The Agentic Buy-Side:
Building the Foundations for Scalable, Transparent AI Operating Models

Thursday
October 29, 2026

8:30 AM onwards,
followed by a cocktail reception

Venue 42 by Convene,
5 Times Square, NYC

Overview

Agentic AI is moving from concept to reality across the buy-side. But there’s a gap between potential and execution. Firms that haven’t addressed data fragmentation, process inconsistency, and governance at the foundation level will struggle to realize meaningful value from AI agents.
Mindmeld New York 2026 explores how leading firms are closing that gap, getting digitally organized first, then deploying agentic workflows that are transparent, traceable, and built to scale.

IVP MM2026 Agenda

8:30 AM|

Breakfast Networking and Registration

9:00 AM|

Welcome Keynote and Highlights

9:15 AM|

Keynote Presentation

To Be Announced

10:00 AM|

Panel Discussion:

Designing the Agentic Target Operating Model: The New Build, Buy, Outsource, and Offshore Calculus

Key challenges faced by funds in moving AI from experimentation into production operating models

Solution options across the sourcing spectrum — in-house, captive GCC, delivery partners, administrators, and loan servicers — as AI shifts the cost and control equation

Role of data, AI, and audit trails in operating model design, oversight (including shadow accounting), and service provider selection

Key lessons from transformation initiatives targeting efficiency, transparency, and TCO

Panelist:

Paulomi Shah

Partner, COO of Enterprise Solutions, Apollo Global Management

Alex Chiou

Managing Director, HPS Investment Partners

Additional Panelists To Be Announced

Moderator:

Gurvinder Singh

Chief Executive Officer, Indus Valley Partners

10:45 AM|

Break

10:55 AM|

Panel Discussion:

Institutionalizing Agentic AI through Data Maturity, Semantic Layer, and Build vs. Buy Decisions

AI-Enabled Target Operating Models: Designing operational architecture for AI readiness and truly functional agents

Designing the Agentic Data Foundation: Metadata orchestration, reference data integrity, and governance

The Semantic and Ontology Layer: Data fabric and business ontology for context-aware, trustworthy agentic performance

Reference Architecture for Agentic Workloads: Where agents sit, how they interact, and how humans stay in control

Data Steward Agents in Production: Using AI agents to monitor, clean, and reconcile data in real time

Build vs. Buy in the Agentic Stack: Platform and infrastructure decisions when agents are in the loop

Measuring What Matters: Defining ROI across efficiency, accuracy, transparency, and operational risk reduction

Panelist:

To Be Announced

Moderator:

Gaurav Aggarwal

Chief Commercial Officer, Indus Valley Partners

11:40 AM|

Panel Discussion:

Challenges in Private Credit in 2026

Impact of high profile defaults and fraud: Need to improve the quality of underwriting and on-going monitoring to be better prepared

Redemption pressure from the retail investors: Focus on investor education and managing operational processes to support distribution

Is Saaspocalypse real? Review performance of existing tech investments and future of investment

Valuation and Transparency Opacity: Need for daily valuation and impact of complex structures

Increased competition: Differentiation for investors and borrowers

Panelist:

To Be Announced

Moderator:

Piyush Singhi

Managing Director, Credit and Private Funds, Indus Valley Partners

12:30 PM|

Lunch

1:15 PM|

Panel Discussion:

From Processing to Oversight: Rethinking the Role of In-House Operations Teams

Why managers are moving from running operations to overseeing them

What scale delivers: cost control, global talent, round-the-clock coverage, and faster time to market

Technology now drives operations. Should you build it, or should your partner?

AI use cases that work, how to evaluate them, and how to track results

Panelist:

To Be Announced

Moderator:

Ashish Kumar Jain

Director, Indus Valley Partners

2:05 PM

Track 1

Master and Enterprise Data Management

2:05 PM - 3:30 PM

Track 2

Reconciliation Solution

2:05 PM - 2:45 PM

Track 3

Asset Backed Financing

2:05 PM - 2:45 PM

2:45 PM

Track 4

Navigate - NAV Oversight

2:50 PM - 3:30 PM

Track 5

FV 360

2:50 PM - 3:30 PM

3:30 PM|

Break

3:45 PM

Track 1

Treasury and Cash Management

3:45 PM - 4:30 PM

Track 2

Expense Allocation System (EAS)™

3:45 PM - 4:10 PM

Track 3

Private Funds Platform

3:45 PM - 4:10 PM

4:10 PM

Track 4

EDM- Developer's Delight

4:10 PM - 4:30 PM

Track 5

CLO's

4:10 PM - 4:30 PM

4:30 PM|

Panel Discussion:

Proof Over Promise: Modernizing at Scale in the Post-Hype AI Era

From Prototype to Production: What actually changes when GenAI moves from experimentation to enterprise-scale workloads?

Prompt Engineering → Systems Engineering: Where do traditional software architecture, distributed systems, and reliability engineering become critical, and how is the development methodology changing?

Deterministic Agentic Systems: How do we make autonomous/agentic workflows predictable, observable, testable, and recoverable?

API Boundaries & Guardrails: How should enterprises enforce strict tool/API access, permissions, data boundaries, and policy controls?

Governance Without Killing Velocity: How can enterprises introduce security, compliance, evaluation, and auditability without slowing engineering teams down?

Measuring “Production-Grade”: What metrics prove that an AI system is ready for enterprise adoption beyond accuracy or demo quality?

Practical Lessons & Enterprise Roadmap: What architecture decisions looked good in a prototype but had to be redesigned at real scale, and what interim milestones help enterprises move toward their target AI architecture?

Panelist:

To Be Announced

Moderator:

Sandeep Malhotra

Chief Technology Officer, Indus Valley Partners

5:20 PM|

Cocktail & Reception

Speaker Details

Alex Chiou

Managing Director, HPS Investment Partners

Paulomi Shah

Partner, COO of Enterprise Solutions, Apollo Global Management

The Agentic Buy-Side:
Building the Foundations for Scalable, Transparent AI Operating Models

Overview