power of technology, education, and community service. Let’s connect, collaborate, and create impact together.

Email:  harsha.srivatsa at connectwithharsha.info | Website: https://www.connectwithharsha.me

Harsha Srivatsa

Whoa! I just realized that my network connection crossed 10,000 recently! 
I am elated as well as overwhelmed that the connections count crossed the five figure mark. 

There has been a spurt in connection requests both incoming and sent by me. Many times I have not had the chance to personalize connection requests (LinkedIn on Mobile just sends the request) so I thank those who just accepted a blank connection request. 

Shoutout to my latest connection Ridhima Ranka as I write. 

Thanks you Brian Kalis and Brian Kemler for accepting my recent connection requests. 

I know my network is large but I shall do my best to make our connection meaningful and value added. 

Please DM or reach out if you want to chat with me, I can share what I know and help in any way. I shall surely put my priority and focus on growing our interactions in a meaningful way. 

As I mentioned earlier, following my mentor Ha Nguyen advice, I took the resolution to be a Go-Giver in 2026. I intend sincerely to do that. 

May we all live in interesting times.

Senior AI Product Manager | Generative AI, AI Agents, AIoT, Responsible AI, AI Product Strategy | Ex-Apple, Accenture, Cognizant | I help companies build, debug and launch standout AI products

I just applied to OpenAI Grove 🌱 https://lnkd.in/g2-ED-E8

From what I understand, Grove isn’t a traditional accelerator. It’s a 5-week builder program designed for people at the very start of the company-building journey—bringing together a small, high-signal cohort (~15 people) with workshops, office hours, and mentorship from OpenAI’s technical leaders, plus time to co-build and pressure-test ideas. 

Why I applied: because the next wave of AI isn’t only an algorithm story. It’s a physics story.
Power. Water. Cooling. Land. Community resources.

As AI infrastructure scales, we’re already seeing grid constraints, water stress, and real policy/community pushback. And yet… we’re also moving toward an AI++ world where AI spreads into edge, robotics, and cyber-physical systems. We can’t unlock that future with “more GPUs” alone. We need responsible scaling.

So my application is about what I’m calling Resource-Responsible AI:
 a Resource Ledger + “GreenOps for AI” cockpit that helps AI teams measure, attribute, and reduce energy / carbon / water footprint across training, inference, and agentic workflows—turning sustainability from a slide into an operational KPI.

This idea came straight from my work in AI Micro Data Centers—where resource constraints aren’t abstract… they show up every single day.

Now comes the hopeful part 🙏
 I’m genuinely praying / manifesting that I get to be one of the lucky ~15.

#OpenAIGrove #OpenAI #ResourceResponsibleAI #SustainableAI #AIInfrastructure #AIDatacenters #GreenOps #MLOps #AIOps #AIAgents

Human-in-the-Loop (HITL) vs Human-on-the-Loop (HOTL) in Multi-Agent Systems

While I was thinking, researching and writing out the section on Human involvement in MAS in my recent book - The Illustrated Guide to Multi-Agent Systems, I had an epiphany on how to design Human + AI agent teams that actually work in production.

Most teams pick ONE human oversight model and apply it everywhere. Most agent demos treat “human oversight” like a feature you add later.

That's a mistake.

In production Multi-Agent Systems (MAS), that’s backwards.
Humans aren’t just approvers at the end of a workflow. They’re embedded in:
 ✅ objectives
 ✅ constraints & permissions
 ✅ evaluation criteria
 ✅ escalation paths
 ✅ accountability

There are actually two operating modes:
HITL (Human-In-The-Loop)
 ➡️ the system pauses. No consequential action proceeds without explicit approval.
HOTL (Human-On-The-Loop)
 ➡️ the system runs, but humans supervise with the power to intervene, override, or kill-switch.

Here’s the key: Production MAS needs both — applied selectively, not uniformly.

Here's the framework that actually works:
Map every action on two dimensions:
→ Risk (low to high) → Reversibility (can you undo it?)
Then apply the right model:
🟢 Low risk + Reversible (draft reports, queries) → HOTL. Let agents run. Monitor for anomalies.
🟡 High risk + Reversible (budget proposals, config changes) → HITL with fast approval cycles. Human validates, but keep it tight.
🔴 High risk + Irreversible (fund transfers, data deletion, contracts) → HITL with enhanced guardrails. Multi-party approval. Audit trails. No shortcuts.

