Cory Hymel

VP of Product & Research @ Gigster | Innovating on the Future of Work

//--In my own words
Washed software engineer that was better at telling stories and painting visions that writing code. I usually find myself working in research or innovation teams due to attention deficient and boredom with solvable problems. I very much enjoy speaking, teaching, and sharing things I've learned with others. 


//--In my PR teams words
The root of Cory’s passion for the connected nature of tech enhancing the future of work began in the early 2000s in robotics research initiatives for the FedEx Institute of Technology. He went on to pioneer mobile helping enterprises develop strategies to mobilize staff workflows and collaboration in the pre-enterprise project management app era of “BYOD to work” in the mid-2010s. Seeing the promise of physical and digital integration, Cory brought physical products to life leading the Connected Technologies division at a hardware development firm. Beginning in 2018 Cory began researching and leading the Future of Work at Gigster as VP of Product and Research advocating for leveling outdated hierarchical structures within organizations. Cory is a futurist with the goal to continuously question the status quo. His research answers the questions that will define how the innovations of today will impact the work of tomorrow.

Head of Academic Partnerships

Helping lead research with Stanford and Berkeley into how distributed teams work at scale.

Passionate about turning ideas into realities, seeking solutions to problems or challenges that arise in a volatile, uncertain, ambiguous, and complex environment, learning from diverse perspectives and experiences.

I leverage my expertise in the GTM & Revenue motion of the business.Helping customer around the globe with AI performance using observability and insights to make LLMs and GenAI workflows reliable, measurable, and aligned with business outcomes. Empowering both engineering and product teams to collaborate seamlessly around data, performance, and product decisions.

Agustin Vivas De Lorenzi

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗮𝗿𝗲𝗻’𝘁 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀.
They’re struggling with making them reliable in production.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗿𝗲𝗻𝗱; 𝗶𝘁’𝘀 𝗮 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻.
We’re moving from “prompt engineering” to “system orchestration”.
From single-model outputs → to multi-agent collaboration → to production-ready reasoning.

The real challenge?
Not intelligence.
Accountability.

* How do you make an AI agent traceable?

* How do you observe decision chains?

* How do you ensure reliability when models act autonomously?


Most “GenAI demos” fail at the exact moment users depend on them.
That’s where agentic observability comes in understanding, debugging, and improving AI behaviour post-deployment.

𝗔𝘁 𝗔𝗿𝗶𝘇𝗲, 𝘄𝗲’𝘃𝗲 𝘀𝗲𝗲𝗻 𝘁𝗵𝗶𝘀 𝗳𝗶𝗿𝘀𝘁-𝗵𝗮𝗻𝗱:

The AI that works in your notebook might fail silently in production.
The difference between a research toy and a trusted product is the system layer 
observability, monitoring, feedback loops, and agent reliability metrics.

𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮𝗴𝗲𝗻𝘁𝘀 𝗼𝗿 𝗟𝗟𝗠 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵:

Your infra, not your model, will decide your product’s future.
The winning teams in 2026 won’t just build smart AI.
They’ll build accountable AI systems.

Let’s make AI measurable again.
We’re testing a new framework designed for Agent Reliability Tracking 
a structured way to evaluate how your AI behaves, adapts, and learns in production.


If you’re an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱, 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁, 𝗼𝗿 𝗛𝗲𝗮𝗱 𝗼𝗳 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻,
and you’re scaling agentic systems this quarter. I’d love to share the early playbook.

𝗖𝗼𝗺𝗺𝗲𝗻𝘁 “𝗔𝗴𝗲𝗻𝘁𝗢𝗽𝘀” 

I’ll personally send you the framework we’re testing and insights on how teams are making their GenAI systems production-safe.


#AgenticAI #AIOps #LLMEngineering #AIInfrastructure #AIObservability
#AIInnovation #AgentArchitecture #MachineLearning #AIEcosystem #AIProductEngineering
#Founders #TechLeadership #DataScience #AIDevelopment #GenerativeAI

MBA | Country Manager & Site Lead | LinkedIn Top Customers Voice | LLM Observability

Making AI reliable, one evaluation at a time.

From SF streets to global AI teams, we're here for you!

Arize AI

Excited to share that we will be in Tokyo next week 🇯🇵

getting together to talk about what it actually takes to build, deploy, and operate AI-driven agent systems in real enterprise environments beyond demos and proofs-of-concept.

The focus will be on the challenges teams run into once things get real:

reliability, evaluation, governance, and scaling agent-based systems in production.

AI Agents, Beyond Just Building Them Practical lessons on moving from prototype to production
How observability, evaluation, and experimentation help scale agent systems.

If you’re around and working on agents (or planning to in 2026), would love to have you join us.

Open article: イベントタイトル:エンタープライズにおけるAIエージェントの構築と運用 · Luma by luma.com

イベントタイトル:エンタープライズにおけるAIエージェントの構築と運用 · Luma

taplio

The Battle

Cory Hymel

Agustin Vivas De Lorenzi

Followers

Posts

Engagements

Likes

Performance ScoreAgustin Vivas De Lorenzi's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Cory Hymel

Agustin Vivas De Lorenzi

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Agustin Vivas De Lorenzi's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAgustin Vivas De Lorenzi's LinkedIn Performance