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Orchestration

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How enterprises coordinate and scale AI in production.

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VB Event

Scaling AI agents requires three things: Inventory, contracts and maintained context

Enterprises that run more than a handful of AI agents in production face risk, cost and oversight burdens that grow exponentially with every agent they add, says Felix Van de Maele, founder and CEO of data governance company Collibra. In a conversation with VentureBeat founder and CEO Matt Marshall at VentureBeat's AI Week event in New York, Van de Maele traced most agent failures to the business context those agents receive — and said companies need deterministic controls on agent behavior before their agent count grows.

Infrastructure

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The hardware and platforms underneath enterprise AI.

VB.WEKA.AI Impact Session.10.02.26

VB Event

Up to 60% of GPU compute goes to recomputing context. Flash storage can put that capacity back to work

GPUs running large language model inference can spend up to 60% of their compute recalculating context they already processed, says Alan McSeveney, field CTO at WEKA. Speaking with VentureBeat lead analyst Rob Strechay at the latest stop in the VentureBeat AI Impact Series, McSeveney said the waste goes back to how large language models hold conversation history in GPU memory, arguing that fixing it lets companies serve several times more users on hardware they already own. The efficiencies WEKA customers struggle with most show up in infrastructure, separate from the application metrics each business tunes for its own domain.

Events

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Conferences and summits where enterprise AI strategy gets made.

Data

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Pipelines, architecture, and governance for AI-ready systems.

Security

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Protecting AI systems and the data behind them.

Technology

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Enterprise tech coverage through the lens of AI adoption.

Newsroom

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Video

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VB Latest

Why the enterprise AI security gap starts below the model

Moving AI into production requires confidence in how sensitive data, intellectual property and business operations will be protected. What should enterprise leaders expect from the infrastructure partners supporting that transition? Nir Chervoni, Head of Security Products at Nebius, discusses how enterprises can assess AI security, retain practical control over their workloads and evaluate emerging approaches such as confidential computing. The conversation will explore the questions leaders should ask as they balance AI ambition with security, sovereignty and business continuity.

VB Transform 2026

Zendesk's 20 Billion Conversation Problem

Agentic infrastructure: How legacy stacks create the bottleneck to AI autonomy Multi-agent systems fail in production if the infrastructure underneath them isn't designed for autonomous, long-running loops. Token costs compound across chained tasks, latency bottlenecks surface inside orchestration layers, and agents drift from their original intent with no mechanism to catch this drift before downstream damage is done. This panel brings together agentic AI leaders who have moved past first-generation agentic deployments to deconstruct four key drivers of success: Inference Economics: How teams are managing cost-per-task in long-running agentic loops, including where speculative decoding and purpose-built small language models actually reduce spend versus where they add complexity. Physical Infrastructure decisions: Compute density, storage, memory, GPUs, inference processing units Orchestration Design: When a central master agent creates a single point of failure versus when a decentralized mesh or hybrid approach introduces coordination overhead and how to make that call based on your workload. Governance at the Infrastructure Layer: How teams are embedding rate limits, kill switches, and audit trails directly into the infrastructure stack to contain runaway API calls and prevent cascading failures before they reach production users.

VB Transform 2026

220 Million Miles. Zero Driver.

VB Transform 2026 Highlights Intelligence at Scale: How Waymo Builds Safe, Efficient AI for the Physical World The Waymo Driver represents the most mature application of AI operating in the physical world. With over 200 million fully autonomous miles driven and an expansion plan for 20+ cities, the company’s pioneering work serves as a roadmap for how to build AI systems that are reliable and safe, even in the most unpredictable scenarios. In this session, Waymo’s director of systems intelligence and machine learning, Manasi Joshi, reveals the key engineering approaches behind their operation and how data systems are improving Waymo's already exemplary safety record. This session will provide lessons for technology leaders in all industry verticals who are scaling their own agentic deployments.

More

VB Pulse September

OpenAI's AI agent platform is used by twice as many companies as Anthropic's, new VentureBeat survey finds

Twice as many companies use OpenAI's agent orchestration platform as Anthropic's, VentureBeat Intelligence found in its September survey on agent orchestration. OpenAI holds that lead even though Anthropic may lead OpenAI on other important indicators. For example, Ramp, the corporate card and spending company, reports that more businesses now pay for Anthropic than for OpenAI.

Control layer

AI agents can be unpredictable. Your control layer shouldn't be.

In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.