I am an accomplished Product Management expert and instructor. After working 15 years in small and FAANG companies, I moved on to helping others excel in their product career. My online courses were attended by over 20,000 people, and hundreds of them got a new job thanks to my help. I am also a co-author of a best-selling book, "Next-Gen Product Manager," and a successful live event speaker.

I'm currently focusing on growing my Newsletter, tending to live cohort training I lead with Aakash Gupta, a fellow product creator, and growing as a speaker and in-house trainer for companies that seek my help.

If you need my help, make sure to contact me today on LinkedIn or email me at contact@drbartpm.com.

Bart Jaworski

I am terrible at design, but I can still guide my teams to create awesome-looking products.
So can you! How?

When working with a designer, you should not focus on details like button layout or colors. That doesn't mean you should simply leave the design team to do all the work for you.
Here are 6 ways to make the best out of this relationship:

1) Explain what you want to achieve
It's best to know the goals the design needs to reach and what problem you are solving. Details can come later.

2) Start with a simple wireframe to show your high-level vision
It's a great kick-off point. You need no talent, just an idea to put on a screen!

3) Share your inspirations
You may have seen another product that solves a similar problem elegantly. Or something you'd use if you were creating the design yourself? All you need is a screenshot and perhaps some pen and paper suggestions.

4) Acknowledge competencies
Make sure that the team understands they are to create their design, not draw what you asked. They are the experts!

5) Set milestones and check-in points with your team
People will take as much time on a task as they are given. Checkpoints will also allow you to identify if the design is going in the wrong direction. Again, Notion will be a great tool to monitor progress.

6) Get the rest of the team involved
Allow the developers and other stakeholders to enter the design brainstorm. You will get a diverse outlook and might avoid technical issues.

Ok, but what if you do not have any designers and you still want to present excellent work? Or you want to walk into a design review with something that already makes sense before design even starts?

That is exactly where Figr, partner of today's post, fits in. With Figr, you can start from your real product. Most AI design tools jump straight to mockups from prompts.

Fast, yes.
Useful, rarely.

Figr works differently. Instead of generating mockups from a blank prompt, it starts with what you already have. Your live product, screen recordings, analytics, and even your design system. So you're not copying some other app's UI. You're building on your own.

What surprised me is how it picks up on your actual flows and suggests designs that make sense for your product logic, not generic best practices. It pulls from thousands of real-world UX patterns, but applies them to your context.

The other thing I didn't expect: it catches edge cases I'd missed. It finds how similar products actually handled them.

Remember, the goal isn’t about faster mockups.

It is to solve problems faster. 

So when you hand it to your designer, you're handing them clarity, not a puzzle.

How do you come up with great designs for your products?

Let me know in the comments.

Also, check out today's post partner: https://figr.design/

#productmanagement #productmanager #design

Dr Bart Jaworski

Become a great Product Manager with me: Product expert, content creator, author, mentor, and instructor

You know what the thing is that Product Managers get mostly wrong? They chase details while they should focus on the big picture. How to escape this trap?

Listen, I get it. A product manager is responsible for the end shape of any product and release, so no wonder you may feel compelled to micromanage every detail.

Even worse, your stakeholders might force you to do that, by putting a lot of pressure on you (after all, if you fuck up, it's like they did as well).

But it's simply not the way, not the proper way. At least, unless you prefer to be a product manager in title only, and instead focus on project and engineering management.

At the same time, it's not like I will tell you this and magically you can shift your attention from detail-oriented to high-level. You need to set this up properly. 

How?

1) Delegate the responsibility for quality to your team
Your engineers and designers are professionals. Trust them to deliver quality work. Set clear standards, provide context, then step back. Create systems where quality is everyone's job, not just yours. Regular code reviews, design critiques, and testing protocols should run without your constant oversight.

2) Ensure you won't proceed without a stellar strategy and goals that you can monitor with metrics
No initiative moves forward without a crystal-clear "why" and measurable success criteria. Before diving into features, nail down the business case, user problem, and specific metrics you'll track. This becomes your north star when everyone else gets lost in implementation details.

