Adrienne Tom

Co-Founder | Small Business and Productivity Coach for Career Pros

Every executive has a career story. The difference between being overlooked and being invited forward is how clearly your story demonstrates impact and relevance at the next level.

Too often, resumes read like job descriptions. Responsibilities are listed, but context is missing. Outcomes are implied instead of proven. Yet, boards and decision-makers are not looking for what you were responsible for. They are looking for evidence of how you think, what you drive, and where you can take their organization next.

For 15+ years, I’ve partnered with C-Suite, VP, and Director-level leaders across Canada and the U.S., translating complex careers into executive narratives that hold up in boardrooms, recruiter conversations, and high-stakes decision environments. This work is not about job searching. It’s about positioning, readiness, and control of your professional narrative before opportunity requires it.

I help leaders uncover the stories behind their successes and translate them into persuasive, market-ready career files. Stories that make decision-makers stop and pay attention.

As Canada’s first Certified Executive Resume Master (CERM), a 32X award winner, and a 10X certified career professional, I bring expertise in executive resumes, LinkedIn profiles, and leadership branding that position leaders as the candidate of choice. 

How I help executives tell their story:
◆ Craft executive resumes and narratives that go beyond ‘responsibilities’ to decision-level impact
◆ Unearth results and value that are not easy to spot yourself
◆ Translate experience into strategic stories that resonate with boards and hiring teams
◆ Deliver polished, market-ready career files aligned to next-level leadership goals

The executives I work with have gone on to:
• Step into C-Suite, VP, and Director roles with greater confidence and leverage
• Strengthen visibility with boards, investors, and executive search firms
• Secure roles with Fortune 500 companies, high-growth firms, and complex, multibillion-dollar organizations

Recognition and expertise:
⚜ Featured in Forbes, The Globe and Mail, NBC News, and Calgary Herald
⚜ Named one of LinkedIn’s Top Voices in Job Search and Careers
⚜ Certification Assessor for Career Professionals of Canada
⚜ Published in 7 resume and cover letter books

Your career story is your competitive edge. Let’s showcase your leadership and open doors. Explore my client recommendations below or visit my website to learn more. 

Calgary Executive Resume Writer | LinkedIn Profile Writer
🌐 www.CareerImpressions.ca
📧 adrienne @ careerimpressions .ca

LinkedIn Profile Writer >> Executive LinkedIn Content and Strategies to Get You Found!

⚜ Looking to get your LinkedIn profile to bubble to the top of search results? Want to build a robust executive online presence? 

⚜ I develop LinkedIn profile content, from top to bottom, for forward-thinking executives, directors, and managers across North America to help them get found and hired faster.

LinkedIn services include:

✔️ Complete LinkedIn profile audit

✔️ Creation of customized, SEO-rich LinkedIn content to increase your profile's visibility on the #1 site for recruiters and hiring managers (over 95% of hiring personnel use LinkedIn)

✔️ Content creation for the Headline, About, Education, and 3 Work Experiences

✔️ Content suggestions for your Volunteer, Accomplishments, Skills, Awards, and Interests sections. 

✔️ Recommendations for all other sections, photo tips, and more

✔️ LinkedIn user guides and engagement strategies (you must also get active on the site to get found!)

▶️ In 2020, I was named a LinkedIn Top Voice for elevating essential career and job search topics and driving important business conversations on the platform.

One of the fastest ways I “scare off” potential clients has nothing to do with cost or timing. It’s the moment I ask: 

“What specific job are we targeting?”

Often, I hear replies like:
“I want to keep my options open.”
“I’m exploring a few different paths.”
“I don’t want to narrow too early.”

I get it. Exploration is smart. Careers evolve. Few people move in straight lines. But here’s the part that matters a lot.

You can explore.
Your career files can’t.

The second a resume or LinkedIn profile tries to speak to everyone; it stops working for anyone.

I'll break down why narrowing the target matters more than people expect.

First, it makes you easier to place.

Hiring managers aren’t reading with curiosity. They’re scanning for fit. They want to quickly answer one question: can this person solve my problem? When your file is written for a specific role, level, or type of environment, your experience becomes easier to understand and easier to advocate for.

Second, it forces better decisions.

A strong resume isn’t a complete record of your career. It’s a set of choices. What stays, what goes, what gets emphasized. Without a target, everything feels important and details expand. With a target, the story tightens fast.

Third, it creates a clear through-line.

