You’re a great Product Manager.

You know you’ll be an even better Product Leader. But...

Why is it so hard to make the leap from PM → Product Leader?

Hard Truth #1:
Opportunities for advancement in product management are scarce. Product teams are typically smaller, and when leadership roles open, the competition is fierce. You need to be primed and ready for when your chance comes.

Hard Truth #2:
The skills that make you a stellar PM won't necessarily make you a stellar Product Leader. Leading 100 people isn't the same as leading 10; it requires a significant evolution of your skill set.

So, what skills do you need? How do you acquire them?

That's where I come in!

I’ve been building products for over 25 years, and have grown from leading a small team of 5 to leading a 300+ org at a top-tier company. I became a Product Leader by navigating the very same obstacles facing you today.  

My mission is to help great PMs like yourself transform into outstanding Product Leaders.

You can do it! There are immediate steps you can take starting today to set yourself on this path.

Follow me here on LinkedIn to discover how.


- - - - -
🔥 If you *really* want to fast-track your career, visit: amplifypm.com/transform

Roshan Gupta

I have been a PM at Meta and a Group PM at Google, and if you think I had it all figured out. I would disagree.

What experience really gives you is not certainty.
It gives you scar tissue.

You learn some things late.
Often after you’ve shipped the wrong thing, said yes too many times, or lost trust you had to earn back.

Looking back over my career, these are the product-building principles I wish I had internalized earlier.

Not because I didn’t know them. But because I didn’t practice them consistently.

1. Outcomes over output.
I spent years celebrating launches before I learned to celebrate impact.

2. Fall in love with the problem, not the solution.
Most early mistakes came from defending ideas instead of understanding pain.

3. The art of “no” defines your strategy.
Every yes I regretted was a painful, future no I could have avoided.

4. Data informs. Intuition guides.
Data matters, but don’t ignore intuition. Intuition is rooted in data acquired over years.

5. Manage stakeholders, don’t appease them.
You don’t need their approval. You need their respect. Deliver value, not “yes”.

6. Empower with “why,” not “how.”
Micromanagement feels productive. It quietly kills velocity.

7. Perfect is the enemy of shipped.
Speed of learning beats elegance in almost every real system.

8. Your team is your best product.
You can fix a roadmap. You can’t outrun a broken team dynamic.

None of this is taught cleanly.

You absorb it slowly
- Through tension
- Through missed promotions
- Through meetings that don’t land

This is what senior PM growth actually looks like.

Making mistakes. Learning. Honing your judgement.

If you are earlier in your PM journey, save this.
If you are a senior, you probably recognized yourself in at least two of these.

Share this if you found it relatable.

P.S. If you are a PM who’s struggling to be heard and noticed, I have designed the live “Persuading People: The PM's Edge in the Age of AI” cohort to help you learn how to communicate decisions so clearly that leadership trusts your judgment, and your team follows you.

More than 100 PMs have used it to accelerate their growth. 

Register now if you're ready to be seen as the leader you are: https://lnkd.in/d7STtuG8

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PST (Maven)

Master the persuasion system I used to secure 9-digit budgets & Fortune 50 partnerships: amplifypm.com/maven

One-Off - 2026-01-29

amplifypm

Follow me to go from PM → Product Leader 🪜 | Founder of AmplifyPM (ex-Google, Meta, Yahoo, startups) | I grew from leading a team of 5 to leading a 300+ org. I'll show you how to do the same.

A Product Manager (IC4) at Meta can make around $242K.
A Senior PM (IC5) can make $449K+.
A Staff PM (IC6) can make $622K+.

If you want to close that gap, stop thinking the next level is “more scope.”
It’s a different skill.

At IC4, you get rewarded for being dependable. You keep the train moving.
At IC5 and IC6, you get rewarded for making other people confident in your judgment.

That is what changes comp and promotions.

Here is the practical difference.

When a roadmap review gets tense, IC4 PMs try to be thorough. They add context, show charts, and explain the process.

IC5 PMs do something else. They frame the decision first, then the details.

They walk into the room already knowing:
→ What decision needs to be made.
→ What the tradeoffs are.
→ What they recommend.
→ What they are willing to give up to get it.

