Business

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#AI Models#Machine Learning#AI

MCP solved tool calling. A2A solved coordination. What solves transport?

The history of distributed computing is one of protocol proliferation followed by consolidation. Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), Java remote method invocation (RMI), and early simple object access protocol (SOAP) competed for the enterprise integration market in the late 1990s before representational state transfer (REST) quietly won by being simpler and HTTP-native.

#AI#Microsoft#Technology

Enterprise AI agents keep creating data silos. Microsoft's Build answer is Microsoft IQ and Rayfin.

Every new AI agent your team deploys starts from scratch: no memory of how the business works, where data lives, or what rules apply. And as agentic coding tools spin up applications faster than anyone can govern them, each one risks becoming another silo outside your data layer entirely.

#AI#Enterprise AI#Business

AI agents are quietly generating chaos engineering failures enterprises don’t track yet

There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template. The agent initiated an action.

#AI#Meta#Technology

AI tool poisoning exposes a major flaw in enterprise agent security

AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true.

#AI#Autonomous Systems#Robotics

Intent-based chaos testing is designed for when AI behaves confidently — and wrongly

Here is a scenario that should concern every enterprise architect shipping autonomous AI systems right now: An observability agent is running in production. Its job is to detect infrastructure anomalies and trigger the appropriate response.

#AI#Anthropic#Claude

Anthropic wants to own your agent's memory, evals, and orchestration — and that should make enterprises nervous

Just a few weeks after announcing Claude Managed Agents, Anthropic has updated the platform with three new capabilities that collapse infrastructure layers like memory, evaluation, and multi-agent orchestration, into a single runtime. This move could threaten the standalone tools that many enterprises cobble together.

#AI#Anthropic#Claude

Anthropic introduces "dreaming," a system that lets AI agents learn from their own mistakes

Anthropic on Tuesday unveiled a suite of updates to its Claude Managed Agents platform at its second annual Code with Claude developer conference in San Francisco, introducing a new capability called "dreaming" that lets AI agents learn from their own past sessions and improve over time — a step toward the kind of self-correcting, self-improving AI systems that enterprises have demanded before trusting agents with production workloads. The company also moved two previously experimental features .

#AI#Google AI#Google

Google made agentic AI governance a product. Enterprises still have to catch up.

Two weeks ago at Google Cloud Next ’26 in Las Vegas, Google did something the enterprise AI industry has been dancing around for the better part of two years: it made agentic AI governance a native product feature, not an afterthought. The centrepiece announcement was the Gemini Enterprise Agent Platform, pitched as the successor to Vertex AI […] The post Google made agentic AI governance a product.

#AI#Machine Learning#AI Models

New IDC: How EMEA CIOs can jumpstart AI rollouts

Getting stalled enterprise AI rollouts in the EMEA region moving again will require CIOs to aggressively audit their systems. Over the past 18 months, AI deployments across Europe advanced far beyond initial testing.

#AI#OpenAI#ChatGPT

Elon Musk and Sam Altman are going to court over OpenAI’s future

After a yearslong legal feud, Elon Musk and OpenAI CEO Sam Altman are heading to trial this week in Northern California in a case that could have sweeping consequences. Ahead of OpenAI’s highly anticipated IPO, the court could rule on whether the company is allowed to exist as a for-profit enterprise and might even oust….

#AI#AI Models#Machine Learning

Context decay, orchestration drift, and the rise of silent failures in AI systems

The most expensive AI failure I have seen in enterprise deployments did not produce an error. No dashboard turned red.

#AI#Enterprise AI#Business

Why AI agents need interaction infrastructure

To stop automation waste, enterprises must deploy interaction infrastructure that physically governs how independent AI agents operate. AI agents now populate corporate networks, reasoning through tasks and executing decisions with increasing autonomy.

#AI#ChatGPT#OpenAI

OpenAI unveils Workspace Agents, a successor to custom GPTs for enterprises that can plug directly into Slack, Salesforce and more

OpenAI introduced a new paradigm and product today that is likely to have huge implications for enterprises seeking to adopt and control fleets of AI agent workers. Called "Workspace Agents," OpenAI's new offering essentially allows users on its ChatGPT Business ($20 per user per month) and variably priced Enterprise, Edu and Teachers subscription plans to design or select from pre-existing agent templates that can take on work tasks across third-party apps and data sources including Slack, Goog.

#AI#Enterprise AI#Business

Amazon S3 Files gives AI agents a native file system workspace, ending the object-file split that breaks multi-agent pipelines

AI agents run on file systems using standard tools to navigate directories and read file paths. The challenge, however, is that there is a lot of enterprise data in object storage systems, notably Amazon S3.

#AI#AI Models#Machine Learning

As models converge, the enterprise edge in AI shifts to governed data and the platforms that control it

Presented by Box As frontier models converge, the advantage in enterprise AI is moving away from the model and toward the data it can safely access. For most enterprises, that advantage lives in unstructured data: the contracts, case files, product specifications, and internal knowledge.

#AI#AI Models#Machine Learning

OCSF explained: The shared data language security teams have been missing

The security industry has spent the last year talking about models, copilots, and agents, but a quieter shift is happening one layer below all of that: Vendors are lining up around a shared way to describe security data. The Open Cybersecurity Schema Framework (OCSF), is emerging as one of the strongest candidates for that job.

#AI#Enterprise AI#Business

The consequential AI work that actually moves the needle for enterprises

Presented by OutSystems After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused on the practical matters of governance, orchestration, and iteration, along with integrating agents into the systems they've spent dec.

#AI#Autonomous Systems#Robotics

Nvidia launches enterprise AI agent platform with Adobe, Salesforce, SAP among 17 adopters at GTC 2026

Jensen Huang walked onto the GTC stage Monday wearing his trademark leather jacket and carrying, as it turned out, the blueprints for a new kind of monopoly. The Nvidia CEO unveiled the Agent Toolkit, an open-source platform for building autonomous AI agents, and then rattled off the names of the companies that will use it: Adobe, Salesforce, SAP, ServiceNow, Siemens, CrowdStrike, Atlassian, Cadence, Synopsys, IQVIA, Palantir, Box, Cohesity, Dassault Systèmes, Red Hat, Cisco and Amdocs.

#AI#AI Models#Machine Learning

NTT DATA and NVIDIA bring enterprise AI factories to production scale

NTT DATA has announced an initiative to deliver NVIDIA-powered platforms designed to give organisations a repeatable, production-ready model for scaling AI. The offering integrates NVIDIA’s GPU-accelerated computing and high-performance networking with NVIDIA AI Enterprise software, including NeMo and NIM Microservices, into a full-stack agentic AI platform that can be deployed in cloud and edge environments.

#AI#AI Models#Machine Learning

Fixing AI failure: Three changes enterprises should make now

Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical.