Greg Wester

Vanta/Drata for MAPD? As I spend a lot of time with Drata for our SOC2 compliance at falcon health, I see a clear parallel between: A) General privacy/security frameworks for enterprises, and B) Risk adjustment, data reporting/submission, and payment compliance for Medicare Advantage plans There are hundreds of things to track for SOC2 compliance - various checks and balances - but you also need processes and mechanisms to monitor changing environments: network traffic, compute usage, new users, cyber threats. The platform organizes hundreds of data streams, automatically checks for errors or risks, and provides alerts to users. So imagine this for Medicare Advantage: Utilization trends, HPMS memos, changing CMS regulations and policies, risk score compliance, and all downstream vendors and their data. What if we had a platform that organizes these data streams, analyzes them, and provides appropriate action plans? The features would mirror what we see in modern compliance platforms - but tailored for MAPD operations. Multi-program compliance across MA, Part D, and Star Ratings. Automated regulatory tracking instead of evidence collection. CMS experts instead of security experts. Provider/vendor management instead of vendor security questionnaires. Compliance protocols instead of security policies. Continuous CMS compliance monitoring instead of 24/7 security monitoring. Same proven framework, different regulatory universe. Do we have such a platform in the MAPD space? --- The image is adopted from the Drata site! Changed the features to meet the MAPD compliance

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LLMs alone aren’t enough to scale agents in the enterprise. What makes agents reliable isn’t just model quality—it’s the system around them. We’ve learned that high-quality search is foundational not only for grounding agents in the enterprise context but also for mapping process knowledge, personalizing agents, and scaling agents usage of enterprise tools and workflows. Here’s what that system looks like at Glean: • Grounding agents in relevant, authoritative knowledge: Autonomous agents raise the bar — they can’t rely on outdated or incorrect knowledge. They need precise, task-aligned, enterprise-specific context. Knowledge bases alone aren’t enough — this challenge requires high-quality search. • Personalizing agents for each enterprise: Every organization works differently. We analyze usage to learn how work actually happens, then surface relevant, customer-specific examples to guide agent design. • Creating agents that understand how enterprises work: We’ve observed how Glean users progress from simple Assistant queries to more complex, agentic workflows. We’ve grouped these into distinct query classes — such as enumeration (“list all Jira tickets assigned to me”) or people-seeking (“who’s my manager’s manager?”). These classes help us guide agents in how to interpret intent and execute real work. • Scaling evaluation with LLM judges — For every query class, we build eval sets from real usage and design task-specific LLM judges. But building a judge isn’t enough — it must produce consistent results across similar inputs. Consistent, scalable evaluation is a precursor to agents tackling real work in the enterprise. We’re excited to keep pushing the boundaries of what agents can do at work. A huge credit to the team behind our agentic reasoning engine — every part of it is purpose-built for enterprise AI.

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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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We at Foundation Capital been closely tracking the shift from software-as-a-service to services-as-software for over a year. In my latest with Jaya Gupta, we distill a year of learnings into three high-level trends: ➡️ Features aren’t your moat anymore - differentiation comes from how deeply and effecitvely you integrate your product into the customer’s world. ➡️ The traditional pre-sales vs. post-sales divide is gone - customers expect a working solution during the sales process. Selling and implementation have fused into a single, continuous effort. ➡️ Pay for outcomes, not seats - pricing is moving from per-seat or per-API charges toward alignment with business outcomes (you pay when the AI actually delivers value). There's lots of complexity in pulling this off, but it's where the strongest AI startups are heading. Why do these shifts matter? Because beyond the $200B SaaS market lies a $4.6T pool of spending on labor and services that this wave of AI companies is poised to absorb. Our full piece here: https://lnkd.in/gyBKXcit

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I sat down with Frontegg CEO Sagi on scaling CIAM for Insight Health. A few highlights from our convo, • Day-one enterprise readiness (HIPAA, multi-org, customizable access policies) • Why “build your own auth” slows growth (SSO/RBAC/SCIM, audit logs) • Delegated admin so partners self-manage without pinging engineering • CIAM as a GTM enabler for AI agents and clinician workflows Replay + transcript: https://lnkd.in/grRed-C5

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