Vincent Wehren

If you already have a stable ELT setup for ingesting data from Klaviyo into BigQuery and are considering migrating to the preview Klaviyo connector "Data Transfer", don't do it. You can set it up in seconds with mouse clicks but you lose error logging, the ability to do anything about the errors, control over back-fills, and other important things.

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Big news: WordPress.com just enabled AI agents to autonomously write, publish, and manage content at scale — via MCP, the same protocol that's quietly becoming the USB-C of AI integrations. This isn't a gadget. It's a preview of every enterprise workflow in 2026 and onward. The "human-in-the-loop" narrative? A transition phase — not the destination. Once agents prove reliable, humans stop checking. Not from trust. From laziness, From pressure on productivity, From just pure human optimism. That's fact. The endgame: agents delegating to agents. One coordinates, another executes, a third validates. Humans become the occasional escalation layer. https://lnkd.in/geAvJaf7 #AI #AIAgents #UCaaS #CPaaS #WorkloadAutomation #CustomerExperience

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Hypergraphs are being discussed a fair amount lately in semantic circles, but I usually thought of them primarily in the context of queries, which limited their use cases quite a bit. However, the more that I’ve explored SHACL Rulesets, the more that I’ve come to realise their power as a generative tool for creating triples and doing fairly complex set operations on those to handle everything from product catalogs to access controls to genetic research. This post is long, but gets into how one can implement such hypergraphs in RDF and how they can be used to reduce the process of building rich knowledge bases by one or two orders of magnitude. It also looks at one particular case where LLMs can dovetail nicely into semantics for ontologists. Thoughts on the nature of hypergraphs in this issue of … The Ontologist!

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Why trade-offs in #AgenticAI is a critical Factor? #DesigningAIagents is not about finding the perfect configuration but making deliberate #tradeoffs based on your product’s goals and constraints. This framework outlines the most critical trade-offs in Agentic AI to help you build successfully. 🔸Autonomy vs Control Giving agents more autonomy increases flexibility, but reduces human oversight and predictability. 🔸Speed vs Accuracy Faster responses often come at the cost of precision and deeper reasoning. 🔸Modularity vs Cohesion Modular agents are easier to scale. Cohesive ones reduce communication overhead. 🔸Reactivity vs Proactivity Reactive agents wait for input. Proactive ones take initiative, sometimes without clear triggers. 🔸Security vs Openness Opening up tool access increases capability, but also the risk of data leaks or misuse. 🔸Memory Depth vs Freshness Deep memory helps with long-term context. Fresh memory improves agility and faster decision-making. 🔸Multi-Agent vs Solo Agent Multi-agent systems bring specialization but add complexity. Solo agents are easier to manage. 🔸Cost vs Performance More capable agents require more tokens, tools, and compute, raising operational costs. 🔸Tool Access vs Safety Letting agents access APIs boosts functionality but can lead to unintended outcomes. 🔸Human-in-the-Loop vs Full Automation Humans add oversight but slow things down. Full automation scales well but may go off-track. 🔸Model-Centric vs Function-Centric Model-based reasoning is flexible but slower. Function calls are faster and more predictable. 🔸Evaluation Simplicity vs Real-World Alignment Testing in a sandbox is easier. Real-world tasks are messier, but more meaningful. 🔸Static Prompting vs Dynamic Planning Static prompts are stable. Dynamic planning adapts better, but adds complexity. 🔸Generality vs Specialization General agents handle a wide range of tasks. Specialized agents perform better at specific goals. 🔸Local vs Cloud Execution Cloud offers scalability. Local execution gives more privacy and lower latency.

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Hierarchical Abstraction is emerging as a key technique for building more robust and efficient LLMs. Instead of processing information as a continuous stream, this approach models data at multiple levels – think individual details *and* summarized concepts. This allows AI systems to retain crucial context over longer interactions, improving reasoning and reducing errors in complex tasks. For enterprises, this translates to more reliable AI copilots, better decision-making in dynamic environments, and scalable automation of knowledge-intensive operations. 🚀 Ultimately, it’s about building AI that ‘understands’ rather than just ‘remembers’. 💡📈

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RAG is evolving from “AI answers with citations” into something more operational: Agentic RAG, where the system can orchestrate multi-step retrieval across sources and apply measurable checks like context relevance, answer relevance, and groundedness. This Q&A with Eudald Camprubí explains why that matters. Enterprise adoption hinges on trust, traceability, and governance as much as model quality. The payoff isn’t just Q&A. It’s powering practical workflows like content drafting that stays on-message, faster campaign and competitive research synthesis, better internal enablement/onboarding, and more reliable customer support experiences. All grounded in your organisations actual materials, anything from web pages, to call transcripts to earnings call recordings.. Worth a read if you’re thinking about AI as a durable capability—not a one-off chatbot project. #AgenticRAG #RAG #MarTech #MarketingOps #GenAI #EnterpriseAI #ContentStrategy #AIQuality

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To FTP or to sFTP? Been using these technologies for 30+ years, and somehow I find myself still wondering when to use one or the other. The folks at #kiteworks have a nice article: https://gag.gl/oi1wa2 SFTP (Secure File Transfer Protocol) offers a highly secure method for transferring files by using encryption, authentication, and data integrity checks, making it a significant improvement over the older, unencrypted FTP protocol. However, it comes with drawbacks such as increased complexity, slower performance, and limited native support for compliance features like audit logging or encryption at rest. Tools like Kiteworks enhance SFTP by adding security, governance, and compliance capabilities, making it more suitable for regulated environments.

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