The rollout eliminates a long-standing operational bottleneck for modern software developers. While generative AI coding tools have significantly accelerated application and website creation, provisioning the underlying domain infrastructure remains a heavily fragmented, manual process. Traditionally, engineers have had to halt their development loops, exit their code editors, navigate to a browser-based registrar portal, and execute multi-step registration and configuration sequences by hand. The new platform resolves this friction by embedding these operations directly into the environments where software is actively written.

“AI is fundamentally changing how software is created and the infrastructure behind the internet has to evolve with it,” said Travis Muhlestein, chief technology officer of product AI at GoDaddy. “Our Developer Platform now connects GoDaddy domain services directly into developer tools, giving our customers, whether they have one domain or thousands, the ability to complete the entire domain lifecycle. The result is faster domain search, purchase and configuration – going from idea to a live online presence in minutes.”

Also Read: Port Unveils Industry’s First Vibe Coding Experience for Platform Engineering 

Programmatic Control and Agentic Guardrails for Internet Infrastructure

The GoDaddy Developer Platform unifies domain logistics by exposing deep programmatic hooks designed to interact smoothly with local terminals, continuous integration pipelines, and Large Language Model (LLM) workflows.

The software architecture introduces five core components to optimize infrastructure provisioning:

Unified Domain APIs: Provides end-to-end programmatic access covering predictive domain search, live name recommendations, volume-specific account pricing, instant procurement, and automated DNS record configuration.

Modernized Authentication Protocols: Employs scoped Open Authorization (OAuth) tokens, enabling developers to delegate highly restricted, precise operational parameters to AI agents without exposing core account credentials.

Deterministic Agent Safeguards: Protects corporate capital through mandatory programmatic check points, requiring explicit price confirmation before transaction execution and strict validation gates for permanent, irreversible domain alterations.

Native Command-Line Interface (CLI): Translates the comprehensive API feature catalog into a fast, terminal-friendly console engineered for both human engineers and the autonomous coding agents operating beside them.

LLM-Optimized Developer Portal: Features a restructured documentation repository specifically indexed for seamless ingestion by AI models, accelerating the path from initial account registration to a successful API call.

Future-Proofing Scalable Domain Operations

The initial release marks a strategic evolution for enterprise teams, digital agencies, domain portfolio managers, and independent creators who manage online properties at scale. By abstracting manual configuration layers into developer-centric interfaces, the system transitions domain registry from an administrative chore into a scalable code asset.

“GoDaddy’s Developer Platform is built for the next generation of builders,” said Muhlestein.

Looking ahead, subsequent deployment phases are expected to expand the platform’s footprint deeper into professional investor mechanics, aftermarket exchange APIs, self-healing automated DNS repair scripts, high-volume bulk tools, and expanded endpoints for GoDaddy’s broader cloud product catalog.

The GoDaddy Developer Platform is live and available immediately as a free tier upgrade for technology teams worldwide. While existing enterprises running legacy API frameworks can continue daily operations without operational interruption, they retain the flexibility to migrate to the new beta environment at any time. Software engineers, platform architects, domain investors, and agency leads can download the native CLI, evaluate the programmatic documentation, and initialize automated domain registration by visiting GoDaddy’s modern developer portal.

The post GoDaddy Launches Developer Platform to Automate the Domain Lifecycle appeared first on ITDigest.

Build at the speed of AI, governed by the enterprise

Decisions unites the speed of vibe coding to enterprise application development while keeping business logic, data, and workflows securely governed.

Development teams can use AI tools such as Claude Code to rapidly create front-end experiences, while Decisions provides a safe, scalable, and production-ready backend of flows, data, and business rules. Because AI-generated interfaces remain connected to the Decisions’ orchestration layer, organizations can deliver modern applications without creating disconnected systems or exposing critical business logic.

Whether serving as the front end, the backend, or both, Decisions integrates with existing enterprise systems to support governed, end-to-end automation.

“Decisions transforms vibe coding from a weekend experiment into something the enterprise can actually trust,” said Decisions CEO Giles Whiting. “Vibe coding is phenomenal for spinning up front-end experiences at AI speed, but it introduces risk when those AI-generated interfaces get anywhere near production data, rules, and business logic. With our latest release, our customers can use whatever vibe-coding tool they like on the front end, while keeping their core decisions, rules, and data protected behind a governed Decisions backend. They get the velocity of AI-assisted development, without crossing business red lines on control, compliance, and reliability.”

