Ken Elliott

As we rethink enterprise workflows for the AI-agent era, we shouldn’t focus only on the workflow or model or the agent itself—we need to design the agent harness around it. The harness is the infrastructure that manages tools, prompts, memory, and execution loops, turning an LLM into a reliable “work engine” for real tasks. If you’re exploring how AI agents will reshape workflows, this post from LangChain can be a helpful primer on what an agent harness is and why it matters

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Last week, the inaugural launch of The AI Collective's North Dallas Chapter brought together AI professionals, data leaders, and technology executives for a comprehensive exploration of enterprise AI transformation, with a particular focus on the evolution from traditional AI to agentic AI systems. Dr. Geethamani Jakka (Capital One) - Cyber Data Engineer Linga Reddy Alva (Warner Bros Discovery) - Data AI Solution Architect MAHESH KUMAR GADDAM (Fedex) - Solutions Architect Naveen Nalajala (Dell Technologies) - AI Implementation Engineer Shiva Krishna K. (Broad Financial) - Senior Engineering Manager Key Speakers & Presentations Lestan D'Souza - Co-Founder & CTO at ReKnew Mantu Samadder - Head of Data and AI / Agentic Gen AI at Genpact Manish Jain - Vice President and Global Head of AI Architecture, Firstsource Surendar K Chintala - Specialist Leader at Deloitte Consulting LLP (Summary of Presentations in the comments below.) The event emphasized three key themes: -Beyond the Hype: Moving from buzzwords to real-world agentic AI capabilities and applications -Industry Transformation: AI fundamentally transforming industries through efficiency, innovation, and new business models -Survival Imperative: Adopting agentic AI as essential for business competitiveness and long-term survival Key Takeaways +Terminology matters: Clear definitions prevent costly implementation failures +Context is king: Enterprise AI success depends on sophisticated context engineering rather than simple prompt-based approaches +Governance first: Comprehensive oversight frameworks must be implemented from day one +Business alignment: Technology decisions must start with business outcomes, not technical capabilities +Cultural transformation: Success requires addressing organizational resistance through training and emphasizing human-AI collaboration The event successfully established the North Dallas Chapter as a hub for practical, enterprise-focused AI transformation discussions, combining theoretical frameworks with real-world implementation experiences and cautionary tales from the field. Exciting to see that AICamp, The AI Collective, and other organizations are bringing the AI community together in DFW

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Dan Sinawat started from zero. Shaw Talebi left Toyota to teach the world AI. Both learned the hard way: being known for everything means being remembered for nothing. Full episode drops now. Keysha.ai #AI #Entrepreneurship #DallasAI

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Of the top 10 fastest-growing jobs in Dallas, look at #2: AI consultants and strategists. A role that barely existed a year ago is now one of the hottest in the market. The best way to build the skills for this kind of work? AI-Native Foundations and AI-Native Change Agent from AI-Native Training by Scaled Agile, Inc. It’s a strong signal that the work Asim Javed, Rashid Smith and I have been laser-focused on over the past few years is showing up in the market — and fast. https://lnkd.in/dY2CNC6X

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Part 2 of our series on how to secure Agentic workflows is out - Dive into how Detection & Response looks like in the era of autonomous agents…

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Thanks for the post however What the 65 agents really reveal is something even more uncomfortable. It’s not that enterprise AI isn’t transformational yet. It’s that most enterprises are structurally incapable of transformation. Let’s call it what it is. Intake. Classification. Synthesis. Routing. That’s not transformation. That’s workflow acceleration. And yes — that’s fine. But let’s stop pretending shaving seconds off steps is the same as redesigning how value is created. Low-code platforms produce task-level solutions because that’s what they are optimized for. You cannot drag-and-drop: • decision rights • cross-functional authority • shared semantic meaning • governance frameworks • enterprise memory • AI accountability You cannot automate your way into strategic coherence. And here’s the deeper issue most reports won’t say: Task automation is comfortable because it does not threaten power structures. True AI transformation does. Real transformation asks: • Who owns decisions now? • What happens when agents escalate? • Who is accountable for model-driven outcomes? • How do we govern bias, risk, and regulatory exposure? • How do we architect AI across the value chain, not just the intake stage? That is not a tooling problem. That is a paradigm problem. And that’s why most enterprise AI efforts stall at “agent demos.” They optimize inside the existing operating model. They don’t redesign the operating model. At Coach Leonardo University, our obsession is not with building agents. It’s with building AI Architects. The difference? Agents automate steps. Architects redesign systems. Agents reduce friction. Architects reduce structural waste. Agents optimize tasks. Architects redefine decision frameworks. Transformation begins where low-code platforms stop. It begins with: • Governance by design • Human–AI role clarity • Decision architecture • Enterprise semantic alignment • Capability upgrade at leadership level That’s the AI paradigm shift. From: “Where can we insert an agent?” To: “How must the enterprise evolve to operate with intelligence embedded in every layer?” Most organizations will stop at faster steps. The few who build foundations will eliminate unnecessary steps entirely. That’s the real divide. Check https://lnkd.in/e8Fwruin to know more about our AI Manifesto #AITransformation #EnterpriseAI #AIGovernance #AIArchitecture #AgenticAI #DigitalTransformation #AIParadigmShift #CapabilityUpgrade #ExecutiveLeadership #CoachLeonardoUniversity

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Turn generic AI meeting summaries into industry-specific intelligence by customizing your prompts. Start with a foundation of roles (meeting outcome analyst) and output sections (decisions, priorities, issues, actions, topics). Then, add an industry-specific section. For AI, it's "Technical Context" (processes, tools, platforms). For CPAs, try "Accounting Processes." For law firms, "Legal Processes." Store summaries by client and ask follow-up questions anytime. See more than 50 other powerful AI prompts and workflows here: https://lnkd.in/eUcX3MAY #AIMeetings #ProductivityHacks #BusinessIntelligence #WorkflowAutomation

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