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I made a controversial podcast appearance a few weeks ago and waited until now to post about it. I've even shared clips from it but never actually linked to the full episode until just now 🤦♂️ So here is my official plug to catch me on From A to B with Shiva Manjunath where I was initially trolled for my belief in "spaghetti testing", and within 35 minutes had turned the tables and steered us into a career growth b̶u̶t̶t̶k̶i̶c̶k̶i̶n̶g̶ coaching session. You can listen here: https://lnkd.in/gPYwUE6u Or if you're looking for something shorter, maybe this quick 5-minute appearance on The Lantern from MGMT Boston - if you've ever wondered why I continue to choose Boston as my homebase for my experimentation evangelism mission (and not, say, SF), I'll break it all down for you real quick: https://lnkd.in/g45wetrV And if you want the *full* briefing on Building Self-Serve Experimentation and Decreasing Cost-Per-Experiment, you've gotta invite me to give a 30min talk to your company. I'll do two of these free of charge for the first folks to get it on the calendar. DM me 😎

Just a small bit of news this morning: Eppo has been acquired by Datadog! You can read our official announcement in the blog post below, but I wanted to share a personal take on the news and why I'm thrilled: I've been running experiments for over a decade. Somewhere along the way, I decided that just running experiments wasn't enough for me - I wanted to focus on accelerating the overall adoption of experimentation in business. I first got the chance to do that as a consultant at Cro Metrics, working with 5-10 companies at a time and helping them build experimentation programs. I joined Eppo to scale my impact 10-100x. When I placed this bet, Eppo was the only warehouse-native experimentation tool on the market, and I saw a founding team equally dedicated to statistical rigor and widely-accessible UI. I knew this could enable end users at a scale far beyond what other products on the market were capable of, and I was right: today many Eppo customers successfully run thousands of experiments/year across dozens of teams in their organizations. And now it's time for another 100x leap, because now we can take the mission to 30,000+ existing Datadog customers. This is obviously not just a huge win for us, this is a big moment for the experimentation space too. It's the biggest bet yet from a wildly successful, publicly traded company who sees the exact same vision we do: a *lot* of growth and opportunity ahead for experimentation leaders, programs, and companies. There will be a lot more to come over the next few weeks but for now, suffice to say: our focus is still on building industry-leading experimentation capabilities, and it will the same Eppo team building the product for years to come. The acquisition also unlocks huge improvements in scaling feature flags and canary tests (necessary infra underlying all experimentation use cases) and some pretty exciting forward-looking capabilities for Agentic AI. New digs, new teammates, and a way bigger war chest - but the mission remains the same. Let's get everybody experimenting. https://lnkd.in/eX_Cs8jV