IC6 PMs go one step further. They don’t just answer the question. They simplify the entire debate so it does not keep coming back.
They turn a one-time decision into a reusable principle.

If you want to excel and move up faster, practice these three habits.
Start meetings with the decision, not the backstory.

Replace “here is the data” with “here is what the data means and what I recommend.”

After every big call, write down the tradeoff you made so leadership can retell your reasoning when you are not in the room.

That’s how you become the PM people trust with bigger bets.

And that’s what you are paid for.

P.S. I have designed the live Persuasive Storytelling for PMs course to help you learn how to communicate decisions so clearly that leadership trusts your judgment.

More than 100 PMs have used it to accelerate their growth. Early-bird enrollment is open for a short window. Register here and save 33%: https://lnkd.in/d7STtuG8

At Google, Meta, and Yahoo, I have worked with enough PMs to notice something interesting.

High-performing PMs don’t stand out because they do radically different work.
Most of them are doing roughly the same things as everyone else.

What separates them shows up in quieter moments.

→ In how they frame a decision when the data is incomplete.
→ In how they say no without creating friction.
→ In how they help a room move forward instead of getting stuck debating details.

You rarely notice it at first. It just feels easier to work with them.

Meetings end with clarity.
Teams leave knowing what matters.
Leadership trusts that if something changes, this PM will adapt without drama.

And the opposite is also true.

When a PM struggles, it’s almost never because they lack effort, intelligence, or frameworks. It’s because their thinking does not travel well.

People hear the work, but not the judgment behind it. 
They see activity, but not direction.

Over time, that gap compounds.

→ The PM who can make tradeoffs legible earns trust faster.
→ The PM who can connect data to meaning gets more room to operate.
→ The PM who owns outcomes without over-explaining becomes the one leaders lean on.

Those are not flashy skills. But they are the ones that quietly shape careers.

P.S. If you are a PM who wants to build those skills deliberately, Persuasive Storytelling for PMs is designed around exactly this shift.

Over 100 PMs have already used it to grow their influence, trust, and trajectory. 

Early-bird enrollment is open for a short window. 
Register here and save 33%: https://lnkd.in/d7STtuG8

Google

Yahoo

When you present to senior stakeholders, the first 20 seconds matter more than the next 20 slides.

The biggest mistake I see PMs make: they assume their audience will pay attention.

That their audience cares as much as they do.
They’re wrong.

That's not how it works. 

You must learn to make people WANT to hear you.

Next time – instead of starting with slides, background, or the solution – I’d start like this:

“Right now, 18% of users who start this flow drop off before step three. If we do nothing, that’s a $12M annual loss.”

Then I’d stop talking.

Because now I have something far more valuable than airtime.
I have attention.

Only after that would I explain:

→ Why this problem exists
→ What we’ve tried so far
→ What decision I’m asking for

Most PMs do the opposite.

They start with: “Let me walk you through some background…”

By the time they reach the actual problem, the room is already gone.
And once attention is gone, you don’t get it back with more slides.

You get interrupted.
You get pulled into edge cases.
You end up defending details that don’t matter.
The meeting turns into a debate instead of a decision.

That’s why the first 20 seconds matter more than the next 20 slides.

Senior stakeholders don’t need the full download.

They need a sharp frame:
What’s at stake?
Why now?
What do you need from them?

Do that well, and the rest of your story becomes easier to land.

Repost this if it resonated.

P.S. I have designed the live “Persuading People: The PM's Edge in the Age of AI” cohort to help you learn how to communicate decisions so clearly that leadership trusts your judgment so you get seen and rewarded.

More than 100 PMs have already used it to accelerate their growth. Register now: https://lnkd.in/d7STtuG8

Bumped into this thread today, and it took me straight back to my time at Google when I turned a $20M budget into $120M.

A Staff PM admits they spend 20% of their time on strategy and 80% on storytelling.
Because strategy without narrative doesn’t move people.

For a long time, I believed the same thing many PMs do: “If the thinking is solid, the data will speak for itself.”

So I showed up with decks, perfect charts, clean assumptions, and a clear logic.