3) Secure the support of your manager
This part is political, and it matters more than you think. You need air cover. If your manager expects you to chase Jira tickets, you’ll never have the space to think strategically. Fix that first.

4) Put extra care into product requirements and background for any initiative
Invest heavily upfront in comprehensive PRDs that capture context, user stories, and success metrics. The more thorough your requirements, the less you'll need to micromanage implementation.

5) Define your future focus outcomes
It's best to translate your future-focused efforts into something tangible. Otherwise, you may end up working a lot with nothing to show for it.

Only with this will you find the proper space that will fall into place naturally and will allow you to do proper product management. After all, you are there to secure a stable and prosperous future for your product, not just the next sprint.

Are you able to focus on the big picture right now?

Let me know in the comments.

Do you want to have a great strategy to land your next product job? Get a new PM job in 2026 with Aakash Gupta and my cohort "Land PM job". We're starting the 2nd cohort next week, after already getting many people hired from the first one. Check it out here: 

www.landpmjob.com

#prodcutmanagement #productmanager #strategy

It's so frustrating that great product managers won't get a job unless they are also great at job interviews. Why is that? How to correct it?

Before the promised 8 reasons, let me note that there is nothing wrong with this discourse. Knowing you are not great at product interviews should only motivate you to improve. 
Having said that, here's why theory is so different than reality:

1) Lack of clean narratives
Interviews love tidy stories: one problem, one action, one outcome. Real product work is messy, circular, and full of tradeoffs that do not fit a STAR template. A great PM often tells the truth instead of polishing a perfect arc. And truth rarely sounds impressive in 30 minutes.

2) Too much context to compress in little time
Strong PMs carry years of product, domain, and organizational context in their heads. In an interview, that becomes a curse. Explaining why a decision mattered requires ten minutes of setup, and you only get two. Shortcuts sound shallow. Depth sounds rambling.

3) Real impact is often invisible
Great PMs spend most of their time preventing disasters, aligning people, and killing bad ideas early. None of those ships. None of those screenshot well. Interviews reward visible launches, not the quiet work that saved the company millions.

4) Refusal to oversell
Bad PMs exaggerate. Great PMs qualify, caveat, and acknowledge uncertainty. They say things like “it depends” and “we were partially wrong.” Interviewers often hear this as a lack of confidence, not intellectual honesty.

5) Discomfort with hypotheticals
Interview questions love imaginary worlds. “How would you design X?” Great PMs are wired to ask for data, constraints, users, and context before answering. Pushing back or asking clarifying questions can be misread as avoiding the question.

6) Optimization for outcomes, not optics
In real work, you optimize for long-term outcomes, team health, and sustainable delivery. Interviews optimize for sharp answers, bold claims, and fast thinking. Different muscles. Different game.

7) Assumption games vs actual data-drivenness
Interviews often reward confident assumptions made on the spot. You are expected to “pick a direction” quickly and defend it. Great PMs do the opposite: slow down, ask for data, validate hypotheses, and avoid premature conclusions. In a real product, guessing is a risk. In an interview, not guessing fast enough can look like hesitation.
 
Is this also your job experience? 

Let me know in the comments.

Do you want to excel as a PM and at product interviews? Get a new PM job in 2026 with Aakash Gupta and my cohort "Land PM job". We're starting the 2nd cohort, after already getting many people hired from the first one. Check it out here: 

www.landpmjob.com

#productmanagement #productmanager #jobsearch

It can be extremely stressful when an authority figure, like a manager or CEO, comes to you with a bad idea. Here are 8 ways to say "No" effectively:

1) Force the CEO/manager into a discussion
Don't just say "yes" or nod. If the CEO/manager wants you to do something, you need to do your due diligence. Having a conversation is the first reasonable step.

2) Ask for the reasoning
You need to understand what user/business problem the CEO/manager wants to solve. Perhaps you can offer a better alternative? Or at least understand that the request is not that stupid from a wider perspective.

3) Ask for data
To have the request added as a task to the backlog, you need data to allow prioritization and to estimate the impact hypothesis. If the figure of authority gave you the request, perhaps it comes with some handy data. You wouldn't act on an opinion, now would you?