The cost of getting it wrong:
→ All-HITL? Approval bottlenecks. Humans become the constraint. You've built a system slower than doing it by hand.
→ All-HOTL? One retry storm cascades across agents. Customer-facing workflows fail before anyone notices.
→ Misaligned incentives? Agents optimize for speed, skip validation steps, and technically "succeed" while violating business intent.

The production-grade approach:
* Classify actions by risk × reversibility
* Set confidence thresholds per workflow
* Layer guardrails: policy gates → confidence scoring → runtime monitoring → escalation routing
* Reserve HITL for the ~2% that's genuinely high-stakes
* Close the feedback loop: every override becomes training data

The mark of production-grade MAS isn't how autonomous it is.
It's how effectively it embeds human judgment at every level—while still moving at machine speed.

It’s finally here. 🚢
After two months of building, tinkering, and turning real lessons into reusable patterns. 

 📘 The Illustrated Guide to Multi-Agent Systems is now live on Amazon.

2025 was the year of AI Agents. The next unlock is multi-agent systems—planner/router/executor/verifier—built to survive tools, evaluation, and production reliability. This is a production-grade approach to AI agent engineering.

What makes this book different:
 ✅ Visual-first (see the system)
 ✅ Production-first (quality, safety, cost, ops)
 ✅ Artifact-driven (plans, evidence, verification)

🎁 Includes a FREE MAS Toolkit + learning artifacts.

If you’re building AI agent products or leading AI teams shipping AI agent systems, this is for you.

Links + reviewer form in the comments 👇

No book gets written in isolation.

While working on The Illustrated Guide to Multi-Agent Systems, I went deep into several books that shaped how I think about agent architectures, orchestration, and what it actually takes to build systems that work beyond demos.

A few that made a real difference:
Victor Dibia's Designing Multi-Agent Systems taught me to think in protocols and system guarantees, not just frameworks. 
Sinan Ozdemir's Building Agentic AI showed me why agents break and how to design around it. 
Thomas R. Caldwell's AI Engineering Bible grounded me in what production-ready actually means. 
Sadie St Lawrence's work on AI orchestration reframed how I think about the role behind the systems. 
And Pascal Bornet, Melissa Pamela, Anjanava Biswas, and Wrick Talukdar each added layers to my understanding of business impact, control loops, and organizational transformation.

My writing is a synthesis of existing thinking and experiential learning. 

These authors did the hard work of documenting their thinking, and I'm grateful I could stand on their shoulders while building my own visual playbook.

If you're serious about multi-agent systems, these books belong on your shelf. See attached document. 

Many thanks to these Authors: 
Pascal BORNET
Rakesh Gohel
Tom Davenport
David De Cremer
Melissa Pamela
Anjanava Biswas
Wrick Talukdar
Victor Dibia, PhD
Thomas R Caldwell
Sinan Ozdemir

Awesome AI Agent Engineering Books

The moment when an epiphany about AI Agent Observability happened, is when I put down some notes for this post. 

Most AI Observability approaches today tell you what happened.
However, AI agents don’t fail like microservices or servers.  AI agent observability must tell you what’s about to break.

AI Agents don’t just crash or timeout. They loop, over-retry, thrash tools, or quietly escalate work to humans while appearing “healthy.”

While there is great guidance from Arize AI / LangChain / Langfuse / Hugging Face, I feel that Observability for AI agents has to move beyond traces, cost, and latency—and focus on agent behavior.

I liked how IBM framed it: 
"Evaluate agent performance by collecting data about actions, decisions and resource usage. It helps answer critical questions, such as:
Is the agent providing accurate and helpful answers?
Is the agent using processing power efficiently?
Is the agent using appropriate tools to fulfill its goals?
What are the root causes of issues with an agent?
Is the agent complying with AI ethics and data protection mandates?

With these insights, organizations can troubleshoot and debug issues more effectively and improve the performance and reliability of AI agents. 
"
Hence I came up with a framework to monitor, measure and report on AI Agent Observability. 

Four operator questions. Eight metrics. One goal: trustable autonomy.

1️⃣ Is it working?
 • Task Success Rate
 • Verifier Pass Rate

2️⃣ Is it struggling?
 • Tool Success Rate
 • Retry Rate
 • Loop Rate

3️⃣ Is it efficient?
 • Time to Result (median, not average)
 • Cost Proxy (tokens, tool calls, retries)

4️⃣ Does it need help?
 • Escalation Rate

What’s different here isn’t the metrics themselves—it’s what they represent.
These aren’t infrastructure metrics.
 They’re behavioral signals:
• Retry & Loop Rates expose reasoning breakdowns
 • Verifier Pass Rate applies truth pressure
 • Escalation Rate defines the autonomy boundary
 • Time & Cost reveal economic viability, not just performance

This is observability as operability.
Not:
“Can I see what the agent did?”
But:
“Can I trust what it will do next—and recover when it doesn’t?”