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🎥 𝗣𝗔𝗥𝗧 𝟯 𝗢𝗙 𝟯: 𝗔𝗖𝗖𝗘𝗦𝗦 𝗖𝗢𝗡𝗧𝗥𝗢𝗟 𝗙𝗢𝗥 𝗔𝗚𝗘𝗡𝗧 𝗦𝗘𝗥𝗩𝗘𝗥𝗦 | Deep Dive with 𝗝𝘂𝘀𝘁𝗶𝗻 𝗟𝗮𝗻𝗴𝘀𝗲𝘁𝗵 In the final installment of our Agent Server series, Justin Langseth, Co-Founder & CTO of Genesis Computing, tackles the most nuanced challenge in production agentic AI: 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝘄𝗵𝗼 𝗰𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝗱𝗼? 🔐 𝗧𝘄𝗼 𝘄𝗮𝘆 𝗮𝗰𝗰𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 Agent access flows both directions: 𝗗𝗼𝘄𝗻𝘀𝘁𝗿𝗲𝗮𝗺 What can agents access in enterprise systems? 𝗨𝗽𝘀𝘁𝗿𝗲𝗮𝗺 Who can interact with agents and what can they ask them to do? 💡 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗝𝘂𝘀𝘁𝗶𝗻 𝗠𝘂𝗹𝘁𝗶 𝗰𝗵𝗮𝗻𝗻𝗲𝗹 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 Users engage agents via Slack, Teams, Jira, Google Docs, and APIs, each with unique security considerations. 🚨 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗗𝗼𝗻’𝘁 𝗶𝗻𝗵𝗲𝗿𝗶𝘁 𝘂𝘀𝗲𝗿 𝗮𝗰𝗰𝗲𝘀𝘀 𝗿𝗶𝗴𝗵𝘁𝘀. OAuth-style impersonation lets agents “step into the shoes” of a user. Talking to an agent should not grant it 𝗬𝗢𝗨𝗥 enterprise privileges. Why this fails: visibility by many, human vs agent trust differs, context-dependent security. 𝗕𝗲𝘁𝘁𝗲𝗿 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: 𝘁𝗿𝗲𝗮𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 🔸 Give agents their own identity and RBAC 🔸 Define which users or groups can interact with each agent 🔸 Specify allowed request types per user 🔸 Control what information agents can share back 🔸 Decouple agent permissions from caller permissions 🔸 Monitor agents as you monitor employees 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝗮𝗴𝗲𝗻𝘁 (𝗔𝟮𝗔) Secure inter-agent communication follows the same access principles as agents take on more functions. 🎯 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲 Access control is not just security; it is a sustainable, scalable framework where agents operate as trusted members of your organization with clearly defined roles, like your human employees. 👀 𝗪𝗮𝘁𝗰𝗵 𝗣𝗮𝗿𝘁 𝟯 👉 https://lnkd.in/ed9Js2AH Missed the series? 👇 𝗣𝗮𝗿𝘁 𝟭: What is an Agent Server https://lnkd.in/ejqXbVVT 𝗣𝗮𝗿𝘁 𝟮: Where to deploy Agent Servers for maximum security https://lnkd.in/eFPiwtFn 𝗚𝗲𝗻𝗲𝘀𝗶𝘀 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 solves production agentic AI for all your data engineering needs at scale with security and governance from day one. #AgenticAI #AccessControl #ZeroTrust #EnterpriseAI #DataSecurity #AIGovernance #RBAC #CyberSecurity #AIAgents #ProductionAI

🎥 𝗣𝗔𝗥𝗧 𝟮 𝗢𝗙 𝟯: 𝗪𝗛𝗘𝗥𝗘 𝗗𝗢 𝗔𝗚𝗘𝗡𝗧 𝗦𝗘𝗥𝗩𝗘𝗥𝗦 𝗥𝗨𝗡? | Deep Dive with 𝗝𝘂𝘀𝘁𝗶𝗻 𝗟𝗮𝗻𝗴𝘀𝗲𝘁𝗵 In the second installment of our Agent Server series, Justin Langseth, Co-Founder & CTO of Genesis Computing, tackles one of the most critical questions enterprises face when deploying agentic AI: 𝗪𝗵𝗲𝗿𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁 𝘀𝗲𝗿𝘃𝗲𝗿 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻? 🔐 𝗧𝗵𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: We’ve gone from floppy discs → SaaS applications → and now to AI agents that can replace entire categories of SaaS tools. But here’s the catch: while agents can reduce SaaS dependency and costs, they need somewhere secure to run, especially when they’re working with 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮. 💡 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗝𝘂𝘀𝘁𝗶𝗻: 𝗪𝗵𝘆 𝗗𝗮𝘁𝗮 𝗔𝗴𝗲𝗻𝘁𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗥𝘂𝗻 𝗜𝗡𝗦𝗜𝗗𝗘 𝗬𝗼𝘂𝗿 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 Unlike traditional SaaS where software runs in a vendor’s cloud, data agents need broad access to your enterprise data, both structured and unstructured. Running these agents externally creates 𝘂𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗿𝗶𝘀𝗸𝘀. Genesis customers deploy agent servers 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, ideally on the same platform where data lives. 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗢𝗽𝘁𝗶𝗼𝗻𝘀 Genesis Agent Server runs as a 𝗗𝗼𝗰𝗸𝗲𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿, giving you complete control over where it lives: 🔸 Snowflake - Native application with Snowpark Container Services (easiest path for Snowflake customers) 🔸 Databricks - Container deployment inside the platform 🔸 Amazon Web Services (AWS) - EC2 or ECS via Terraform scripts 🔸 Microsoft Azure - Container instances or VMs via Terraform 🔸 𝗢𝗻-𝗣𝗿𝗲𝗺𝗶𝘀𝗲 Kubernetes - Yes, even hedge funds running everything in their own buildings 🛡️ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗧𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗦𝗲𝗻𝘀𝗲 Justin introduces a powerful mental model: 𝘁𝗿𝗲𝗮𝘁 𝗱𝗮𝘁𝗮 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗻𝗲𝘄 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀. 🔹 Create service accounts with appropriate read/write permissions 🔹 Grant access thoughtfully, not full admin rights on day one 🔹 Monitor agent activity the same way you’d monitor human activity 🔹 Leverage existing security protocols you already trust 🎯 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 When agents have broad access to corporate data, running them 𝗶𝗻𝘀𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝘁𝗿𝘂𝘀𝘁𝗲𝗱, 𝘀𝗲𝗰𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗲𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 isn’t just better, 𝗶𝘁’𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹. 👀 𝗪𝗮𝘁𝗰𝗵 𝗣𝗮𝗿𝘁 𝟮 👉 https://lnkd.in/e27G3iby Missed Part 1? We covered what an agent server is and why it’s the foundation of production AI agents. Coming up in Part 3: Justin will dive deeper into the final critical elements of agent server architecture. #AgenticAI #DataSecurity #EnterpriseAI #CloudArchitecture #DataEngineering #Snowflake #AIAgents #CyberSecurity #TechLeadership

