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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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OEE is one of the most powerful metrics in manufacturing, but only if you can act on it. FreshBI takes your raw OEE data and transforms it into real-time recommendations that show exactly where your biggest losses come from and the fastest way to fix them. If your team is working hard but still struggling with downtime, slow cycles, or inconsistent output, this dashboard will give you the clarity you’ve been missing. Schedule a call and let’s walk you through the platform.

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AI agents are moving from answering questions to taking action. They are approving workflows. Triggering payments. Updating records. Responding to customers. Making operational decisions. But there is one question enterprises need to answer before scaling agentic AI: What happens when a good AI agent acts on bad data? Most AI observability tools can help teams understand what went wrong after the fact. But if an agent has already processed the payment, approved the claim, updated inventory, or sent the customer response, the damage may already be done. That is the problem we are working on at FirstEigen. We are preparing to introduce AgentBuck, a new product built for the agentic AI era. AgentBuck is designed to help enterprises discover which AI agents are running, understand what data those agents depend on, continuously validate that data, and prevent risky records from reaching the agent before action happens. In simple terms: AgentBuck is being built to make bad data invisible to AI agents. If a record is incomplete, abnormal, non-compliant, duplicated, stale, or contextually unfit for purpose, the goal is to quarantine it before the agent acts on it. Because AI agents do not just need better models. They need trusted data. And in autonomous workflows, trust cannot wait for an alert. It has to be enforced at runtime. More coming soon. AI agents are only as safe as the data they act on. #AgenticAI #EnterpriseAI #DataQuality #AITrust #DataGovernance #DataObservability #FirstEigen #AgentBuck #DataBuck

Is your data ready for AI agents? Ask these 5 questions first. Before scaling AI across the business, leaders should pressure-test the data foundation behind it. 1. Can we trace the answer? When an AI agent gives a business-critical output, can we explain where that answer came from? 2. Can we catch changes early? If business logic, rules, or data patterns shift, will we know before users are affected? 3. Can we prove the data was trusted at the time of use? Not just clean in general, but reliable for that specific decision, report, or AI workflow. 4. Is someone accountable when quality drops? Do we know who owns the fix, how fast it should happen, and what action to take? 5. Can business teams see the confidence level? Trust in AI should not live only with engineering. Business users need clear visibility too. If too many answers are “not yet,” the problem is not AI ambition. It is data readiness. And that is useful news because it shows exactly where to strengthen the foundation before scaling AI further. 💭 How many of the five would your current data stack pass today? Reply with your score out of 5. #AIReadiness #AIAgents #DataTrust #EnterpriseAI #DataQuality

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Which of your training sources would fail an audit today? Most teams can't answer. The cost lands later: a model that quietly underperforms in exactly the segments the bad source covered. We verify every source before it ships. Provenance tagged, field by field. Get a source-quality audit.

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When I speak with leaders in food delivery, one theme always comes up: decisions are being made with incomplete or biased data. London is one of the most competitive food delivery markets in the world. Yet until now, no reliable benchmark was available to the industry. Reports like these are usually locked behind paywalls or kept for internal use only. At DoubleData, we specialise in the food delivery sector. Our team scrapes and matches millions of data points every month with 99.9% accuracy, helping executives make faster and smarter decisions. That is why we created the Food Delivery Benchmark Report: Uber Eats in London (2025) and made it freely available. Based on 691 restaurants across three key zones (E1, SE1, CR0), it reveals: ✅ Why quality, not quantity, drives customer loyalty ✅ Where London’s biggest “quality deserts” are hiding ✅ How promotions and logistics shape competitive advantage 👉 Download the report here: https://lnkd.in/ged8cnKd This is the first open benchmark of its kind in food delivery. And it will not stop with London. We are building benchmarks for major markets around the world because reliable, high-quality data should not be a privilege reserved for a few. Over the next weeks, I will share the most eye-opening insights from London right here. Stay tuned.