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.

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

🚀 I am thrilled to announce the upcoming release of my new book: The Future of AI Data Centers - Creating Resource-Responsible AI for an AI++ World! 🚀

Over the past year, I have been on a deep learning journey exploring the physical realities of our AI ambitions. As AI models grow exponentially, so do their compute demands. With rack densities rapidly scaling from traditional 15 kW to 130+ kW and beyond, it became incredibly clear to me that legacy cloud infrastructure simply cannot support this scale.

Why this topic is critical right now: The physical constraints of our power grids, cooling systems, and natural resources are quickly becoming the ultimate bottlenecks to AI innovation. Understanding how to navigate this massive shift to purpose-built, "AI-Native" facilities is one of the most pressing challenges for the future of the tech industry.

The "Resource-Responsible AI" Angle: While researching the thermal management discontinuities and power distribution shifts, I realized that efficiency isn't just a "nice-to-have" ESG goal; it is an existential requirement for scale. This inspired me to develop the Resource-Responsible AI framework. I wanted to shift the conversation to ensure we are treating sustainability—energy efficiency, closed-loop water stewardship, and carbon-conscious computing—as first-order design principles, rather than afterthoughts.

My core message is simple: If we want to build an "AI++ World," we must confront the looming bottlenecks of energy, water, and raw materials head-on. The future of AI must evolve by being fundamentally resource-responsible.

📖 The eBook will very soon be available on the Amazon Bookstore and included in Kindle Unlimited! I will share the official link the moment it goes live.

Grateful for everyone who supported, challenged, and encouraged me along this writing + research journey. Ten people have kindly agreed to review and share their thoughts about the book. 

The topics in the book will also be covered in my LinkedIn newsletter on Resource Responsible AI. [https://lnkd.in/ggUt-rYE]

#AI #AIDataCenters #ResourceResponsibleAI #ArtificialIntelligence

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

Multi-agent AI Systems can unlock real leverage—but only if we treat it like a production system, not a prototype.

Here are 6 common assumptions that increase cost, risk, and operational load:
1) “Adding more agents improves results.”
 In reality, complexity rises quickly—more handoffs, higher latency, more failure points. Right-sizing matters.

2) “The system can reliably self-check.”
 Self-review misses issues. You need independent verification to protect quality and trust.

3) “A successful demo means we’re ready.”
 Demos are controlled. Real users bring edge cases, misuse, and unexpected behavior—often after hours.

4) “It will naturally know when to stop.”
 Without explicit stop conditions, costs can balloon with little progress. Guardrails are essential.

5) “We can address security later.”
 Security is foundational. Retrofitting it later is slower and more expensive than designing it in.

6) “Flexible handoffs are better than clear contracts.”
 Ambiguity increases rework and makes failures hard to trace. Clear interfaces reduce operational friction.

Bottom line: Multi-agent systems succeed when we design for reliability, governance, and operational reality from day one. 

The real takeaway:
 The gap between demo and production isn’t “more features.”
 It’s fewer assumptions.

** Build with AI in Public | Build and share useful stuff ** 

So you are an AI Product Manager / AI Product Builder / AI Engineer.

There is this new development in AI, a new paper and new hot topic that came out. (Remember when MCP came out?) 

What this turns out in reality is that some stakeholder, Technical / Product Leader is going to ask you "Hey did you see the news / tweet / paper on X? What do you think about it? Should we look into it? Y said that this can help and address our problems etc.." 

I have been in that situations many times. To not only grasp and understanding of some new development in AI, but become ready rapidly to have a critical and crucial conversation with different teams and stakeholders. 

Hence I created this AI Paper Analyzer with Google AI Studio to leverage the power of AI to analyze a paper / report and make me conversation ready. 


The AI Paper Analyzer is an AI application designed to solve the "translation gap" between dense academic research and actionable product strategy. For Engineering Leaders and Product Managers, dissecting ArXiv papers to determine technical viability often takes hours of study; this application condenses that process into seconds. By leveraging advanced Gemini models (including 3.0 Pro and 2.5 Flash), it performs rigorous Technical Due Diligence, extracting system architectures, "build vs. buy" recommendations, and implementation stacks. It transforms static text into dynamic SVG diagrams, generates "Red Team" risk analyses, and even synthesizes a "CTO vs. Lead Engineer" audio debate to debate the paper's merits. It turns theoretical breakthroughs into concrete engineering roadmaps, drastically reducing R&D analysis time. It makes an AI professional to get upto speed on a new AI topic and be ready to have the crucial conversation and present their point of view. 