4) Ask for time
If there is no data and no good reason, you will need time to look into this. Anything stupid on the surface can be a hidden gem (say, the original premise of Twitter: Who would want to run a microblog with only 160 chars to work with?), and you need time to evaluate.

5) Perform your research
Is there any data on the request? How did competitors solve the same problem? Did science tackle it? Was it tried in the company before? You know, the usual backlog item stuff :)

6) Prioritize the request in your backlog
Now that you can assess the impact, certainty, risk, and perhaps the effort needed, it's time to see where the CEO/Manager mandate items rank in the list of priorities. If it's a round the bottom or middle, time to:

7) Arrange a follow-up meeting
You are now able to speak with the stakeholder again on a more equal footing. This should be set as soon as possible. Perhaps you will need to timebox your research phase to adjust to the other side's calendar.

8) Demonstrate the cost of prioritizing the request
Show the CEO/Manager all your other plans, the problems they solve, and the expected impact. The stakeholder will have a clear visibility of where his/her request ranks in comparison.

Afterward, you shouldn't even need to say "No". The CEO should withdraw her/his request!

Would you be able to say "No" to a high authority figure?

Let me know in the comments.

Are you sick and tired of a role that forces you to say "No" too often? Time to land your next product job. Get a new PM job in 2026 with Aakash Gupta and my cohort "Land PM job". We're starting the 2nd cohort next week, after already getting many people hired from the first one. Check it out here: 

www.landpmjob.com

#productmanagement #productmanager #leadership

Original meme template: shencomix

Senior AI PM makes $184K (US) with Meta, paying $737K+. An analysis of over 100+ AI PMs job offers shows 5 most common skills they're looking for:

1. AI Product Lifecycle (82%)

Example: Owning product strategy, vision, and go-to-market (AWS).

Start here:

→ How to Create an AI Product Strategy: https://lnkd.in/ds9EQ7hS
→ How to Design a Value Proposition: https://lnkd.in/du9zcZDA
→ 5 GTM Principles You Should Know as a PM: https://lnkd.in/dEPMtmKU
→ AI Evals for PMs: https://lnkd.in/dHUFrkVs
→ How to Evaluate AI Agents: https://lnkd.in/dKM9YvNW

2. Cross-Functional Collaboration (76%)

Example: Collaborate with cross-functional teams, including engineering, UX/UI, and business stakeholders (Google).

Start here:
→ WTF is an AI Product Manager: https://lnkd.in/dBZUmDHq
→ What is Product Discovery: https://lnkd.in/dNUB__n3
→ AI Prototyping: https://lnkd.in/dPaKYv7S
→ Experiments Library: https://lnkd.in/dvUisjR2
→ AI PRD Template: https://lnkd.in/dQ2jiMfr
→ Product Discovery Course (free for premium subscribers): https://lnkd.in/eianWDnk

3. Gen AI, LLMs, and AI Frameworks (69%)

Example: Experience with Generative AI or LLMs (T-Mobile).

Start here:
→ Neural Networks, Transformers, and LLMs: https://lnkd.in/dveWsD9f
→ AI PM Glossary: https://lnkd.in/dVCki_gf
→ 14 Prompting Techniques for PMs: https://lnkd.in/dnCGVH6a
→ Fine-tuning for PMs: https://lnkd.in/dwJfQiC7
→ Context Engineering for PMs: https://lnkd.in/d_XVN5KV
→ AI Agent Architectures: https://lnkd.in/dnmQ_kP8
→ The Ultimate Guide to n8n for PMs: https://lnkd.in/dKYZ5Y3n

4. Data-Analytics for AI (67%)

Example: Define and track key performance indicators (KPIs) for product success (ZnA).

Start here:
→ How to Find The Right AI Product Metrics: https://lnkd.in/d9tZcvVT
→ Cohort Analysis: https://lnkd.in/daJ5KQWW
→ Funnel Analysis: https://lnkd.in/d8Gt5ECV
→ AARRR Metrics: https://lnkd.in/dpM_9A55
→ North Star Metric: https://lnkd.in/dfBNB77S

5. AI-Driven Customer Experience (48%)

Example: Create conversational experiences that are personalized, relevant, and grounded in trusted information (ServiceNow).