That’s how AI agents I think can earn trust at scale.

I have written more about it in my upcoming book "The Illustrated Guide to Multi-Agent Systems". 

#AIAgents #AgentObservability #AgentOps #LLMOps #ReliabilityEngineering #EvalOps #AIArchitecture

LLM / Gen AI security is a topic that I am not familiar with although I follow the  awesome writing by my friend Rammohan Thirupasur on this topic. 

So when Dipali M., my fav editor at Packt sent me this book, I read this with a beginner mindset. 

One of the concepts in the book - Trust boundaries - inspired me to think about an equivalent aspect for Multi-Agent systems which I parlayed into my recently published book on Multi-Agent systems. 

AI-Native LLM security is a very important topic with very few credible resources. So this book from Ken Huang / Vaibhav(VB) Malik Ads Dawson
is a seminal work in the field. 

AI-Native LLM Security (Packt, 2025) — a book that lands at exactly the moment many of us are moving from “LLM demos” to “LLM systems in production.”

The big shift is this: LLM security isn’t just AppSec with a new wrapper. Once you add RAG, tools, agents, and enterprise data, the attack surface becomes language-shaped—prompt injection, system prompt leakage, vector store weaknesses, and “agentic” misuse don’t fit neatly into old threat models.

What I liked about this book is its practicality: it treats security as an end-to-end product discipline (architecture → trust boundaries → controls → monitoring → incident response), and it gives teams a shared language through frameworks like OWASP Top 10 for LLM Apps—so security, engineering, and product can align without hand-wavy debates.

The Security Question Has Changed. Is Your LLM Architecture Security Ready? Here's a question I've been sitting with lately: If your LLM made one wron

Open article: Review: AI-Native LLM Security by Vaibhav Malik, Ken Huang, and Ads Dawson by Harsha Srivatsa • 4 min read

Review: AI-Native LLM Security by Vaibhav Malik, Ken Huang, and Ads Dawson

Attending CES 2026 at Las Vegas for the first time. Its exciting, overwhelming, lots to take in.. But its an AI show like no other. Especially given my interest in Physical AI. 

CES 2026 (Day 1 Jan 5 2026) made one thing clear: AI is growing hands, wheels, and sensors.

We’re watching the shift from “AI that talks” → AI that moves, sees, touches, and acts in the real world.

Here are the Physical AI signals I’m tracking from Jan 5, 2026:
1) NVIDIA is pitching the “factory for robots + AVs.”
Rubin + new AV models (Alpamayo) feels like NVIDIA saying:
👉 “If you want autonomy at scale, you’ll train + simulate on our full stack.”
This is less about one chip… and more about owning the pipeline (compute → simulation → deployment).

2) Home robotics is jumping from gadgets → generalist helpers.
SwitchBot’s Onero H1 is a bold move: a wheeled humanoid doing real chores using an on-device VLA brain (Vision + Language + Action).
Translation: it can see the world, understand instructions, and do stuff—without depending on the cloud every second.

3) The AV sensing war is getting spicy.
Teradar’s terahertz sensor is trying to blend “lidar-like detail” with “radar-like all-weather.”
Arbe + NVIDIA are pushing ultra-HD radar as something closer to a primary perception system (not just a backup).
Big theme: “lidar-class perception” without lidar constraints.

4) Drones are turning into flying edge-AI computers.
Ambarella’s CV5 powering an 8K 360 drone concept is a reminder that high-res + onboard AI is becoming the baseline.
But… regulation and supply chain policy may decide who actually wins the market.

5) Samsung is framing the home as a “physical workflow.”
Not just “smart devices.” More like AI companions that sense → decide → act across the home.

My takeaway:
Physical AI is now a platform game.
The winners won’t ship devices. They’ll ship systems: hardware + models + sensor stack + ecosystem + safety.

For Physical AI Product Builders:
Build sensor-agnostic perception (avoid single-modality lock-in).
Go on-device first, cloud assist second (latency + privacy + safety).
Treat policy/compliance as a product requirement, not a footnote.

I wonder which becomes the “default brain” for Physical AI in 2026–2027: on-device VLA, cloud copilots, or hybrid?