Also Read: Quality Clouds Launches Hub: Enterprise-Grade Governance for AI-Generated Code

The Decisions Deployment Tower: governed deployment, built in

As automation initiatives grow, the hard part is moving solutions safely across environments. Deployment Tower makes release governance part of the platform, connecting planning, validation, and promotion into a single governed lifecycle and making it possible to:

• Understand what’s deployed where, across every environment.
• Trace every deployment back to the originating task (whether managed in Jira, Azure DevOps, or another project management system).
• Govern the movement of changes, with access controls, approvals, and audit built in.
• Control deployments at scale, from a single organization to many.

By importing stories and sprints directly into Decisions and keeping them synchronized, Deployment Tower replaces opaque revision numbers with a clear record of what changed, when, why, and by whom, giving teams in regulated industries a stronger foundation for traceability, governance, and audit.

An upgraded, more intuitive experience

This release also modernizes the entire platform with a cleaner, intuitive interface that reduces visual noise while making complex automation easier to build and manage.

Redesigned rule and truth table authoring improve readability and simplify the management of sophisticated business logic. Updated navigation, consistent design modules, and a new dark mode make the platform easier to learn and faster to build in, without sacrificing enterprise-grade capabilities.

“Teams everywhere want to build faster, but what really changes the game is being able to move quickly and trust every outcome,” said Whiting. “The latest release of the Decisions platform gives customers an even smarter way to create and a more controlled deployment, orchestrating work across people, systems, and AI so the more they build, the more value and confidence they get back from the business.”

Source: PRNewswire

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The post Port Unveils Industry’s First Vibe Coding Experience for Platform Engineering appeared first on ITDigest.

The launch responds to a structural shift in software delivery. As AI coding assistants drive unprecedented volumes of code, enterprises face a widening gap between how fast code is produced and how reliably it is reviewed exposing them to security, compliance, and quality risk at scale. Quality Clouds Hub closes that gap with automated certification, quality gates, rule enforcement, and a complete audit trail of every scan, decision, and policy applied giving compliance and risk teams a defensible record of how AI-generated code was governed, and giving engineering teams the confidence of knowing exactly what is being shipped.

Also Read: iTmethods Joins AI Governance Foundations for Regulated AI

Hub is built on nearly a decade of governance data spanning more than 950 enterprise instances and 13 million catalogued issues — the foundation of governance already trusted by Fortune 500 organisations across financial services, manufacturing, and consumer goods.

“Production-ready AI isn’t about writing code faster — it’s about trusting what reaches production. And what ‘production-ready’ means is different for every organisation,” said Albert Franquesa, Co-founder & Board Member at Quality Clouds. “Hub lets each enterprise define those standards for themselves and enforce them automatically, so AI Code Governance becomes something teams control rather than something they react to.”

Source: GlobeNewswire

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In Hardware and Network news this week…

Efficient Launches Efficient Labs Interactive Tool Suite

Efficient Computer, the company building the world’s most energy-efficient general-purpose processors, launched Efficient Labs, a collection of interactive, browser-based tools for engineers working with the Electron E1 general-purpose processor. Efficient Labs is built to get useful tools into customers’ hands quickly, learn from how they are used, and improve them fast.

In Artificial Intelligence news this week…

The Rise of Sovereign AI: How the Cognizant-Domyn Partnership Redefines the Artificial Intelligence Landscape

The global Artificial Intelligence (AI) industry is experiencing a massive shift in how technology is developed, managed, and deployed. While the initial wave of the AI boom was dominated by generalized public large language models (LLMs) running on centralized, borderless hyper-scaler clouds, the next frontier belongs to data control, localized governance, and national compliance.

In Cloud Computing & Mobility news this week…

Options Tech Launches AtlasInsight V5 to Elevate Financial Trading Infrastructure Performance

Options Technology has officially rolled out AtlasInsight V5, the latest iteration of its prominent network visibility and analytics engine. The upgraded platform introduces substantial leaps forward in data processing speeds, user accessibility, and infrastructure diagnostic capabilities.