Evals are not enough. Develop an AI intuition by building, not theorizing. Step-by-step guides (no coding):
→ RAG chatbot: https://lnkd.in/dew--RqD
→ AI voice agent: https://lnkd.in/dSW6_KiT
→ Multi-agent research system: https://lnkd.in/dpBGF3pq

Thank you, Paweł Huryn for the original analysis.

Which skill will you prioritize?

Let me know in the comments.

P.S.: If you want to become an AI PM, then Product Faculty's #1 AI PM Certification, built with OpenAI’s Product Lead, Miqdad Jaffer, is for you.

1. Master the full AI product lifecycle
2. Build scalable, production-ready AI products
3. 3,000+ AI PMs graduated
4. 600+ reviews - highest on Maven.

The next cohort starts NEXT MONDAY, the 26th. Secure your spot with 500 USD discount using my link:

https://lnkd.in/diQHbFth

#productmanagement #aipm #ai

If you’ve ever tried to evaluate translation quality in your product, you know the pain. 
However, there is a solution:

Actually, it's best to use MQM (Multidimensional Quality Metrics), the gold standard for evaluating translation quality, adopted by major localization teams.  In practice it often means spreadsheets, manual setups, or heavyweight enterprise tools that slow everything down.

Not anymore!

Alconost, my ongoing partner and patronm, just released a free MQM Annotation Tool that makes translation quality evaluation actually usable in real workflows.
What it does well:

• Allows you to evaluate human or machine translations using the full MQM error taxonomy 
• Supports custom error weights and pass/fail thresholds aligned with business quality goals 
• Visualizes error distribution and segment-level scores 
• Exports results as CSV, TSV, JSON, or PDF for reporting and vendor discussions
  
Works instantly in the browser, no setup, no install needed.

This is especially useful for: 
• Multilingual products
• Localization managers benchmarking
• MT quality LSPs documenting quality in a structured way
• ML and NLP teams are building annotated datasets
• Product teams that need objective, explainable quality signals
  
If MQM ever felt 𝘳𝘪𝘨𝘩𝘵 𝘪𝘯 𝘵𝘩𝘦𝘰𝘳𝘺 𝘣𝘶𝘵 𝘱𝘢𝘪𝘯𝘧𝘶𝘭 𝘪𝘯 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦, this tool is worth a look.

👉 Check it out and support the launch on Product Hunt: 

https://lnkd.in/dN4eA8zF

What's the best translation failure you have ever seen that this tool could have prevented?

Let me know in the comments.

#productmanagement #productmanager #localization

You are an experienced and talented Product Manager, yet you struggle through painful job interviews. 
Why? How to overcome this? Here are a few tips:

First, let’s get one thing straight:  There is nothing wrong with you.
 
Being good at PM interviews is a different skill from being good at product management. Just like discovery, stakeholder management, or roadmap thinking, it’s a muscle. If you don’t train it intentionally, it will fail you under pressure.
 
I can’t give you time.  But I can give you guidance.
 
Here’s what actually helps:

1) Learn and improve with every interview
Treat interviews like another chance to practice them. After each one, summarize what aspects still need work. Patterns show up fast if you look for them. Most candidates repeat the same mistakes for months because they never do a proper retro.

2) Practice common interview questions
This way, you will have your best answer at your fingertips rather than making it up on the spot. You will also avoid common pitfalls, for example, “Tell me about a failure” is rarely about failure, but about learning and improving from it. 

3) Product Management fundamentals > jumping to solutions
Strong PMs often lose points by skipping framing. They rush to solutions because that’s what real work rewards. Interviews reward the opposite. Problem definition, user clarity, constraints, and success metrics. If you don’t anchor there first, even a great idea sounds random.

4) Use a visual aid to help structure your long answer
A simple mental model works wonders. “I’ll walk you through this in three steps,” immediately calms the room. It signals control, not hesitation. You’re not thinking out loud. You’re leading the conversation.

5) Start with a short, 1-sentence answer and add a summary at the end
Interviewers get lost. They always do. Open with your direction in one sentence. Then explain. Then close with a crisp takeaway. This isn’t redundancy. It’s empathy for a tired human on the other side of the table.

6) Keep relaxed, smiling, and confident, even if it is just an act
Interviews are emotional theater. Confidence is often perceived before competence. You don’t need to oversell, but you do need to look comfortable owning your decisions.

Listen, following those pieces of advice might not land you a job at your next interview. It is a process to follow and improve with every try, until it's your second nature, just like it is to find the right product solution to the hardest of challenges.

Did you ever struggle through a job interview?

Let me know in the comments.

Do you want to excel as a PM and at product interviews? Get a new PM job in 2026 with Aakash Gupta and my cohort "Land PM job". We're starting the 2nd cohort, after already getting many people hired from the first one. Check it out here: 

www.landpmjob.com

#productmanagement #productmanager #jobsearch

Controversial opinion: Product Managers don't need a roadmap. They needs goals to achieve with a deadline. How to find a realistic middle ground?

Here are 7 actions to consider:

1) If possible, commit to working on goals, not features
This is the ideal situation: Your roadmap is showing how you will timebox different challenges and goals over the coming months. However, let's be honest: Hardly any management will accept such a roadmap. In that case, you will need to supplement this with a set of proposed deliverables that you MAY work on that should address your challenges. 

2) Show how your propositions fit into the wider narrative
If you can highlight how your proposed changes address your goals, but also fit into product/company vision, and strategy, and answer market/user needs, you will be a Product Management GOAT (Gen Z readers, how I used that correctly ;)? ). Trust me, this is a roadmap masterclass in this single paragraph.

3) Don't go too granular
You want to impress and inspire your stakeholders. The roadmap meeting will be a long one, and if you dive too deep, you will confuse your audience and lose the narrative. Do it Hollywood style: show, not tell! In any case, you should focus on the details over the course of the year, not when you are launching.

4) Include visualizations
If you have any wireframes or early designs, show them! They might change 1000 times later, but showing early concepts will show that you are nicely progress with your plans. This will build confidence that the presented outcomes will land when you say they will land.

5) Make it easy to access and check the progress
This will save you time on updates - either stakeholders will be able to check your progress on their own, or it will be easier for you to create a "check-in" presentation. 

6) Involve the team
Before showing the roadmap to stakeholders, show it to your team for input. Make them a part of the roadmap, not receivers. They should feel proud to follow it and feel co-responsible, not be surprised by your propositions.

7) Stress that it's always only a draft, not a commitment
That's right! If you make this a commitment, it will make it harder for you to change direction later if, say, another ChatGPT pops up out of nowhere. You, as the Product Manager, should always pursue the best value for your product, and no roadmap should stop you from doing that!

How do you craft your roadmap? 

Let me know in the comments. 

Do you want to have a great strategy to land your next product job? Get a new PM job in 2026 with Aakash Gupta and my cohort "Land PM job". We're starting the 2nd cohort next week, after already getting many people hired from the first one. Check it out here: 

www.landpmjob.com

It's only this weekend to sign up!

#productmanagement #productmanager #roadmap 

P.S. Thank you 🥞 Carl Vellotti for another inspiring visual :)

What if I told you you can make your company legendary starting with... localization? That's basically the story of CD PROJEKT RED, and it all starts with one game:

In the late 1990s, Poland was not an obvious place to build a global gaming powerhouse. The PC market was small, piracy was everywhere, and publishers treated localization as an afterthought. Then Baldur's Gate arrived and changed everything.
 
When CD Projekt decided to publish Baldur’s Gate in Poland, they made a radically different bet. Instead of cutting corners, they invested heavily in a premium localization. The game was fully translated, professionally edited, and dubbed with well-known Polish actors. 

Manuals, boxes, and extras were treated with the same care as the game itself. All world-class.

I was 13 and hyped as crazy when I preordered the original BG in my home in Poland.
 
The result?