#CES2026 #PhysicalAI #EmbodiedAI #Robotics #HumanoidRobots #AutonomousVehicles #EdgeAI #SensorFusion #ComputerVision #AIHardware #SmartHome #AgenticAI

The art of constructing Golden Data Sets (using the 6V framework) 🧪✨

While I was thinking and researching about Golden Sets for Agent Evaluations, the following questions came to my mind: 
- What should be the size of the Golden Data Set for effective evaluations of an Agent application? 
- What should be the variety of the representative entries in the GDS? 
- For an multi-agent system, should be have Evals - GDS for each agent and also for the MAS end to end? 

I didnt find too many authoritative source and definitive guidance. But I was able to find brilliant snippets that helped me a lot. 
* Stella Wenxing Liu substack blog Data Science x AI had an awesome article on how big an evaluation test should be. 
* Talking to an AI architect inspired me to come up with the GDS coverage - Happy Path (50%), Edge cases (20%), Tool failure cases (10%), safety and regressions (15%, 5%). 

Then I had the epiphany for applying the classic Big Data framework to GDS. 
Most teams treat a golden dataset like a spreadsheet they “should probably have.”

In AI Agent world, a golden set is something else:
* It’s your definition of “good”.
* Your release gate.
* Your anti-regression insurance.

The “big data” thinking - the 6V's - is a solid approach to building high quality GDS. 

1) Volume — Do we have enough test cases?
2) Variety — Do we cover different kinds of reality?
3) Velocity — How fast does it go stale?
4) Veracity — Can we trust labels + scoring?
5) Value — Does the dataset protect what matters?
6) Variability — Do we capture ambiguity + distribution shift?

The MAS twist: Golden sets are two-layer
If you’re building multi-agent systems, the 6Vs naturally imply two eval views:
✅ System-level E2E golden set (user journey → final outcome)
✅ Per-agent golden sets (tight contracts → faster debugging + safer iteration)

One last point: you don’t always need massive GDS. Often, a small pilot (~20) tells you the score variance, and you can estimate an effective test size—sometimes ~100 is plenty, not 5,000.

I have covered this in detail in my newly published book "The Illustrated Guide to Multi-Agent Systems" - link in comments. 

#EvalOps #LLMEvaluation #AgenticAI #MultiAgentSystem

Yet another awesome session from SuperInterviews with Kasey Superinterviews and Jeremy Barton. 

Hear Deborah Liu say there is no such thing as true failure in interviews. Every interview is a prep to the next interview.

I am convinced that simulation based learning and exploration should be an integral part of AI agent building. 

I used to learn multi-agent systems like this:
📖 read an article, blog, paper, course material
🧠 seem to get it and nod my understanding
🧩 imagine how I can implement it into my product
🚀 vibe build a MAS prototype
😅 get surprised when I make it work for real 

The uncomfortable truth: I could design and vibe build MAS architecture… but I didn’t have full confidence in my system design until I could see the system behave under real conditions.

So I changed how I learn.
Now I learn MAS architecture like a pilot learns flight:
In a simulator.

Not “animated diagrams.”
A decision lab: inputs → choices → consequences → artifacts.
I pick a simple multi-agent use case (any domain works: support, finance, research, ops) and simulate the build approach and design decisions. 

This is what’s helping me become a Super IC AI PM — because the simulator makes the invisible… visible:

Tradeoffs stop being theoretical
Topologies aren’t “better/worse.” They’re trade spaces.
You can literally watch coordination tax show up, and see where the quality gains stop being worth the latency/spend.

Reliability becomes a systems skill
Not prompt luck.
I inject failures (noisy retrieval, flaky tools, partial context, conflicting agents) and learn what actually stabilizes systems: guardrails, budgets, circuit breakers, fallbacks, escalation paths.

Production thinking becomes muscle memory
EvalOps, observability, and safe rollout aren’t just checklists — they’re behaviors over time.
Shadow → Canary → Full becomes something you practice, not just plan.

My litmus test is simple:
If a simulation helps me make a better build and decision decision and teaches me why, it’s real learning.

