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Orchestration

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How enterprises coordinate and scale AI in production.

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Salesforce just put its entire CRM inside Claude — and says you’ll never need its app again

The centerpiece of the announcement is Salesforce in Claude, a plugin for Anthropic's Claude CoWork that ships with 37 pre-built sales skills — covering meeting preparation, deal health reviews, and pipeline analysis — and lets sellers query, update, and act on live CRM data without ever opening Salesforce itself. The product is available to select pilot customers today, with an open beta planned for September and additional skills for other business functions beginning to launch in the third quarter.

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Anthropic’s new Claude Tag update lets its Slack agent read the full conversation — and jump in unprompted

In an exclusive interview with VentureBeat, Scott White, Anthropic's head of product for enterprise, laid out the company's thesis for what it calls "multiplayer AI": a shift away from the single-user chatbot paradigm toward AI agents that operate across teams, read organizational context, and proactively insert themselves into work — sometimes without being asked.

Messy data

Enterprise AI agents are only as reliable as the messiest documents behind them

Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individual AI applications. While this approach works well for isolated assistants and copilots, it treats enterprise knowledge as application-specific context rather than a shared enterprise asset.

Infrastructure

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The hardware and platforms underneath enterprise AI.

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Perplexity partners with Nvidia to launch Portable Computer, a fully local AI agent with zero token costs

The launch, developed in close partnership with Nvidia, is one of the most aggressive attempts yet to move serious AI agent workloads off the cloud and onto local devices. The model, the user's files, and the work itself can all stay on the machine. Work completed locally consumes no billing credits, and the company says every task starts on the device by default — with the system asking permission before sending any individual step to a more powerful frontier model in the cloud.

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IBM’s next-gen mainframe chip is the first to run Arm and Z workloads on the same cores

The chip, which will power the next generation of IBM Z and LinuxONE systems, is the first dual-architecture mainframe processor ever built. It is designed to let enterprises run the vast and fast-growing ecosystem of Arm-native Linux software — including the AI frameworks that increasingly define modern infrastructure — directly alongside the z/OS transaction-processing workloads that anchor the world's banks, insurers, and governments.

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 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.

VB Transform 2026

The 'Two Poisons' Problem in AI Testing

VB Transform 2026 Highlights LLM-as-a-judge vs. human-in-the-loop oversight: The great debate on model validation As agents move from prototype to production, the evaluation bottleneck has become one of the biggest hurdles. Can we truly trust an LLM to grade another LLM, or are we simply building a hall of mirrors? In this session, panelists weigh all sides of model validation, testing and putting trusted agents into production. They’ll get into the necessity of LLM-as-a-judge for real-time, automated observability at scale vs. the real world challenges of poor agent responses and the indispensable role of golden-set validation and human-in-the-loop oversight.

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AI yielding

Enterprises winning with AI agents are limiting how much the agents can do alone

For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments — and in a lot of deployments it's failing. The companies that end up benefiting from agentic AI won't necessarily be the ones that have given their agents the most flexibility. They're the ones who create AI agents with specific responsibilities and make sure they operate within clear rules.

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Slack wants to drag AI coding out of the terminal and into the group chat

The Salesforce-owned messaging platform today announced Slack Code, a new product that embeds AI coding agents — including Anthropic's Claude Code, Cognition's Devin, GitHub Copilot, and Vercel's agent — directly into dedicated Slack channels where entire teams can watch, steer, review, and ship software together. Slack Code is available on any Slack plan at launch, though customers need their own access to the partner agents.

Rob Strechay

VentureBeat names Rob Strechay as its first Lead Analyst, expanding its enterprise AI research push

Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a founding analyst of VentureBeat Research. His arrival is the next step in a deliberate move at VentureBeat toward deeper specialization: analysis built for the technical decision-makers — the directors, VPs, CIOs, and CTOs — who are evaluating, buying, and deploying enterprise AI.