Amazing!
 
Over time, sales exceeded 50,000 units, an extraordinary number for Poland in 1999. This was not just a commercial success. It was proof of something many believed impossible: 

Players were willing to pay for games if they were respected as customers.
 
That single release rewired CD Projekt’s future.
 
First, it gave the company financial stability in a market considered hostile to legitimate sales. Second, it built trust with global partners like BioWare and Interplay, showing that CD Projekt could handle flagship IPs responsibly. Third, and most importantly, it shaped the company’s philosophy: 

Localization is not translation; it is cultural adaptation.
 
Years later, when CD Projekt moved from publisher to developer, those lessons carried over directly into The Witcher. The game’s success came down to narrative authenticity, mature writing, and deep respect for the gamers, the same values first proven with BG. 

The Witcher felt local, grounded, and honest, yet it resonated globally.
 
The pattern repeated with Cyberpunk 2077. Despite its troubled launch, the ambition was unmistakable: massive localization efforts, full voice acting across languages, and an attempt to deliver a consistent narrative experience worldwide. Few studios would even try at that scale.
 
In hindsight, Baldur’s Gate was not just CD Projekt’s first hit. It was their strategic foundation. It taught them that quality builds trust, trust builds loyalty, and loyalty builds global brands.
 
A masterfully localized RPG in 1999 did not just sell hundreds of thousands of copies in Poland. It launched a company on a path to becoming one of the most recognizable names in global gaming.
 
Sometimes, the road to glory starts with a really good translation. 

Those can be provided for you by the partner of this post: Alconost! Whether you need a localization partner for your game, app, or website, they are the best of the best and my trusted patron for years now.

So, what localized game made you fall in love with it?

Let me know in the comments.

#productmanagement #localization #gaming

You know what the most infuriating part of a Product Manager's job is? It's that it all takes FOREVER. But not with Figma Make. Why?

Ideas are fast. Alignment is slow. Execution is painfully slow.
 
And this is exactly why what Figma revealed recently made me stop and think.
 
I had the opportunity to dive into Figma Make, and while there were many impressive capabilities, one thing stood out immediately:
 
Turning fuzzy product ideas into working UI without waiting weeks for handoffs.

Stop me if you heard this one before: You have an important goal or user problem to solve. You get everyone into a room to get the ideas going. Thus, impressive before you know it, amazing solutions pop up every minute, and you feel that success is just around the corner.

You generate tons of notes and napkin designs and high-five each other. After that, it's up to designers to translate it into something the team can realistically refine and take to the sprint.

Then you wait.

However, the designer has a deadline on another important project, not to mention the flu season, which won't spare her.

Weeks later, the 1st real design appears.

By the time the first real screens appear, the original energy is gone. The context is cold. You are basically restarting the conversation.

If only you could have a prototype of your idea right there on the spot...

Well, you can!

This is where Figma Make quietly changes the game.
 
Instead of stopping at notes, frames, or vague wireframes, you can move straight into something tangible.

A real interface.
Editable.
Interactive.

Good enough to react to and get the energy going again.

You finally work as a cheetah, not a snail.

You suddenly have something you can:
• pressure test with stakeholders
• use to anchor roadmap discussions
• something to iterate on with your team
• validate direction before over-investing
 
Does it still need taste, judgment, and human refinement?

Absolutely.
 
But the difference is timing.
 
You are no longer waiting weeks to see if an idea even makes sense visually. You are shaping it while the thinking is still fresh.
 
For Product Managers, this is not about speed for speed’s sake. It is about shortening the gap between thinking and seeing.

Thinking and releasing.
Thinking and testing,
Thinking and... succeeding!
 
And that gap has been killing great ideas for years.
 
Curious what this unlocks for PMs and teams that already live in Figma.
 
What's the first prototype you'd create?
 
Let me know in the comments.

Or better yet... 

Just create it: https://lnkd.in/dHwTHm5k

#productmanagement #design #figmapartner

With AI being everywhere in Product Management, somehow no one talks about how to ace AI product design interviews. Here's how to do it:

Let's start with these 7 pieces of advice:

1) Start by asking a few sharp clarification questions
Show you can reduce ambiguity fast. Make sure to clarify assumptions, limits, and what “success” means. You are signaling maturity, not stalling.
 