My proposals got approved. But they rarely got momentum.
This is exactly what this thread talks about, and it’s a painful kind of approval.
You get the “sure, sounds fine” approval.
Not the “this is the priority” approval.

The thread nails why. The Staff PM:

→ Walked into exec reviews with 20 slides of immaculate charts.
→ The VP looked at their phone.
→ The initiative technically passed, but it wasn’t championed.

Then they watched a peer pitch a weaker idea with fewer charts.
But the peer told a story.

→ A specific user in distress.
→ A villain, the current broken process.
→ A hero, the proposed change.
→ A cost of doing nothing.

Execs leaned in. They got double the funding.

That’s the difference between being correct and being compelling.

Being correct gets you approval.

Being compelling gets you attention.

Data proves, but story persuades.

And at senior levels, persuasion IS the job.

If you want your work to move faster, get prioritized, and get real backing, the lever is rarely another chart. It’s narrative structure.

P.S. When stakeholders understand your narrative, they trust your judgment even if they disagree. This is the kind of storytelling PMs build inside “Persuading People: The PM’s Edge in the Age of AI”.

If you want a repeatable system to turn feedback into alignment to get seen and rewarded as the leader you are register here: https://lnkd.in/dB7xfrvr

If I wanted to grow my career as a Product Manager in 2026, I would stop blindly following most of the advice that gets recycled on LinkedIn.

Because a lot of the loudest PM advice comes from people who have barely done the job.

→ They have never been the ones trying to get engineering time when every team is understaffed.

→ They have never had to kill a feature users loved because the data quietly said it was the wrong bet.

→ They have never stood in front of skeptical stakeholders defending a roadmap that was already starting to crack.

→ They have never owned a launch that didn’t go the way the deck promised.

Yet they sell frameworks. Playbooks. “Proven systems”.
And the irony is they're teaching a job they just started learning themselves.

I see it constantly.
People who have never been PMs, but aspire to be one.
People who are one year into their first PM role.

They package theory as experience.

And PMs eat it up because we are desperate for guidance in a role that's different at every company.

I'm not calling anyone out, but I cannot watch as PMs fall for everything they see online and then blame themselves when it doesn't work.

You can never learn Product management from templates.

You can learn it from:
→ Shipping features that flop
→ Making trade-offs that upset everyone
→ Building trust with engineers who don't report to you
→ Saying no to your CEO's pet projects

Before you follow PM advice on LinkedIn, ask one question:
Have they done the job?
And have they done it long enough?

If not, keep scrolling.

Today, I’m going to give away a big part of what actually helped me grow as a PM at Meta, and then GPM at Google, so read this carefully.

Like every PM earlier in my career, I thought growth was about getting better at the craft.

→ Better PRDs
→ Cleaner metrics
→ More structured thinking

And to be fair, that’s what gets you promoted the first few times.

But at some point, that stopped being the bottleneck.

When I moved into more senior PM roles, including at Meta and later at Google, I noticed something uncomfortable.

→ My work was solid.
→ The thinking was sound.
→ The analysis held up.

Yet some initiatives moved fast and got real backing, while others stalled despite being “approved”.

No one said anything explicitly. There was no negative feedback.
Just a quiet difference in energy.

Over time, I realized what was missing.

I wasn’t failing at product.
I was failing to carry the thinking through the room.

I was explaining instead of leading.
Answering instead of framing.
Providing context instead of anchoring decisions.

In senior rooms, that distinction matters more than people admit.

People aren’t just listening to what you’re saying.
They’re paying attention to what happens after you speak.

→ Do you drift into the background when challenged, or do you come back to the core trade-off?
→ Do your answers tighten the narrative, or scatter it?
→ Does the room feel clearer after you speak, or more complicated?

That’s when I learned something that sounds obvious but took years to internalize.

👉🏽 There’s a difference between getting your message out and getting your message across.

Getting it out means the words were spoken.
Getting it across means the room moved.

That shift didn’t happen overnight for me.
It came from sitting in enough rooms, missing enough signals, and slowly realizing why some conversations moved the room, and others didn’t.

If you’re a PM who feels like your work is good but your impact isn’t compounding the way it should, this might be the gap you’re feeling, too.

