标签：#Enterprise

IBM plans to train and certify tens of thousands of consultants on OpenAI's technologies as part of this deal.

Thrive Holdings has raised $2 billion in new funding at a $12 billion valuation from investors like SoftBank, D1 Capital Partners, and Altimeter Capital.

If you have ever wanted to ship an AI-powered application without managing GPUs, model servers, or scaling infrastructure yourself, this guide is for you. Managed inference simply means letting a cloud provider run the AI model for you: you send a request, the platform handles the compute, and you get a response back. On Google Cloud, the cleanest way to do this today is to pair the Gemini Enterprise Agent Platform (formerly Vertex AI) with Google Cloud Run , dividing responsibilities between the two services. The Agent Platform serves as the orchestration and intelligence engine, while Cloud Run hosts your custom application logic, front-end UIs, or Model Context Protocol (MCP) servers. By the end of this article, you will be able to: Explain the hybrid architecture and why each layer exists Define an AI agent in code using the Agent Development Kit (ADK) Deploy your app layer to Cloud Run with a single command Choose between online and batch inference for your workload Secure and monitor the whole setup in production New to the underlying concept? Start with Google Cloud's primer: What is AI inference? Prerequisites To follow along hands-on, you will need: A Google Cloud project with billing enabled The gcloud CLI installed and authenticated Python 3.10+ and the ADK installed ( pip install google-adk ) You can also read this purely as an architecture walkthrough; every step is explained, not just shown. 1. The Architectural Blueprint This pattern splits your system into independent, auto-scaling tiers: [ Client / Web UI ] ──> [ Cloud Run Service ] (App Logic / Tool Front End) │ ▼ [ Gemini Enterprise Agent Platform — Agent Runtime ] (Orchestration, Intent Analysis, Memory) │ ▼ [ Managed Inference / Model Garden ] (Gemini 3.x Pro / Flash models) Why split it this way? Each tier scales independently and fails independently. Your web front end can handle a traffic spike without touching the model layer, and you can swap models without redeploying your application code

Blacksmith says revenue has grown more than tenfold over the past year.

This lawsuit follows one filed last month by Runlayer that accused Rippling of stealing its product ideas. It's a seller- and buyer-beware market warning.

Meta’s new open-weight Muse Glimmer model offers a glimpse of Mark Zuckerberg’s personal superintelligence vision, as well as the emerging divide between AI users can own and access.

Programming with Claude Code will soon require even less human oversight.

After its own AI usage wake-up call, Rippling this week unveiled AI Spend Console, a product that tracks individual and team employee AI spending.

Mirendil has signed a $100 million-plus Google Cloud partnership to expand its compute infrastructure, powering research into self-improving AI systems designed to accelerate scientific discovery and AI development.

The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.

After a quarter that delivered $1 billion in profit, Palantir CEO Alex Karp on Monday once again warned that AI frontier labs are too untrustworthy for enterprises.

The Observability Black Box As autonomous AI agents evolve from isolated chat assistants into multi-agent systems executing multi-step business logic across databases, APIs, and microservices, enterprise platform teams face an acute operational challenge: black-box opacity. When an autonomous agent fails, hallucinates, or executes an out-of-bounds API call, traditional Application Performance Monitoring (APM) tools fall short. Standard HTTP request logging and basic prompt-response captures cannot reconstruct the non-deterministic reasoning loops, tool selection branches, or sub-agent delegations that led to an incident. Furthermore, enterprise auditors, security teams, and regulatory bodies (governed by SOC 2, FedRAMP, and the EU AI Act) now require non-repudiable proof of agent execution. Organizations must be able to answer five fundamental questions for every production run: Which human or non-human identity authorized the agent run? What planner reasoning path or tool routing logic was chosen? Which exact data assets or vector embeddings were retrieved into context? What was the precise execution latency, token cost, and error tax of each intermediate step? Can the complete execution graph be cryptographically reconstructed for compliance review? To resolve this challenge, platform engineering teams must deploy Audit, Observability & Lineage —an architecture anchored in OpenTelemetry (OTel), OWASP Agent Observability Standards, and immutable lineage graphs. Deep-Dive Architecture: OpenTelemetry & Lineage Integration A production-grade Agent Observability stack avoids proprietary vendor lock-in by standardizing on OpenTelemetry (OTel) OTLP trace ingestion and open metadata stores. 1. The Unified OpenTelemetry Span Tree Every agent execution unit — from user intent trigger to final task completion — is encapsulated within a single root trace context ( agent.run ). Sub-tasks, tool calls, and model invocations are recorded as hierarchical child spans: [ Root Trace:

A new study estimates only 2,000 U.S. engineers have the expertise to deliver meaningful AI ROI, as enterprises race to hire forward-deployed engineers to implement AI at scale.

Microsoft pitched its own homegrown AI models, harnesses, and even a Mythos competitor on Wednesday, telling Wall Street it plans for continued growth.

On the company’s second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a “large enterprise opportunity” spanning AI agents, APIs, compute, and internal software.

The startup analyzes calls, messages, and CRM data to identify effective sales techniques and turn them into playbooks for AI agents.

Cloudflare has launched its Internal DNS service, providing authoritative and recursive DNS for private networks. This service simplifies DNS management by consolidating private and public DNS operations on a single platform. By Gianmarco Nalin

Runlayer is suing Rippling after Rippling evaluated the startup's MCP gateway product and then opted to build one itself.

Companies without their own models — or without a layer of AI infrastructure known as AI gateways to separate their prompts from the model itself — will be in trouble, Nadella says.

Airbus selected Scaleway as its sovereign cloud provider after a tender that scored protection against non-European extraterritorial legislation alongside technical capability. Airbus frames it as complementing multi-cloud, not exiting AWS. Practitioners note the pattern is spreading past hyperscalers to small US SaaS vendors, and that sovereignty claims still require verifiable controls. By Steef-Jan Wiggers