Its simple to use. Simply upload a PDF or paste a URL to a research paper to initialize the deep analysis. The application immediately generates an interactive dashboard where you can explore the system’s architecture (visualized via code-generated SVGs), review a generated executive slide deck, or listen to the technical strategy audio brief on the go. You can listen to a possible conversational experience with a CTO and also visualize the product concept using the integrated Veo video generator. It ensures you have a 360-degree view of the technology before writing a single line of code. 

I have tried it myself on the latest trending papers on Hugging Face / Top paper list from DAIR.AI and the must read AI papers list from Marina Wyss. This has let me "fine tune" the application to reflect the thinking and relevance to AI Product professionals / AI Solutions people. 

Do try and let me know how it works for you. Suggestions for updates, fixes and enhancements are welcome!

I will deploy the app and come back with the link.

Anthropic just showed that agent teams can build serious software: 16 parallel Claude agents built a working C compiler that can compile the Linux kernel. That’s not a demo—that’s a methodology.

But the bigger question is next:

Can agents build a language that lets agents create other agents—safely and portably?

In this article, I argue the agent ecosystem is converging on protocols for tools and communication, yet we’re still missing the most important layer: a portable, framework-agnostic way to define, spawn, and manage agents.

I’m calling this missing layer ACML (Agent Creation & Management Language)—an “agent factory” substrate with runtime spawning, governance constraints, and cross-platform compilation.

If you’re building multi-agent systems in production, this is the layer that will decide whether “agents all the way down” becomes infrastructure… or chaos

How Claude Code Agent Teams could build a self-referential language for AI agent creation — and why the agent definition layer is the most important u

Open article: ACML: An Agent Creation & Management Language — Built by Agents, for Building Agents by Harsha Srivatsa • 7 min read

ACML: An Agent Creation & Management Language — Built by Agents, for Building Agents

* The inspiration for this post came about while I attended a session on Evals organized by Stella Wenxing Liu and Amy Chen. Do check out their upcoming Maven Course on Evals *

During my work on MAS for healthcare, I realized that one should not jump to LLM-as-a-Judge right away. There is still value in doing unit tests which we (I) are very familiar with. These unit tests lay the right foundation for LLM-as-a-Judge. 

Do not jump straight to “LLM-as-a-Judge” because it feels closer to human review.

We should think in 2 layers:
* System contract → did the agent follow rules, tools, safety rails, formats, privacy constraints?
* Semantic outcome → is it correct, complete, and appropriate?

Assertion-based unit tests (Contract layer)
Deterministic, code-based checks.
✅ high reliability (no stochasticity)
✅ cheap + scalable (run in CI, run often)
✅ excellent for regression catching
✅ ideal for: schema drift, forbidden tool calls, missing disclaimers, PHI leakage checks, citation requirements

Multi-agent nuance:
In multi-agent systems, assertions become the connective tissue between agents:
Did Agent A pass the right metadata to Agent B?
Did the Manager Agent override (or fail to override) a safety tool call from a sub-agent?
Did the retrieval agent return evidence IDs that the writer agent actually used?
Assertions aren’t just for final outputs — they’re for handoffs.

LLM-as-a-Judge (Semantic layer)
A model scores “fuzzy” criteria that code can’t cleanly measure.
✅ high value: correctness, completeness, tone, reasoning quality
✅ scalable vs human review
⚠️ reliability varies (prompt sensitivity, injection, judge gaming)
⚠️ cost adds up at scale

A common miss: the judge drifts.
If you change the judge prompt/model, your scores may not be comparable anymore.
* Treat your judge like a product:
* version the judge prompt + model
* track score distributions over time
* re-baseline when judge changes
* keep a fixed “golden set” for calibration

Its about: traceability vs scoring

The hybrid approach that works
Gate → Grade → Calibrate
Gate (assertions): fail fast on safety/compliance/tool/format invariants
Grade (LLM judge): score semantic quality only after passing gates
Calibrate (humans/SMEs): validate judges using human expert labeled golden set

Healthcare example:
Patient portal triage message:
“Chest tightness for an hour, worse when walking, sweaty + nauseous.”
Assertions must catch:
Must escalate: “Call 911 / go to ER now”
Must not diagnose: “You are having a heart attack”
Must include required safety language + next steps
Must satisfy privacy + formatting contracts
Only after passing gates should the LLM judge score:
clarity, completeness, tone, appropriateness, evidence alignment

The lessons from healthcare AI agent work applies to other domains where trust + traceability matter in the AI agent systems. 

Bottom line:
* Assertions are your seatbelts + black box recorder.
* LLM judges are your scaled reviewer.

Introducing Agentic Genesis 

I've been quietly thinking about something fervently in the last few days and bouncing them off with my friend Anne Cantera.  Today I want to share it.

