Aug 27, 2026

Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

Eon co-founders Ofir Ehrlich and Gonen Stein discuss the critical role of data as a protective moat in the AI era, exemplified by Google's purchase of Spirit Airlines' data. They explain how Eon addresses the challenges of scattered enterprise data by providing tools for mapping, classification, and secure access, enabling its use in AI workflows. The conversation also delves into the emerging threat of autonomous AI agents with legitimate system permissions, the fundamental shifts required in enterprise data infrastructure, and how the rapid, fear-driven adoption of AI contrasts sharply with the slower cloud migration era.

Aug 22, 2026

FinOps for AI Agents: Who Spent All the Tokens? — Tisha Chawla & Susheem Koul, Microsoft

TokenOps introduces a novel control plane for managing AI agent costs, shifting from simple throttling to proactive steering. By integrating an out-of-band system that annotates agent methods and provides a policy-driven governor, TokenOps can dynamically modify agent behavior—like making outputs more succinct—to reduce token consumption and prevent runaway loops. This approach significantly cuts average spend (78%) and dramatically improves run completion rates (from 67% to 96%) compared to traditional halting mechanisms, offering granular, attributable cost control for the agentic era.

Aug 22, 2026

Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic

Sachin Malhotra's talk outlines a critical framework for safely deploying autonomous agents in production, moving beyond simple token-based access. He introduces 'asymmetric verbs,' refilling 'rate limits,' 'trip wires' for aggregate monitoring, and the 'undo test' as a lens. A central tenet is that infrastructure (via a proxy) must stamp an agent's identity, preventing agents from circumventing controls and ensuring accountability, thus providing a "budget" instead of an unbounded "token."

Aug 02, 2026

Understanding AI Agent Hallucination in AI Systems

Learn about AI hallucinations, why they occur in autonomous agents, and how they pose new risks as AI takes action. Discover key mitigation strategies including data grounding, tool-based reasoning, scope control, and human-in-the-loop interventions to ensure reliable AI performance.

Jul 31, 2026

Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs

Mahesh Sathiamoorthy of Bespoke Labs argues that high-quality data and curated RL environments are the true bottlenecks for post-training LLMs, especially for building reliable, autonomous agents. He grounds this in experiences with OpenThoughts, a reasoning dataset, highlighting counterintuitive lessons like the importance of diverse reasoning traces and the fact that stronger teachers aren't always best. A key takeaway, reinforced by their Curator tooling, is that a disciplined curation stack is essential for transforming base models into capable, post-trained agents for real-world applications like credit card compliance.

Jul 21, 2026

Better Agent Auth — Bereket Habtemeskel & Paola Estefania, Better Auth

The Agent Auth protocol introduces a novel approach to securing AI agents by treating them as distinct principals with their own identities, moving away from sharing user credentials. It defines a system for agent discovery, fine-grained capability-based authorization, and comprehensive traceability, enabling users to grant specific authority to agents and revoke it granularly. This paradigm shift offers enhanced security, accountability, and control over autonomous operations.