If you plug an AI agent into your data, how do you know it's giving you the right answer—and not just a confident one?

In this episode of Futureproof, Prakash Chandran sits down with Heather Lutz, Director of Engineering at Datasite, the provider of AI-powered solutions that enable private market investment, including virtual data rooms for mergers and acquisitions. Together, they unpack what happens when you point an agent at a massive data set without doing the foundational work first, why data readiness and governance are non-negotiable prerequisites for any AI initiative, and how Datasite is layering semantic views, verified queries, skills, and a "data doorman" to make agents actually useful.

About Datasite

Datasite provides the infrastructure that enables information flow for private market transactions, with purpose-built tools to optimize outcomes. Datasite’s innovative product portfolio, spanning sell-side virtual data rooms, buy-side intelligence, agentic AI applications, and an open data infrastructure layer, drives execution across the full investment lifecycle while generating unique data insights to empower investors, advisors, and deal professionals worldwide. Trusted by top private equity firms, investment banks, and consultancies, Datasite is built on 26 years of enterprise-grade security, compliance, and reliability. For more information, visit www.datasite.com

Topics covered include:

• Agents are confident interns, not seasoned analysts: Why an AI agent querying your data won't know about data quality issues, duplicate revenue tables, or missing filters—and why confidence without context is worse than no answer at all.
• Data readiness as CI/CD: Why testing data should follow the same discipline as testing software—with checks at every stage of the pipeline—and why continuous data quality monitoring barely exists as a standard practice yet.
• Data governance makes agents work: How domain ownership, shared metric definitions, and semantic layers turn an unnavigable ocean of tables into a surface an agent can actually be expert on.
• Don't work with the ocean: Why starting with your top ten metrics, your most important structures, and a bounded consumable layer is the only practical path to making AI-over-data work at scale.

Episode ID: 19329740-your-agent-doesn-t-know-what-it-doesn-t-know-with-heather-lutz-datasite

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If an AI agent gives you the correct answer but took the wrong path to get there, can you actually trust it?

In this episode of Futureproof, Prakash Chandran sits down with Karthik Narayan, Director of Product Management at Komodo Health, where he leads Marmot, an enterprise AI product for life sciences. Marmot's promise is that life sciences companies no longer need to send their data to McKinsey and wait months for an answer—they can ask complex healthcare questions and get answers directly. The catch? The answers aren't binary. Together, Karthik and Prakash unpack why grading an agent on whether it got the right answer is only half the story, how Komodo uses parallel critique agents and friction detection to close the gap between AI confidence and analyst rigor, and what changes when AI makes product leaders more powerful than they've ever been.

Topics covered include:

• Why the path matters more than the answer: How an agent can arrive at the correct number through the wrong query, pass traditional evals, and then fail catastrophically on the next question—and why trajectory evals are the real measure of trustworthiness.
• Steering, not just answering: How Marmot uses research plans, follow-up questions, and full code transparency to give analysts maximum control over subjective healthcare methodology decisions.
• Friction detection over thumbs up/down: Why users rarely use explicit feedback mechanisms, how Komodo infers dissatisfaction from behavioral patterns, and how that drove a complete platform rewrite at the six-month mark.
• Build vs. buy when AI makes prototypes easy: Why a junior engineer's weekend demo isn't the same as a production system with fallback models, context compaction, token optimization, and continuous evaluation—and how to think about total cost of ownership.

Episode ID: 19252007-the-right-answer-isn-t-enough-with-karthik-narayan-komodo-health

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If AI is making us faster, why does it feel like we're understanding less?

In this episode of Futureproof, Prakash Chandran sits down with Doug Merritt, CEO of Aviatrix. Doug is one of the most accomplished enterprise technology leaders of the last two decades—after serving as CEO of Splunk for years, he's now leading Aviatrix to tackle cloud-native security. Together, they unpack why the speed of AI adoption is outrunning foundational understanding, how a recent supply chain attack on the popular LiteLLM framework exposed a massive blind spot in cloud security, and why the leadership principles that matter most right now—curiosity, empathy, and purpose before action—are the same ones our attention-starved culture makes hardest to practice.

Topics covered include:

• As agents become more human, humans become more binary: Why the speed and abstraction of AI is making our thinking shallower at the exact moment we need it to be deeper—and how to fight back.
• The LiteLLM supply chain attack, explained: A breakdown of how attackers injected malware into LiteLLM, harvesting credentials from cloud environments—and why basic egress filtering would have stopped the damage cold.
• The three fundamental runtime controls: Why identity, endpoint, and network security are the only controls that actually stop attacks in progress—and why most cloud workloads are missing at least one.
• Cloud providers sold speed without brakes: How permissive outbound defaults became the norm, why cloud providers made firewalls an aftermarket add-on, and what that means for every organization deploying AI agents today.
• Five leadership principles for the AI age: Doug's hard-won framework—relentless curiosity, leading with empathy, purpose before action, radical accountability, and celebrating success—and why daily mastery beats chasing the next shiny thing.

Episode ID: 19139581-foundational-thinking-in-the-age-of-ai-with-doug-merritt-aviatrix

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