2) State your framework out loud before you start solving
Use a visual (paper or e-whiteboard), clean structure: 
users → problems → solutions → prioritize → design → AGI connection → risks. 
This prevents wandering, repetition, and “great idea, messy answer” failure.
 
3) Select the right userbase
A key insight: even if the product is “for pets”, the buyer is the human. Call out user archetypes, then pick one and say why (motivation, willingness to pay, measurable outcomes).
 
4) Prioritize problems using a simple hierarchy
Use a “pyramid” idea: first understand 𝘄𝗵𝘆 behavior happens and 𝘄𝗵𝗲𝗻, to best understand where the problem to solve appears. Interviewers love this because it proves you can identify foundational problems, not just brainstorm features.
 
5) Tie the solution back to the company's mission early and use it to prioritize
When OpenAI says “AGI progress is the metric”, don’t ignore it. Use it as a filter: this product improves multimodal understanding, real-world behavior interpretation, and generalization. That becomes part of the prioritization logic, not an afterthought.
 
6) Generate multiple solutions, then select using explicit criteria
Produce a few ideas, then rank them with a lightweight scoring table: user impact, feasibility, differentiation, engagement. The trick is not do the math, but to show logical thinking under pressure.
 
7) Design core flows, then finish with risks and mitigations
Once you pick a direction, shift into product design mode: onboarding, passive vs active usage, “magic moment”, key design decisions, etc.. End with practical AI risks: hallucinations, privacy, cost, scalability, etc., and concrete mitigations to those issues.

Would you to best understand these points in action?

Check out my mock product design interview with Aakash Gupta, where we show how to utilize this post's guidance:

https://lnkd.in/gptaf3is

Have you already taken an AI Product Design job interview?

Let me know in the comments.

#productmanagement #productmanager #ai

AI can already save you a ton of time, but buying all those subscriptions will get you broke. What if I told you there is another way? There is:

We’re barely at the start of the AI age, yet we already have tools that save hours every week. As a content creator, I feel this daily: writing, editing, image generation, meeting recaps, research…

Without AI, I wouldn't have enough time in the day to do all I need.

Not with kids around.

As a creator, all of that used to mean juggling 5–6 different AI tools. Different UIs. That forces me to constantly switch, not to mention the payments for all those subscriptions.

That’s the real productivity and/or mood killer.

That’s why I gave Genspark, partner of today's post, a try.

It recently crossed $100M ARR in just 9 months, becoming Silicon Valley’s first AI agent unicorn, and is positioning itself as an All-in-One AI Workspace 2.0.

One workspace. One dashboard. One subscription.

From content ideas, to images, to animations, to deep research. You just say what you want to achieve, and it figures out how.

In practice, it replaced three tools for me instantly:
• image generation and style transfer - no more Midjourney and ChatGPT needed.
• animation and short video creation - no more need for Google Gemini.
• research - farewell, Perplexity
  
𝗧𝗵𝗮𝘁’𝘀 just scratching the surface.

And here’s the wildest part.

Genspark is offering 𝘂𝗻𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗔𝗜 𝗖𝗵𝗮𝘁 𝗮𝗻𝗱 𝗔𝗜 𝗜𝗺𝗮𝗴𝗲 𝘂𝘀𝗮𝗴𝗲 𝗳𝗼𝗿 𝗮𝗹𝗹 𝗼𝗳 𝟮𝟬𝟮𝟲.
That means unlimited access to top models like 𝗚𝗣𝗧-𝟱.𝟮, 𝗖𝗹𝗮𝘂𝗱𝗲 𝗢𝗽𝘂𝘀 𝟰.𝟱, 𝗚𝗲𝗺𝗶𝗻𝗶 𝟯 𝗣𝗿𝗼, 𝗙𝗹𝘂𝘅, 𝗦𝗲𝗲𝗱𝗿𝗲𝗮𝗺, 𝗡𝗮𝗻𝗼 𝗕𝗮𝗻𝗮𝗻𝗮 𝗣𝗿𝗼, and more.