Not a lack of ability. A lack of narrative.
And once you see that, you can’t unsee it.

This is the work I focus on now – helping PMs turn solid thinking into clear, persuasive narratives that actually move people.

If you want to build that skill intentionally, register for my live class “Persuading People: The PM’s Edge in the Age of AI”.

An exclusive system that helps PMs get seen and rewarded. 

Register here: https://lnkd.in/dB7xfrvr

The PM job is really 2 jobs. And I bet you’re only doing 1 of them.


1. Operator - keep things moving.
2. Strategist - choose what matters.

Most PMs only schedule time for the first.
So the second gets squeezed into “when things slow down.”

And that’s why so many PMs feel like they’re shipping a lot, but influencing less.

Because when strategy is constantly interrupted, you don’t just lose time.
You lose the thread.

The narrative that answers:

→ What are we solving?
→ Why now?
→ What are we NOT doing?
→ What does success look like?
→ What tradeoff are we willing to own?

Without that story, the org defaults to:

→ Opinions over priorities
→ Loudest stakeholder over clearest direction
→ “Quick wins” over meaningful bets

This is why I don’t think the next PM superpower is another framework. It’s storytelling.

Not presentation skills or being charismatic.
The real skill is turning messy inputs into a clear, repeatable narrative that:

→ Gets buy-in when you’re not in the room
→ Protects the roadmap from constant re-litigation
→ Gives your team confidence in the why, not just the what

And yes, storytelling is also how you protect mindspace.

Because when the story is clear, you stop re-explaining decisions 20 times a week and focus on what matters the most.

PMs who leverage storytelling get noticed, rewarded, and promoted fast, and I've seen that happening at Google and Meta.

P.S. I have designed the live Persuasive Storytelling for PMs course to help you learn how to communicate decisions so clearly that leadership trusts your judgment.

More than 100 PMs have used it to accelerate their growth. Early-bird discount ends today, register now and save 33%: https://lnkd.in/d7STtuG8

Today I want to admit something I avoided for a long time. For many years, I was a “good PM”...

..But not an influential one.

Like most PMs, I lived in Line 1 and Line 2, because they feel safe.

Line 1: To report
Weekly updates. Status decks. “Quick sync?” calls.
I was responsive, available, and always “on top of things.”

Line 2: To collect frameworks
I could name every prioritization model.
I had templates for discovery, strategy, roadmaps, OKRs, you name it.

And yet, something kept happening.

→ My roadmap would get questioned every time.
→ Decisions would get reopened again.
→ Stakeholders would “align” in meetings, then push back in Slack.

I thought I needed more data, slides, and frameworks.
What I actually needed was something simpler.

A story people could believe.

Because here’s what no one tells you:

In most companies, you don’t win with the best idea.
You win with the idea that can be understood, repeated, and defended when you’re not in the room.

That’s when it clicked for me.

There are four lines PMs stand in:

1. To report - easy to be busy, hard to create movement
2. To collect frameworks - feels like growth, often becomes hiding
3. To prioritize - where tradeoffs begin
4. To persuade - where careers accelerate

The shift for me happened the day I stopped sending “updates", and started sending a narrative.

A narrative that told:

→ Here’s what changed
→ Here’s the tension we can’t ignore
→ Here are the options (and the tradeoffs)
→ Here’s the decision I recommend
→ Here’s what I need from you

Suddenly, I wasn’t fighting for attention.

People were pulling clarity out of my message.

If you feel stuck right now - busy but not advancing - ask yourself:

Are you standing in the lines that keep you safe, or the line that makes you effective?

P.S. I have designed the live Persuasive Storytelling for PMs course to help you learn how to communicate decisions so clearly that leadership trusts your judgment.

More than 100 PMs have used it to accelerate their growth. Register now if you're ready to be seen as the leader you are: https://lnkd.in/d7STtuG8

I can usually tell in 30 seconds whether a PM will lose the room.


By how much they tried to fit on the first slide.

The slide is packed.
↳ Context paragraph at the top
↳ Two charts side by side
↳ A roadmap snapshot squeezed in
↳ Three footnotes “just in case.”
↳ And somewhere in the corner, a tiny line that says “Decision needed.”