In Enterprise Software news this week…

Bizoforce is launching HeyAdmin.ai: An Enterprise-Grade Agentic AI Platform

Bizoforce is launching HeyAdmin.ai – an Agentic AI platform for Enterprises. Agentic AI autonomous workflows can be built on our platform and deployed on any platform at low cost through voice, video, and text agents.

In Quantum Computing news this week…

IQM Achieves Milestone in Quantum Error Correction, Enabling Fault-Tolerant Computing

IQM Quantum Computers, a global leader in superconducting quantum computers, announced a significant breakthrough in quantum error correction (QEC) with the introduction of “directional tile codes.” Developed in collaboration with researchers from Freie Universität Berlin, the University of Edinburgh, and Johannes Gutenberg-Universität Mainz, the framework achieves up to a 1,000-fold reduction in logical error rates compared to traditional surface codes without requiring modifications to standard hardware.

In FinTech news this week…

Infinite and Sardine Unveil Bidirectional Integration to Unify Compliance, Risk Management, and Digital Payments

Infinite, an AI-native compliance and payments architecture engineered specifically for the stablecoin landscape, has joined forces with fraud prevention and transaction network Sardine to launch a comprehensive two-way system integration. The strategic partnership merges Sardine’s live fraud and risk analytics directly with Infinite’s automated compliance workflows and dedicated account infrastructure.

In HealthTech news this week…

Anthropic Launches Claude Science: A Paradigm Shift for the Biotech and Life Sciences Industries

As part of its ambitious mission to change the laboratory workflow and scientific discovery process, Anthropic has introduced Claude Science – an AI-based workbench designed exclusively for researchers. Going beyond generic conversational models, this particular system will function as an operational platform for conducting complicated research in biology, chemistry, and medicine through integrating specialized tools, native visualization, and computing infrastructure. The objective is to address one of the biggest frustrations in contemporary science – the fragmentation of the workflow.

In Cybersecurity news this week…

ServiceNow and Accenture Team Up to Move Enterprises Off Legacy Risk Stacks

The modern enterprise threat landscape has outpaced the capabilities of traditional governance, risk, and compliance (GRC) tools. Historically, risk management operated as a slow, manual cycle of checkbox audits, detached vendor spreadsheets, and point-in-time compliance checks.

Article of the Week

How to Adopt DevOps Culture in Large Organizations: A Practical Guide to Enterprise Transformation 

Most enterprise software problems don’t begin with bad code. They start way earlier, like inside meeting rooms, approval chains, and groups that barely understand how the other side does things. In fact, a lot of companies dump millions into cloud platforms, automation tools, and newer infrastructure, hoping for speedier delivery, right. Then nothing really changes. Releases still move slowly.

The post ITDigest’s Weekly News Roundup Featuring Anthropic, ServiceNow, IQM, Accenture, Options Tech and more appeared first on ITDigest.

 iTmethods , a company developing the governance and assurance layer for agent-based AI in enterprises, announced its membership in the Linux Foundation as a Silver member. Through its membership in the Fintech Open Source Foundation (FINOS) and the Agentic AI Foundation (AAIF), iTmethods will contribute its expertise in runtime governance, tamper-proof evidence, and model portability to the development of open standards that will be instrumental in establishing trust in autonomous AI in regulated environments.

As banks, insurance companies, and other regulated institutions move agent-based AI from the pilot stage to production, the ability to demonstrate control becomes a prerequisite for deployment. iTmethods addresses this challenge with “Continuous Agentic Assurance”: the governance, evidence, and portability layer that enables companies to run any number of models, exchange models under pressure, and demonstrate control to regulators.

The company participates in three interconnected open-source initiatives that lay the foundation for trustworthy agent-based AI:

• The Linux Foundation provides the neutral governance framework for the most widely used open standards.
• FINOS , the financial services arm of the Linux Foundation, is advancing work on responsible AI governance through its AI Fund (backed by DTCC, Morgan Stanley, RBC and NatWest) and the recently announced Open Source Enterprise Resiliency Alliance (OSERA).
• The Agentic AI Foundation (AAIF) develops open standards for interoperable autonomous agents, including the Model Context Protocol (MCP).

iTmethods is already active in this ecosystem. The company offers a managed, regulated implementation of Fluxnova, the open-source orchestration platform hosted by FINOS, applying runtime governance and tamper-proof evidence to autonomous workflows in regulated environments. iTmethods brings this practical experience to the standardization efforts.