Here are three examples of simulation based artifacts that helped in better MAS system design: 

1. The EvalOps & Observability Dashboard is an interactive simulation that implements the complete evaluation lifecycle for multi-agent AI systems. Manage golden test sets, monitoring real-time metrics (latency, accuracy, cost, safety) and debug with distributed traces. 
https://lnkd.in/gvEE67RY

2. The Failure Mode Diagnostic Lab is an interactive simulation that shows the four critical failure modes in multi-agent AI systems: Loops, Tool Thrashing, Cost Explosions, and Security Breaches. Instead of just reading about what goes wrong, you can watch failures cascade through real-time metrics and audit logs—then apply fixes and see them work.
https://lnkd.in/g2j6SbjZ

3. The Cost Explorer is an interactive simulation that makes multi-agent system costs tangible. It visualizes the five root causes of cost explosions—context snowballing, unlimited fan-out, missing caches, memory hoarding, and wasted verifications. 
https://lnkd.in/gBnxx7qt

Simulation Artifacts for Multi-Agent Systems Design

The Evolution of Evaluations

The evolution of evaluations is happening in plain sight:  we’re moving from LLM-as-a-Judge → Agent-as-a-Judge.

This recent paper - A Survey on Agent-as-a-Judge [https://lnkd.in/dh9ThVRR] is an awesome recent survey publication and captures the trend thinking quite well. 

Many people have commented and written about the paper albeit in a static commentary way but not delving into the nuances. With Google Dynamic views in Gemini, I have found a way to really understand an AI paper and understand the principles and working outlined in a paper. Instead of passive reading (or even “paper → code”), I’m building interactive simulation-based learning artifacts: https://lnkd.in/d42i3hgp

In this simulation based learning artifact, I simulate the judge’s workflow (plan → evidence → verify → deliberate → report), stress-test edge cases and failure modes, run “what-if” scenarios to expose hidden assumptions
and walk away with a mechanistic understanding of how this would behave in production. I have used a code generation use case to illustrate the difference between Agent-as-a-Judge and LLM-as-a-Judge. 

Please do check out Google Gemini Dynamic View artifact link above. 

For agentic evaluation specifically, simulation stimulated my thinking about: 
 What tools? What verification steps? What memory? What arbitration? What’s the stopping rule?

LLM-as-a-Judge was a huge leap: scalable, “near-human-ish” scoring, and a practical proxy for preference/reward signals. 

But it’s starting to hit a ceiling as evaluands become multi-step, tool-using, and domain-specialized. 

Where LLM-as-a-Judge breaks down
A single-pass judge tends to struggle with:
* Biases baked into the judge (e.g., rewarding verbosity, style, or its own output patterns). 
* “Looks correct” vs “is correct”: it often can’t verify claims against the real world, so you get hallucinated correctness. 
* Cognitive overload when the rubric has many dimensions → coarse scores that miss nuance. 
* Over-focus on final output, while failing to verify intermediate actions in complex workflows. 

What Agent-as-a-Judge changes
Agent-as-a-Judge turns evaluation into an active process, not a static opinion:
* Plans + decomposes evaluation into subtasks
* Uses tools to ground & verify (search, code execution, environment checks)
* Leverages multi-agent collaboration to reduce single-model blind spots
* Uses memory to preserve intermediate evidence and produce more traceable judgments 

The paper even frames a maturity curve:
Procedural → Reactive → Self-Evolving judge agents (from fixed workflows, to adaptive routing/tool use, to agents that can refine rubrics and learn over time).

If you’re building EvalOps for agentic systems, I think this shift matters:
the Judge is no longer a prompt — it’s an Agent system.

Like many others, I too am on the bandwagon of AI agent / AIoT agents. 
I have delved into the nitty gritty of ~10 multi-agent implementation - 2 in Healthcare, 1 in AI Infra and the rest across different use cases mostly for productivity and product management. 
One of the MAS is currently deployed and running in a Hospital system. 

From these experiences, I’ve learned the hard way that with multi-agent systems, “it works” in a demo doesn’t mean it will survive production.

The biggest gap isn’t tooling. It’s intuition—how agents behave over time when tools fail, handoffs break, loops run, costs spike, or security boundaries get fuzzy.

So I’m sharing a systems-thinking approach I’m excited about: Simulation-First Development. Before we prompt and build, we simulate. We practice the tradeoffs. We inject failures safely. We generate artifacts that become design records.

In the article below, I break this down and outline a 22-simulation roadmap that mirrors the full multi-agent lifecycle—from mental models all the way to EvalOps, scaling, and enterprise constraints.

I have documented the complete set of 22 simulation based learning and exploration artifacts that are enumerated in the roadmap described in the article. 

If you would like to take a look at these simulation artifacts, comment SIM below and I shall send you the link.

A Systems Thinking Approach to Vibe Engineering Multi-Agent Systems 85% of AI agent pilots never reach production. Not because the technology failed. 

Open article: Simulation-Based Multi-Agent Development by Harsha Srivatsa • 4 min read

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