The PM thinks they’re being thorough, even I thought the same when I started as a PM.

But senior stakeholders read something very different.
They read hesitation.

Because when a slide is doing everything, it usually means the PM hasn’t yet decided what matters most.

At senior levels, slides are not about conveying information. They are about conveying judgment.

Judgment shows up in:
→ What you choose to leave out.
→ What you choose to highlight.
→ What you repeat.
→ And what you are asking the room to decide right now.

Most PMs overload slides because they are trying to protect themselves.
→ From questions
→ From pushback
→ From being wrong

Ironically, that does the opposite.

→ Crowded slides invite interruptions.
→ They pull the conversation into details.
→ They turn a decision meeting into a debate.

Clear slides signal confidence.
They say, “I’ve thought this through, and this is the part that matters.”

I have a simple mantra that fixes a lot of this.
"When in doubt, leave it out."

Not because details are unimportant. But because timing is.
And clarity is not a design skill. It’s a leadership skill.

P.S. If you want your ideas to land and your decisions to stick, persuasive storytelling is the lever to build. More than 100 PMs have joined my live cohort - Persuading People: The PM's Edge in the Age of AI- to master storytelling to get seen and rewarded. 

Register here if you are ready: https://lnkd.in/dB7xfrvr

The fastest way PMs lose credibility is trying to act on every piece of feedback.

Here’s what it looks like in real life.
- Monday: you present a crisp PRD.
- By Friday: it becomes a “kitchen sink” doc.

Because everyone left feedback.

1. Design: “Can we make it more delightful?”
2. Eng: “We can’t support this edge case this quarter.”
3. Sales: “A customer asked for this last week, can we add it?”
4. And the worst one: “I don’t know… it just doesn’t feel right.”

So you do what many PMs do to be seen as “easy to work with”.

You accept all of it.

→ You add two screens.
→ You expand scope “just a bit”.
→ You rewrite the narrative to please each person.

And then in the next review, something painful happens:
People don’t argue about the product. They question your judgment.

“What are we actually shipping?”
“Why are we solving this now?”
“Didn’t we decide last week?”

That’s the credibility leak.
Not because you listened. Because you stopped being the decision owner.

When I was a PM, what changed for me was treating feedback like a decision input.

Every time I got feedback, I asked myself:

👉🏽 Is it clear and specific?
If someone says “this is confusing”,
I ask: “Which part exactly? What did you expect to happen instead?”
If they can’t answer, I don’t change anything yet.

👉🏽 Is it backed by evidence?
If Sales says “customers want X”,
I ask: “How many accounts? Is it churn risk or nice-to-have?”
If it’s one loud request, it goes to discovery, not the sprint.

👉🏽 Does it further our objectives?
If someone says, “This would be nice to have”,
I ask: “Which OKR does this move, and how will we know?”
If it doesn’t map to a goal we agreed on, I politely disregard.

👉🏽 Is it feasible?
If someone says, “We should do this now”,
I ask: “What would we drop to make that true?”
If the trade-off isn’t worth it, I park it to protect focus.

Then I communicate one of three calls, explicitly:

1. Do it: it aligns with evidence and objectives.
2. Park it: good idea, but the timing is wrong.
3. Kill it: it’s preference, not evidence.

The leadership move is not saying YES louder.
It’s being able to say, calmly: “I heard you. Here’s what we’re doing, and here’s why”.
That’s what senior PM credibility looks like.

Share this if you agree.

P.S. When stakeholders understand your narrative, they trust your judgment even if they disagree. This is the kind of storytelling PMs learn inside Persuading People: The PM's Edge in the Age of AI.

If you want to communicate like a senior PM in high-stakes rooms, register here: https://lnkd.in/dB7xfrvr

Give me 15 seconds, and I will tell you what storytelling for PMs actually is, and why it is quietly deciding who gets promoted, and who stays stuck.

Most PMs I speak to still think storytelling means slides, polish, or sounding confident in a room.

Yes, how you deliver your message matters.
But that’s not what holds people back.

Delivering your message ≠ getting your message across.

What actually breaks PM careers is this moment: you have the data, the work is solid, but different people walk away with different interpretations of what you just said.