Also Read: TestMu AI Introduces DevTools Assertions in Kane CLI, Enabling Browser-Level Validation Through Natural Language 

“In the highly regulated financial services industry, compliance must be automated and infrastructure observable,” said Olivier Poupeney, Field CTO at FINOS. “That’s why we are jointly developing open-source standards and tools including the AI ​​Governance Framework (AIGF), Common Cloud Controls (CCC), Fluxnova, and CALM to provide the ‘governance-as-code’ pipeline required for the responsible scaling of AI. We are very pleased to welcome iTmethods, whose runtime expertise will help the industry safely move agent-based AI into production.”

“Open standards will determine who is trusted in the age of agent-based AI,” said Paul Goldman, CEO of iTmethods. “The missing piece of the puzzle in much of this work is the control and assurance layer that proves what an agent has actually done. We are joining in to bring this operator perspective from regulated environments.”

iTmethods also operates Dark Factory , its regulated platform for autonomous software development, and publishes The Trust Layer, a weekly series on governing agent-based AI in regulated industries.

Source: PRNewswire

The post iTmethods Joins AI Governance Foundations for Regulated AI appeared first on ITDigest.

Also Read: Danske Bank and AWS Extend Collaboration to Accelerate AI and Cloud Innovation

Recipe Linter that allows deterministic validation of recipes locally and Recipe Visualizer that allows visualization of recipes within development environments including VS Code, Cursor and Windsurf. The toolkit also supports tasks such as enterprise integration migrations, Git-based asset management and AI-assisted workflow creation. “AI coding assistants are core to how developers build,” said Adam Seligman, Chief Technology Officer and General Manager of AI Incubation at Workato. “Developers love code, and AI agents do too. Workato Labs extends Workato into developer tools, making it easier to build, validate, and manage Workato projects using the same tools developers already trust, like Claude Code and Codex.” Early adopters, including onXmaps, have already integrated the CLI into existing development practices.

Read More: Workato Launches Workato Labs with a New Open-Source Developer Toolkit for Enterprise Orchestration

The post Workato Unveils Open-Source Developer Toolkit to Simplify AI-Driven Automation Development appeared first on ITDigest.

The coordinated product rollout represents a significant shift in how developers interact with enterprise data layers. While AI projects frequently stall because navigating rigid IT security protocols can delay accessing live production data, CData’s new ecosystem allows developers to securely tap into hundreds of enterprise platforms including Salesforce, Snowflake, NetSuite, Microsoft 365, and Workday—using standard SQL, Python workflows, the command line, and the Model Context Protocol (MCP).

“Developers have been forced to choose between moving fast and meeting the governance their company requires. That tradeoff doesn’t hold up anymore,” said Raviv Levi, Chief Product and Technology Officer at CData. “The same live, governed access that IT trusts and the business depends on should follow developers into the terminal, the Python environment, and the IDE. That’s what these releases do.”

Unlocking Managed Data Connectivity with Three New Developer Tools

CData’s new offerings directly target the data layer friction points that frequently cause enterprise AI initiatives to stumble, such as API rate limits, authentication drift, and schema changes. The launch introduces three specific developer entry points:

Connect AI Developer Edition (Free): This tier gives developers full, free access to CData’s enterprise feature set. It automatically exposes legacy APIs as standard, queryable data layers while natively managing token refreshes, pagination, and API throttling behind the scenes. It features complete MCP server support to interface out of the box with popular AI development environments and coding assistants like Claude Code, Codex, Cursor, and LangChain.

CData Connect AI Python SDK (Open Source): Built on the familiar DB-API 2.0 specification, this open-source, MIT-licensed SDK allows developers to pull live, governed corporate metrics directly into native Python workflows. Engineers can utilize familiar data-science packages like pandas and SQLAlchemy to communicate with enterprise systems without learning custom APIs.