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One of the biggest concerns enterprises have around AI isn’t capability. It’s security. A question that came up during the discussion: “Does it integrate with Google Security?” Yes — fully. But the more important question was this: “If AI is being used, is any customer data leaving the environment?” The answer: no. The product was architected so that: • No customer data leaves the firewall • No metadata leaves the firewall • All processing stays within the customer environment • AI capabilities are packaged directly into the software That also means: • No external model dependency • No uncontrolled data exchange • Zero token consumption You get the advantages of AI without exposing enterprise data externally and without unpredictable token costs. A lot of organizations are realizing that “AI-ready” also means: secure, governed, and operationally sustainable. ❓ As AI adoption grows, what concerns your organization more right now: security, governance, or cost control? #EnterpriseAI #DataSecurity #AIGovernance #DataGovernance #AIInfrastructure

𝐀𝐈 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐬𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥. 𝐈𝐭 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚 𝐭𝐫𝐮𝐬𝐭. Enterprises are moving fast with LLMs, copilots, and AI agents. But as AI moves from answering questions to triggering actions, the risk changes. Bad data is no longer just a reporting issue. It can lead to: • wrong recommendations • broken workflows • poor customer decisions • compliance risk • automation failures That’s why trusted data has become the real foundation of AI success. Before AI can analyze, predict, or act, the underlying data needs to be: • complete • consistent • validated • reconciled • traceable • fit for business context This is the trust layer most AI strategies are missing. In our latest blog, we break down why data trust matters, where most organizations fall short, and how platforms like DataBuck help enterprises validate data before it impacts analytics, automation, or AI decisions. Read the full blog here: https://lnkd.in/g9EJT5wM What is the bigger AI challenge in your organization right now — model performance or trusted data? #DataTrust #AIReadiness #EnterpriseAI #DataQuality #AIGovernance #DataBuck

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The team behind one of the best open web corpora reported something remarkable: the 10% of data they kept was 'actually worse than the 90% we removed.' That's the free corpus. It took 60,000+ GPU-hours just to validate one filtering pipeline, and the conclusion was that filtering barely helps. The implication for anyone building on web data: · Free and usable are different products · Cleaning at corpus scale is a research programme, not a preprocessing step · The differentiating data was never in the free crawl anyway We acquire, structure, and verify the data your model actually needs, from sources the crawlers don't reach. Talk to us about your corpus.

80% to prepare the documents. 20% to build the system. That's how AI teams describe their own projects, and it matches what we see: · A corpus with 27 different PDF types, each breaking the parser a new way · Tables that lose their structure, entities split across line breaks · A quarter of 'AI work' that is actually document plumbing We take the 80%. Extraction, structuring, verification: your corpus arrives AI-ready, in your schema. Your team keeps the 20% they were hired for. Talk to us about your corpus.