When a document is built around a defined direction, patterns show up. How you lead. How you think. The scale you operate at. The kind of problems you’re trusted with. The reader doesn’t have to connect the dots. You’ve already done that work for them.

Fourth, it increases perceived confidence.

Ambiguity, even when unintentional, often reads as uncertainty. Candidates who know where they’re headed tend to be taken more seriously, even when they’re open to a few different options.

And yes, you can absolutely have more than one job target. What that usually means, though, is more than one resume. Sometimes more than one positioning strategy.

A COO story is not the same as a CMO story.
A fractional role may be framed differently than a full-time role.
A move into a regulated environment isn’t positioned the same way as a growth-stage company.

Trying to make one document flex across all of that usually produces something generic. And generic is expensive in this market.

When I push for a narrowed job target, I’m not trying to limit someone. I’m trying to give their experience a real chance to land.

You can keep your options open. Just don’t ask one document to do five jobs.

A quick gut check: if a hiring manager skimmed your resume for ten seconds, would they know exactly what role you want next and why you make sense for it? That’s the goal. 

P.S – many times prospects leave (refusing to narrow their target), try things their way, and then return to me a bit later… defeated but more open-minded to the process of writing for one job. They learn the hard way that it works!

32X Award-Winning Executive Resume Writer | Positioning C-Suite Executives, VPs, and Directors for Their Next Move Through Executive Narrative and LinkedIn Authority ٭ Branding and Career Storytelling ٭ Board Resumes

Executives often lead their resumes with responsibilities and tenure.
What they were responsible for. How long they were there. The problem?

Responsibilities are assumed.
Time served is expected.

Impact is not.

Leadership on paper comes from owning outcomes. From showing what accelerated, shifted, stabilized, or improved because you were there.

A strong executive resume does not simply document a career.
It makes a case for value created.

Quick tests:
If a bullet starts with “Responsible for,” rewrite it. That is blah! 
If a bullet point could be claimed by anyone in the same role, rewrite it again. Be more specific.

The goal is not to prove you had the job.
It’s to prove the difference you made while doing it!

Free resource incoming! 👉  Most job seekers aren’t stuck in the resume writing process because they lack experience. They’re stuck because they’re guessing what a “good” resume should look like, what should be included, what to cut, and what format or structure might be helping or hurting them.

That’s exactly why I put together my resume kit. I want to help remove some of the guesswork around resume writing and design. 

Inside this kit, you’ll find:

• A professional, ATS-friendly 2-page resume template that works across industries
• Font guidance, spacing, layout, and hierarchy already done for you
• Easy editing in Word, no design skills required
• Bonus templates for your cover letter and reference sheets
• AND, a resume writing guide to walk you step-by-step through the creation process

In other words: structure, clarity, and direction, all in one place.

If you’ve ever thought, “I know I’ve done good work, but I don’t know how to show it on paper,” this kit was built for you.

And this week only, I’m making the entire kit FREE. 🎉 

Why?  Because job seekers need a break. 

It's tough out there. Worrying about which resume format to use shouldn't be part of that challenge (nor do I want job seekers using bad templates found on Canva).

If this kit can remove even one layer of stress, or replace guessing with clarity, then it’s doing what I intended it to do.

I've had dozens of job seekers use this format, and many return to share that it was easy and helpful!

This week only (Jan 17-23), use promo code KIT100 at checkout to get the entire resource kit at no cost: https://lnkd.in/gbkNDtSe 

Hopefully, with this resource, job seekers can move forward with a little more confidence.  
====•

Please repost to help others! ♻️
For ongoing resume help and resources visit my profile and click the 🔔 to get notified when I post.

*This product is not for redistribution or resale. It is for individual use only

The reality heading into 2026 is that resumes are no longer judged on completeness or tenure alone. Instead, they are analyzed for clarity, relevance, and proof. Readers want to know, quickly:

What do you do best, who do you do it for, and how you can solve our problems.

The biggest resume trends I’m seeing for 2026 are centered around discipline.

Clear job targets.
Audience-first writing.
Results, not responsibilities.
Tighter stories. Fewer assumptions. More intent.

I’ve pulled together 26 resume tips for 2026, based on what’s working in today’s market and where hiring expectations are headed next. The article breaks down each tip with context, examples, and practical guidance so you can apply them without guessing.

If you want a resume that holds attention and earns trust in 2026, read the full article with resources on each tip:  https://lnkd.in/guQHQQzD

26 Resume Tips for 2026

Over the past year, I’ve watched a career dynamic unfold for several senior leaders in my network, and each time it happens, it raises the same quiet question for me: how can we help these professionals plan better?

