AI Tech Leader's Club, Q2 2026

Name: AI Tech Leader's Club, Q2 2026
Start: 2026-06-24T17:30:00.000-07:00
End: 2026-06-24T21:00:00.000-07:00
Location: Palo Alto, CA

AI Tech Leaders' Club

Register to See Address

Palo Alto, CA

Past Event

Welcome! To join the event, please register below.

About Event

AI Agents in Production: Autonomy, Accountability & Control

Speakers

Prakash Nanduri, Global Head of Product Management, SAP Business Data Cloud

Mita Mahadevan, VP of Engineering, Pinterest

Gayathri Radhakrishnan, Partner, Hitachi Ventures

Nenshad Bardoliwalla, Group Vice President, AI Products, ServiceNow

Eli Chen, Technology Partner, AI Fund

Panel Moderator

Daniela Busse, Ph.D., VP Strategy & Transformation in Tech, Product & AI

Venue

SAP HQ, Palo Alto

Participants

Eligible attendees are current or recent (within three years) senior technology leaders, including CxOs, VPs, Heads of AI/Engineering/Product, Staff/Distinguished Engineers, AI-focused Fund GPs, and similar roles. Approval decisions are based on attendees' LinkedIn profiles.

Format

🎤⭐ Panel Discussion with top industry experts 💡👥 Collaborative Brainstorming on how to navigate AI's next phase 🌐🤝 High-Caliber Networking with peers from major tech companies Food 🧀🍷 Gourmet food & drinks will be served throughout the event.

Agenda

5:15 PM Check-in

5:30 - 6:30 PM Networking & Dinner

6:30 - 7:30 PM Panel Discussion: AI Agents in Production: Autonomy, Governance, and Control

7:30 - 8:30 PM Small Group Brainstorming: Tackling Individual Challenges

8:30 - 9:00 PM Networking

Cost

The event is free but requires approval based on your LinkedIn profile (see section Participants above).

Discussion Topics

🏗️ Defining operational boundaries for autonomous agents: How organizations determine what actions agents are allowed to take independently, where approvals are required, and under what conditions humans remain in the loop.

🔍 Governance, observability, and accountability: How enterprises trace, audit, and explain agent behavior, and what it means to operationally own an AI-driven decision in regulated and large-scale environments.

🛡️ Control systems and trajectory quality: Runtime guardrails, policy enforcement, validators, rollback mechanisms, and evaluating the full sequence of agent actions rather than just final-answer accuracy.

💰 The economics of autonomous AI systems beyond token costs: Orchestration complexity, retrieval overhead, replay and recovery costs, latency tradeoffs, and how enterprises evaluate ROI when autonomous systems operate continuously at scale.

🧩 AI agents as a stress test for enterprise architecture: How autonomous systems expose fragmented data, inconsistent governance, disconnected workflows, and unclear ownership, and how enterprises are adapting their technical and organizational foundations in response.