→ Engineering hears effort.
→ Leadership hears risk.
→ Design hears ambiguity.
→ And somehow none of them hear a clear decision.

That’s where storytelling shows up, even if you never call it that.

Real storytelling for PMs is the ability to take one messy situation and explain it in a way that resonates differently for each audience, without changing the core truth.

→ To engineering, it makes tradeoffs feel intentional instead of reactive.
→ To leadership, it clarifies what matters now and what does not.
→ To design, it gives direction instead of noise.
→ To the team, it creates momentum instead of churn.

When PMs struggle, it’s rarely because they lack insight.
It’s because their thinking comes out tangled, fragmented, or one-size-fits-all.

You can feel it in meetings.
Long explanations.
Too much context.
People tuning out.

The PMs who grow fastest help people understand why a path makes sense, to THEM.

That is the quiet skill behind executive presence, trust, and promotions.
It is not about telling stories. It is about making your thinking legible to other humans.

And once people can follow your thinking, once you convince them to care, they start backing your decisions.

That’s when your career starts to move.

P.S. If you want to get better at making your thinking land so you get seen as the leader you are, Persuasive Storytelling for PMs is built for exactly that. More than 100 PMs have already used it to grow their career, and comp.

Early-bird enrollment is open for a short window. Register here and save 33%: https://lnkd.in/d7STtuG8

Stop thinking AI is going to 10x your PM career.

If you think using ChatGPT to write better PRDs or analyze data faster is your competitive advantage, you’re missing the point.

AI is making execution cheap.
It’s making “good content” a commodity.

In a world where every PM has the same tools to generate output, the value of output drops to zero.

So what actually matters?
The one thing AI cannot do.

Persuasion.

AI can generate a strategy document in seconds.
But it cannot look a skeptical VP in the eye and make them BELIEVE in it.

It can summarize a meeting.
But it cannot read the room, sense the hesitation, and pivot the narrative to save a deal.

We are entering an era where “doing the work” is the baseline.
”Selling the work” is the differentiator.

If you want to survive the AI shift, you don't need to get better at prompting.
You need to get better at influencing.

It’s not about being charismatic.
It’s not about being louder.
It’s about making your message irresistible to your audience.

This is the exact skill set I teach in my upcoming Maven cohort.

You’ll walk away with the persuasion system I used at Google to secure 9-digit budgets and land Fortune 50 partnerships.

🚨 Early enrollment is open right now (33% off).
🚨 This is the ONLY time the course is discounted.

If you’re tired of doing great work that never quite lands –
this is how you change that.

👉 Enroll here: https://lnkd.in/d7STtuG8

Hurry – early enrollment closes soon.

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When I went from leading small teams to helping lead a 300+ org at Google, one pattern kept showing up in promotion conversations.

The PMs who got promoted weren’t always the smartest in the room.
They weren’t even always the highest-output.

They were the ones who stopped being known as “reliable” and started being known as the person who could move the organization.

Because “You’re a reliable PM” is just a compliment.
It means you ship, you unblock, you keep things stable, you handle stakeholders, and you make chaos look manageable.

The problem is what that label signals upstairs.

Reliable often gets interpreted as: “I trust them to execute what we already decided.”

Promotion tends to go to: “I trust them to shape what we decide next.”

That’s the shift no one explains clearly, and it’s why PMs feel confused when they’re doing great work and still hearing vague feedback about “leadership” and “strategy.”

At higher levels, your work is judged less by how well you run your lane and more by how much you reduce everyone else’s confusion.

That shows up in very specific behaviors:

→ You don’t just share updates; you bring a decision with options, tradeoffs, and a recommendation
→ You don’t just manage stakeholders; you align incentives before they turn into escalations
→ You don’t just deliver roadmaps; you create a narrative people repeat when you’re not in the room

If you become the org’s safety net, you will get more responsibility.
But it’s often responsibility that keeps you anchored in execution.

To go to the next level, you need reliability plus leverage.

P.S. If you’ve been labeled “reliable” and feel stuck, I’m hosting a free Maven Lightning Lesson on how PM promotion expectations change as you move into leadership.