Here’s the situation.

These were executives in stable, well-compensated roles, respected internally, not actively looking for work, and operating under the assumption that career preparation could wait because nothing felt urgent or unstable in the moment.

Then conditions shifted.

Sometimes the shift happened because a new executive entered the picture with a different mandate, or a restructure quietly changed reporting lines and decision authority. Sometimes it was because a business unit was shifted or sold. In one case, the role itself remained, but the influence and scope that once defined it slowly disappeared.

What caught these executives off guard was not the change itself, but how difficult it felt to clearly articulate their value when it suddenly mattered. 

Years of experience, outcomes, and leadership decisions lived comfortably in their heads, yet very little of it had been translated into a story that could travel, land, and resonate externally.

These were thoughtful, high-integrity leaders who had delivered meaningful results over long careers. Yet when disruption or opportunity appeared, they were forced to start building clarity under pressure rather than drawing from something already prepared.

Each eventually arrived at the same realization.

Career positioning is not a reactive job search task. It is an ongoing leadership responsibility.

They had to stop what they were doing and actively invest in reframing their resumes, LinkedIn profiles, and professional narratives. Not because they wanted to, but because they had to. And for some, the delay in getting 'caught up' cost them opportunties. 

I’m genuinely curious, what guidance would you offer a senior leader who believes they are too busy to prepare, right up until they are not?

The most expensive bag of chocolate I've ever purchased is this bag of 50% dark chocolate chips. Why?

Because on Friday night, my dog found them.  

We made the costly error of leaving these chocolate chips within reach, and he did what dogs do. 

Being tiny, and eating more than half the bag, the situation was bad. We were able to act fast, but it was a long 24 hours (and ongoing) of emergency vet visits, monitoring, tests, and anxiety.

A simple oversight on our part became expensive, stressful, and unfortunately entirely preventable.

This pattern can also be found in job searches (not the chocolate part, but the oversight and anxiety). 

Some people don’t fail in their searches because they lack experience, intelligence, or work ethic. They get burned because something critical was left exposed.

Maybe it is an unfocused resume, or a LinkedIn profile written for “everything". A vague narrative that doesn’t clearly say where they fit next.

Like unassuming chocolate on the counter, these gaps sit there quietly until the wrong moment. Then they cost you interviews, momentum, and time/$$ you didn’t plan to lose.

Strong job search strategy isn’t about reacting when something goes wrong. It’s about risk management before the emergency.

Don;t wait until you need a role to clarify your value. Avoid leaving leave your career story open-ended and hope the market interprets it correctly or kindly.

The most important career strategy may feel unnecessary or unimportant -- until it suddenly isn’t.

If your resume, LinkedIn profile, or positioning could be misinterpreted, diluted, or ignored, that’s not a small issue. It’s exposure.

And exposure can get expensive.

I'm hearing this more and more: “I need a resume ASAP.” The urgency is understandable, but the stress that follows is entirely predictable and avoidable. When a search starts under pressure, people rush, second-guess their story, scramble for metrics they never documented, and ask a resume to do work it was never designed to do on short notice. 

That is not a writing problem. It is a timing problem.

If you waited until you needed a resume before writing one, you waited too long. 

A strong resume is not an emergency document. It is a necessary career tool. Get it ready before you need it to be ready. 

Always have your resume current, intentional, and aligned with where you want to go next, because opportunities rarely wait for you to catch up.

I don't write rush resumes, and I'm typically booked at least a few weeks out, so I have to turn down every request for a resume ASAP.  This adds even more stress when people realize they can't access quality help quickly. 

If you don't have a good resume, the time to get it ready for any possible opportunity or future job change -- is NOW (the recent news about layoffs at places like Amazon is a good example of why). 

=====
I regularly share resume tips and resources. Hit the bell on my profile to get notified when I post. 🔔

Personal branding is what turns “impressive background” into “obvious hire.” It clarifies what you are known for, the problems you solve best, and why your experience matters now in this market.

I break down personal branding, and its importance in resumes, in a new article, including how it shows up on paper and what hiring decision-makers are actually scanning for in 2026.

If your resume feels accurate but not compelling, this is likely the missing piece.

Read it here:

Open article: Executive Resume Writing: Why Personal Branding Is the Key to Standing Out - Career Impressions by careerimpressions.ca

Executive Resume Writing: Why Personal Branding Is the Key to Standing Out - Career Impressions

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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