Aditi Toshniwal

We made writing code cheap and left everything else exactly where it was. Requirements, review, release, governance. Still downstream gates. Still human speed. So the backlog doesn't shrink, it grows faster. Hexaware's Four-Loop Model runs Intent, Implementation, Verification, and Value Realization concurrently. Trust stops being a stage you pass through and becomes a property of how the work executes. That is the conversation this Thursday, August 27th, in Santa Clara CA. Real ground covered: legacy knowledge extraction, AI-driven de-risking, and what any of this looks like inside a G2000 in regulated industries. Brett Sparks of Gartner moderating, with Cursor, AWS, Consilio, Workfabric AI, and Sanjay Salunkhe of Hexaware. If you own engineering throughput, come have a drink with us. Comment and I'll send you the link.

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How did LangTalks end up meeting with Andrew Ng, Anshul Ramachandran (Windsurf), and Roey Zalta And what book does #LarryPage wish he had when he started Google? It all relates to the question that keeps me up at night: how should our evolving understanding of 'search & retrieval', 'context engineering', and 'evals' shape the way we design systems? And how should we account for these shifting dynamics in the planning stage with a forward looking approach? (Though Page’s recommendation is real, so I hope you read until the end.) First, the new course Agentic AI with Andrew Ng is one of the best resources I’ve found (especially Module 4 on evals) [1]. I also recommend listening to LangTalk’s episode on search & retrieval with Guy Itach [2]. I especially liked the ending, where they show how technical aspects are combined with product sense and domain expertise, and why choosing the right evals isn’t straightforward at all. Don’t miss Roy Zalta’s post [3] on how he achieved 98% accuracy in his RAG system using a combination of eleven different strategies. And last but not least: Windsurf’s AI Coding Agents [4]. Specifically, the section on “Search & Discovery for AI Agents,” where Anshul presents a multi-step retrieval paradigm—that blew my mind (it’s free). Another thought I’ve had on 'evals and error analysis' is that it's somewhat derivative of the OKRs methodology—an incredibly powerful concept for driving clarity, prioritization, organizational alignment, and decision-making. I honestly can’t recommend Measure What Matters [5] enough—but don’t take my word for it. See what Larry Page, Alphabet CEO and Google cofounder, had to say: "I wish I had had this book nineteen years ago, when we founded Google. Or even before that, when I was only managing myself! As much as I hate process, good ideas with great execution are how you make magic. And that’s where OKRs come in." Circling back to my question: As we shift toward agentic systems, how do you see the evolution of system design? What planning strategies are you starting to prioritize from the very beginning?

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Most AI initiatives don't fail on accuracy. They fail because nobody agreed on what decision the output was supposed to change. That idea came up again and again over the last few weeks, and it's the one I'm carrying forward. I've just completed the Leadership with AI programme at the Indian School of Business (ISB). I went in expecting a course about tools. I came out with a much sharper view of the harder part — how AI changes the way decisions get made, who owns them, and what leaders are accountable for once a model is in the loop. Three things that stuck: 1. The bottleneck is rarely the model. It's the clarity of the business question feeding it. 2. Adoption is a change-management problem long before it's a technical one. 3. Governance isn't a tax on speed — it's what makes speed defensible. Why this mattered to me: I've spent my career in Data and Analytics — turning messy data into decisions senior stakeholders can act on. The skill I want to keep building is the one this programme kept pushing on: deciding which questions are worth answering at all, and making sure someone actually acts on the answer. That's also where I'd like to take my next step. I'm open to conversations around Decision Science, Product Analytics and AI-adjacent analytics roles — the kind of work that sits closer to strategy. If you're hiring, or you know a team building in this space, I'd genuinely welcome the connection. And if you're working at this intersection already, I'd love to trade notes regardless. A special thank you to Milind Naik, who mentored our cohort through weekly live sessions. Those conversations were where the frameworks stopped being theory and started being useful. #ISB #ArtificialIntelligence #DataScience #ProductAnalytics #Leadership #LeadershipwithAI

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