More than 1,000 PMs are already attending. Register here for free: https://lnkd.in/eVckANk9

The skills that made me a great PM held me back as a leader. (And until I fixed it, my career was on hold.)

Here’s what changes when you scale:

→ As a PM, you solve problems.
↳ As a leader, you build problem-solvers.

→ As a PM, you own the roadmap.
↳ As a leader, you own the narrative.

→ As a PM, you manage details.
↳ As a leader, you manage risk.

The transition isn’t about doing more of what made you successful.
It’s about letting go of what got you here.

When I led 10 people, I could be in every meeting, review every spec, and influence every decision. That felt like leadership.
When I started leading a 100+ people org, that same approach nearly broke me.

Because you become the bottleneck, without realizing it.
Decisions slow down. Escalations go up. Your calendar fills, but progress gets weirdly sluggish.

The shift that changed everything for me was simple:

I stopped being the answer. I became the frame.

Instead of “here’s what we should do,”
I moved to:
→ “What are we optimizing for?”
→ “What tradeoff are we accepting?”
→ “Who owns this decision?”

Those three questions did more than any framework.

They built judgment in the org.
They made decisions travel without me.
They kept me out of the weeds without losing control of direction.

The hardest part was trusting others to make decisions I used to make myself.
But that’s exactly what separates Good PMs from Great Leaders.

P.S. If you’re stepping into a bigger scope and feeling this shift, register for my live cohort "Persuading People: The PM’s Edge in the Age of AI". 

I help PMs turn solid thinking into clear, persuasive narratives that actually move people, so they get seen as the leader they already are.

Register here: https://lnkd.in/dB7xfrvr

I’ve been seeing a lot of PMs add “AI PM” to their LinkedIn headline lately. I get it. No one wants to feel left behind.

But the moment someone asks, “So how are you actually an AI PM?” – most answers fall apart.

Not because the PM is incompetent. Because they don’t have a story.

They give tool lists.
They mention ChatGPT.
They say “I’m exploring AI.”

And the listener is still thinking: ok, but what did AI change in your product work?

That’s the gap.

“AI PM” isn’t a label. It’s meant to be a narrative.

If you can’t clearly explain:
→ what AI made possible
→ what tradeoff you accepted
→ what you measured differently
→ what new risks you introduced

Then the headline looks like trend-chasing.

The PMs who stand out do one thing well.

They can tell a tight story like this:

→ Here was the user problem.
→ Here’s why traditional approaches broke.
→ Here’s why AI was the right bet.
→ Here’s what new risks showed up (quality, latency, hallucinations, privacy).
→ Here’s how we shipped safely.
→ Here’s how we measured success using AI.

Same PM. Same work. But now it lands.

Recruiters don’t reward labels. They reward substance.
And a clear narrative is what gets you ahead in your career.

P.S. If you want to be the PM who can explain your work with confidence and win trust fast, persuasive storytelling is the skill that gets you there. That’s exactly what we practice inside Persuading People: The PM’s Edge in the Age of AI.

More than 120 PMs have already learned the art of telling stories to lead in their career. Enrollment for the next cohort closes soon! If you are ready, register here: https://lnkd.in/dB7xfrvr

If you’re a PM who feels stuck, this is for you.

You’re doing good work.
Your projects ship.
Your stakeholders trust you.

And yet...nothing changes.

No bigger scope.
No stronger signal.
No clear path into leadership.

That’s the most frustrating part:
You’re working hard – but you don’t actually know what to change.

Most PMs assume the answer is:
→ More execution
→ More ownership
→ More hours

But that’s why so many talented PMs plateau.

The leap from Senior PM to Product Leader isn’t about doing more.

It’s about operating differently.

I just wrapped a free Maven Lightning Lesson where I broke down:

→ What actually changes as you move up the PM ladder

→ Why the skills that got you here stop working

→ The 5 concrete shifts leaders expect – but rarely explain

Good news – the full recording is now available 🔥

If you’ve ever wondered:
“Why am I not being seen as a leader yet?”
or
“What am I missing that others seem to get?”

You can get clarity in under 30 minutes.

Watch the free recording here:
👉 https://lnkd.in/eVckANk9

Stop guessing and start operating at the next level.

graphical user interface, application, website

One week only → 33% off 🔥
This is the ONLY time I ever discount tuition.