If AI agents can write production software and coordinate in teams… why are we still designing agents one-by-one, by hand?
Most frameworks (CrewAI, LangGraph, AutoGen, Google ADK) still require a human to manually specify:
* roles
* tools
* coordination patterns
* constraints
 …agent by agent.

That feels like writing assembly in the age of compilers.

If “vibe coding” was Phase 1 (prompt → code)…
 and Andrej Karpathy Agentic Engineering is Phase 2 (architect → agent teams)…
Then Phase 3 is what I’m calling:
Agentic Genesis — AI agents that design, create, manage, and evolve other AI agents.

Not because humans disappear.
 Because humans move up a level:
From designing every agent → to governing agent populations.

A meta-agent explores the design space, proposes architectures, spawns specialists, evaluates outcomes, and iterates — inside enforced guardrails.

The human stops designing every agent by hand. The human starts governing agent populations.

Why "Genesis"? Because genesis means origin — the beginning of something self-sustaining. In Agentic Engineering, agents are tools humans wield. In Agentic Genesis, agents become the origin point of other agents.

The flagship vision — where this all comes together.

Imagine this: you describe a business idea in a paragraph. A Genesis Agent — the meta-agent at the top — analyzes your concept, decomposes it into functional departments, and designs the entire organizational architecture. But before it spawns a single agent, it produces something first: a Company Handbook.

Inspired by GitLab's legendary 2,000-page handbook  — the Agent Company Handbook becomes the governance-as-code layer for the entire agent organization.

The human? The human is the founder, the board, and the regulator. They defined the business idea, set the governance boundaries, and review outcomes. They govern — they don't micromanage 50 agents.

That's Agentic Genesis. Not agents as tools. Agents as the origin of organizations.

I'm going public because I would love for community help me shape this.

Every powerful technology in computing history eventually learns to produce itself. Software that writes software. Infrastructure that provisions infrastructure. AI agents that build AI agents isn't a feature — it's an inevitability.

The question is whether we build the safety infrastructure before the wave hits, or after things break.

Moltbook showed us what "after" looks like. I'd rather build the "before."

The Agentic Genesis era is beginning. Let's build it right.

#AgenticGenesis #AgenticEngineering #AIAgents #MultiAgentSystems #FutureOfAI  #BuildInPublic

Hallucination Sense: The Diagnostic Intuition AI Product Builders Need (Alongside Evals)

Everyone knows large language models hallucinate.
It’s almost become shorthand in boardrooms and product reviews:
“LLMs are powerful… but they sometimes make things up.”
The bigger problem is what happens next.

When hallucinations show up in a real workflow—legal, finance, healthcare, customer support—teams often jump straight to generic fixes:
“Add RAG.”
“Tighten the prompt.”
“Lower temperature.”
“Add a disclaimer.”

Sometimes those help. Often they don’t. Because hallucinations are not one problem.
They’re a family of failure modes—with different root causes, different levels of enterprise risk, and different mitigations.
That gap—between knowing THAT hallucinations happen and understanding WHY / WHEN / WHAT TO DO—is where trust erodes and enterprise deployments stall.
I’ve started calling the missing capability: Hallucination Sense

If Product Sense is the intuition leaders develop to judge whether a product decision is sound…
Hallucination Sense is the equivalent intuition for AI reliability.
It’s the ability to look at an AI output and quickly assess:
“Is this grounded in evidence or ‘plausible-sounding’?”
“Is this confident language justified—or risky?”
“Is this a retrieval problem, a validation problem, or an instruction-following problem?”
“Is the right response a direct answer… or an honest refusal?”

This matters because:
Evals are the instrument. Hallucination Sense is the clinical judgment.

To develop Hallucination Sense in a systematic way, I’m building a project called Hallucination Lab in Google AI Studio.
The goal is not just detection—it’s understanding and operational learning.
Hallucination Lab is designed as an interactive reliability simulator:
Generate tricky queries (ambiguous, time-sensitive, missing info, constraint-heavy)
Run them through a grounded search pipeline (retrieve → generate → extract claims → validate citations)
Score results against a clear taxonomy and thresholds (because “acceptable” depends on the use case)
Provide a short “flight recorder” trace explaining:
what failed
why it likely failed
what to do next

Why did I include MicroGPT in the Hallucination Lab? 
Because most leaders and practitioners know hallucinations exist, but the mechanics still feel mystical.
MicroGPT from Andrej Karpathy is a powerful teaching device: it strips away scale so you can understand the core behavior of language models in a cognitively accessible way.
The point isn’t that a tiny model matches a frontier model.
The point is that the foundational mechanism—probabilistic next-token generation—remains the same, even when the model becomes too large to inspect directly.
Once leaders and builders internalize that, a lot of “hallucination myths” disappear. 

I’ll share demos and early learnings from Hallucination Lab soon.

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