Felipe Hoffa

Our VP of Data Analytics, Phil, wrote a very accessible blog post about another in-house agentic solution built at Fullscript. Flurry allows anyone on the team to use natural language to ask a data question, and get back accurate, usable, and understandable information. This gives our data team more time to work on the interesting stuff! https://lnkd.in/ekmdfbJg

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I'm really excited about this update. We're trying out some new approaches including using LLMs like Claude combined with a text embedding model to generate geospatial embeddings. Try it out here! https://lnkd.in/eQn74R3D

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Great piece by Emilia David on the LangChain open source integration ecosystem, the power of LangGraph, and our newly released LangGraph Platform Helps put everything you see us working in perspective Link: https://lnkd.in/ghajS7zx

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I came across this today: AI in Procure-to-Pay: Structural constraints holding back P2P platforms (link in comments). “Structural constraints?” At Silkline, we’ve been thinking about these problems since Day 0 - over two years ago. Not as academic critiques, but as real blockers to building procurement software people actually trust and use. Every step of our journey, the team has made explicit product and engineering decisions to answer one question: "How should this be built today, given what’s possible in 2026 - not how it’s been done for the last 20 years?" Given our growth last year (and an even stronger trajectory this year), it’s time to start showing the how. Next week, I’m challenging myself to do 5 posts in 5 days, breaking down how we’ve addressed each of these so-called “structural constraints” in practice: 1. Document-centric data models: Fragmented documents → fragmented context → weak AI reasoning. When data is trapped in PDFs and forms, AI can’t see patterns, history, or risk. 2. Weak state management: It’s not enough to know where you are. You need to know how you got here. Most systems lose the story. 3. Brittle integration architectures: The real problem isn’t “big data.” It’s lots of small, messy, constantly changing data. 4. Policy encoded as static rules: Real procurement is full of exceptions. Static rules break the moment reality shows up. 5. Explainability and trust: If you don’t know why, when, or how something happened, you won’t let AI touch real decisions. These aren’t “constraints” to work around. They’re design choices, and we’ve been intentional about them since day 1. More next week.

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As COO at www.jaiinfoway.com this is an important signal for where agentic AI is heading. Non technical implementations are catching up fast but the real story is iteration speed and learning loops. Building Cowork entirely with Claude Code shows how agents are becoming first class workers not just assistants. At www.jaiinfoway.com we see this shift clearly as enterprises move from copilots to autonomous task execution with guardrails. UX will evolve tooling will mature but the direction is set. Curious how others here see non technical users adopting agent driven workflows this year. Are we ready for agents as coworkers. #Jaiinfoway #AgenticAI #Claude #AIAgents #EnterpriseAI #FutureOfWork #AIProductivity #AITransformation #AILeadership

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