Early enrollment for my next live “Persuading People: The PM's Edge in the Age of AI” Maven cohort (Feb 2) is now open.

Take this course if you want to become known as the PM who:

→ Rallies a room around a clear narrative
→ Persuades senior leaders when the stakes are high
→ Shapes strategy through influence – not just good ideas

This week is the only time I ever discount tuition – to reward those who commit early.

And to make this a no-brainer:

→ If your plans change, you can move to a later cohort at the same locked-in rate.
→ If you don’t see results, I offer a 100% tuition-back guarantee. After running multiple cohorts, I *know* the transformation this class delivers.

In other words: high upside, zero risk.

👉🏽 Enroll now to save 33% off tuition: https://lnkd.in/d7STtuG8

. . .
P.S. Once early enrollment closes, I won’t be offering this discount again.

Over 2300+ PMs have signed up for my free Maven Lightning Lesson. 

If you feel like you’re doing great work but keep getting passed up for leadership positions…

I’m here to tell you that you’re not imagining things, and you’re not alone.

The rules change, and no one ever tells you how. I will.

Don’t miss out – RSVP for free! Even if you can’t attend live, you’ll get the recording afterwards.

I’ve heard EVERY excuse in the book for why a PM isn’t getting promoted.

See if any of these sound familiar:

→ “Promotions feel random.”

→ “I don’t have the right relationships.”

→ “One person’s feedback held me back.”

→ “My results are strong, but my scope is too small.”

→ “I’ve done everything right, but the system is rigged.”

→ “I’m in the wrong company – there’s no growth here.”

→ “I’m not the self-promoting type. I just focus on my work.”

→ “My peer got promoted, and I’m clearly better than them.”

→ “I’m overworked – I don’t even have time to think about this.”

→ “I’ve been in this role for X years. It should’ve happened by now.”

If you’ve used one of these excuses –
you’re not broken.
I’ve said many of them myself.

These excuses aren’t the reason you’re stuck.
They’re the symptom.

The real issue is this:
As you move up, the rules change – and no one explains the new game.

The skills that made you a strong PM will NOT get you promoted into leadership.

The leap from Senior PM to Product Leader requires a completely different operating system.

That’s exactly what I’m teaching in 3 days.

- - - - - -
💬 Move Up the PM Career Ladder, FAST!
📅 Tuesday, January 27
⏱ 30 minutes
💸 Free (recording included)

I’ll break down:
• What actually changes as you move up
• Why great PMs plateau
• 5 concrete techniques you can apply immediately to start operating like a Product Leader
- - - - - -

This is the last call.

Even if you can't attend live, register to get access to the recording.

If you’ve been stuck, frustrated, or quietly wondering “what am I missing?” – this session is for you.

👉 Register free: https://lnkd.in/eVckANk9

There is a specific trap that kills PM careers.

It happens to the best of us.

You are good at your job. You ship features. You manage the backlog. You keep engineering happy. Management loves you. But they don’t promote you.

They keep you exactly where you are.

Why? Because they trust you to execute, but they don’t trust you to lead.

Execution is about managing certainty. Leadership is about managing ambiguity.

To break free from this trap, you have to stop reporting on “here’s what we’re doing” and start selling “here’s where we’re going.”

That requires one specific skill: Persuasion.

It’s the ability to walk into a room of conflicting opinions and walk out with a unified direction. It is the only skill that matters for the jump from Senior PM to Director/VP.

I am teaching this exact skill set starting Monday, Feb 2nd.

This isn't about "presenting better". It’s about learning how to: → Explain your thinking when the answer isn’t obvious → Frame trade-offs so leaders trust your judgment → Turn messy inputs into decisions people align behind

⚠️ Enrollment closes in 72 hours.

Don't let another Monday go by stuck in the execution trap.

👉 Enroll here: https://lnkd.in/gWc7t4sc

P.S. Careers don’t stall because of lack of talent. They stall because of delayed decisions. If you're telling yourself "now isn't the perfect time," remember: next Monday will look exactly like this Monday unless you do something different. The only real risk is staying where you are.

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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