There are also, to name a few:
• AI Sheets that turn spreadsheets into a chat
• AI Inbox that summarizes threads and drafts replies
• AI Developer that plans, writes, runs, and debugs code end-to-end
• AI Image / AI Designer for visual assets, edits, and variations
• 
It honestly reminds me of early Netflix. One place. Everything you need. Before fragmentation ruined it.

If you’re serious about output, not collecting AI subscriptions, this is worth trying.

Test it for free: https://lnkd.in/dG4J8WfQ

Then come back and tell me how many tools you were able to cancel.

#Gensparkpartner #WorkwithGenspark

Did you ever want to test your PM skills by creating a mobile app, but the process felt way too complicated? I have a solution for you:

Before I begin, let me just stress that having a side project as a product manager is super important. It's something you can show on a CV. Something to train your product muscles. And something that gives you proof of your product skills, regardless of your issues at work with the team, strategy, tech stack, etc.

When you are your own boss, your product management skills truly shine, and this is an opportunity you don't want to miss. 

And when it comes to creating a mobile app, there is something where you can simply focus on product management and not on the technical aspect. 

Replit has a solution for you! Whether you are prototyping or building a production-ready app, Replit lets you securely vibe code using natural language- with database, auth, and deployment built-into the platform.

However, unlike similar tools, Replit can now help you get your idea not only working, but also live! 

They’re introducing Mobile Apps on Replit, a conversational way to build, test, and publish 𝗿𝗲𝗮𝗹 𝗺𝗼𝗯𝗶𝗹𝗲 𝗮𝗽𝗽𝘀 just by describing what you want.
 
Users can now push their ideas to AppStore without learning mobile frameworks, installing Xcode, or managing complex build pipelines (and whatever other secret signs and languages do iOS developers use to put apps live). 

This is as easy as the following steps:
 
• You describe your app in chat.
• Replit builds it.
• You preview it on your phone.
• And when you’re ready, you publish it.
 
Behind the scenes, Replit handles the entire mobile stack,  from frontend to backend to App Store submission, so creators can focus on the idea, not the tooling.
 
Thus, expect to be able to test the MVP of my future game:

"Spiders on my face!"

This is meant to be a "simple", 8-bit style game, catching the simplicity and absurdly scaling difficulty of games like "Pac-Man" or "Tetris" in 2026.

You can check it out NOW using the QR codes visible on the video attached :)

Why is it an MVP? What do I want to do with it? 

Stay tuned!

In the meantime, check out Replit and create your own games and apps, without any tech experteese:

https://lnkd.in/dzHhirRV

What will you build?

Let me know in the comments.

#productmanagement #productmanager #vibecoding

Most companies still think they have an API problem. They usually don’t. They have an API discovery problem. How to solve it?

APIs exist.
They work.
They scale.

And you know what? AI might be the game now, but API are still the key to connecting to your key partners and customers.

Even so, teams cannot find them. Partners cannot understand them. Every new integration feels like starting from scratch.

You, the product manager, are leaving long-hanging fruit (value, income, clients) on the table!
That is exactly why I am hosting a live session on API discovery, developer portals, and turning APIs into a growth engine.

This will not be a theoretical talk. No buzzwords. No vendor fluff.

We will break down:
 • Why API programs fail even when the tech is solid
 • How scattered docs silently kill adoption
 • What internal teams really need to reuse APIs instead of rebuilding them
 • How modern developer portals change onboarding speed dramatically
 • Where monetization actually makes sense and where it does not

If you have ever heard:

 “Do we even have an API for this?”
 or
 “Integration will take a few months.”

This session is for you.

My guest will be the Digitalapi.ai’s CPO, Srinivasan Shanmuganathan ( Shan ), who will also show how teams solve this problem in practice.

If APIs are part of your product strategy, you do not want to miss this.

Register today for free: https://lnkd.in/d2RdhPxd

Will I see you at the event?

Let me know in the comments :)

#productmanagement #productmanager #api

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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