CData CLI (Command-Line Interface): A terminal-native tool designed so coding assistants can programmatically query and scaffold connectivity without needing manual documentation review. The initial release provides robust support for CData’s JDBC and Python drivers, with ADO.NET and ODBC capabilities planned for subsequent updates.

Also Read: Kurrent Launches Capacitor to Drive Human-Agent Code Collaboration

Mitigating Token Waste and Schema Drift in Agentic AI

A key technical challenge in multi-agent configurations is managing unexpected schema drift. For example, if an operations manager creates a custom field within a CRM on Monday night, an unguided AI agent executing an automated report on Tuesday morning may generate inaccurate or corrupt data outputs due to missing context. Connect AI resolves this issue through real-time metadata discovery, pinging the target database on every query to ensure the AI model is always acting on the platform’s current state.

Furthermore, to combat the problem of “over-permissioning” in agentic setups, the software introduces Toolkits. This feature allows engineering teams to package precise data scopes into unique MCP Server URLs tailored for specific tasks. This ensures that an active AI worker only receives the exact data parameters required for its given objective, keeping security tight and preventing unexpected token drain.

“The need to accelerate development exists everywhere,” said Jerod Johnson, Director of Technology Evangelism at CData. “Product owners are asking, ‘You have an AI coding assistant, why isn’t this shipping faster?’ And people are still running into the problem — I can ship that feature, but I also have to build the connector that gets the data to it.”

All three developer-centric solutions are fully live and available for download. Engineering leads, enterprise data architects, and software developers can review setup protocols, download the open-source Python driver packages, and join the developer community by visiting the official CData Developer Center.

The post CData Software Launches Connect AI Developer Edition, Open-Source Python SDK, and CLI appeared first on ITDigest.

Traditional browser automation focuses on what users can see: buttons, forms, text, and visual states. However, modern web applications depend heavily on browser activity that remains invisible to users, including network requests, API responses, console logs, cookies, local storage, and performance metrics.

With DevTools Assertions, Kane CLI extends browser validation beyond the UI, allowing teams to verify these underlying signals using plain-English objectives.

Developers can now write objectives such as:

• “Click Add to Cart and assert the request to /api/cart returned status 200.”
• “Run the checkout flow and assert the console has no errors.”
• “Log in and store the session cookie value.”
• “Assert localStorage contains the selected product ID.”
• “Open the pricing page and assert it loads in under two seconds.”

Also Read: GitKraken Introduces Code Flow, a Framework for Software Development in the Agentic Era

Kane CLI automatically maps these objectives to the appropriate browser signals during execution. Running in a real Chrome browser, the platform observes network requests, responses, console activity, browser storage, cookies, and performance timing while the workflow executes.

Unlike traditional automation frameworks, DevTools Assertions require no selectors, instrumentation, or custom code. Users specify intent, and Kane CLI performs the validation automatically.

“As software becomes increasingly API-driven and AI-generated, validating the UI alone is no longer enough,” said Mudit Singh, Co-Founder and Head of Growth at TestMu AI. “Many production issues originate in network failures, browser storage inconsistencies, performance regressions, or client-side errors. DevTools Assertions help teams validate the signals that truly determine whether an application is working correctly.”

The capability supports common validation operators such as equals, contains, greater than, and less than, while automatically normalizing browser events and console outputs for easier interpretation. DevTools Assertions further advances Kane CLI’s mission of making browser validation accessible through natural language while enabling AI agents to perform deeper, more reliable software verification. The feature is available immediately in Kane CLI.

Source: BusinessWire

The post TestMu AI Introduces DevTools Assertions in Kane CLI, Enabling Browser-Level Validation Through Natural Language appeared first on ITDigest.

The announcement is accompanied by new research conducted by GitKraken in May 2026 among more than 550 software developers, revealing that AI adoption has moved well beyond experimentation.

Key findings include:

• 96% of respondents reported some level of AI adoption on their team
• 65% said more than half of their team has adopted AI tools
• 30% actively run multiple AI agents in parallel, with another 33% experimenting with this practice
• 28.5% are already allowing AI to operate autonomously, either individually or alongside other agents

“What’s changing isn’t just how developers write code – it’s how they work,” said Matt Johnston, CEO of GitKraken. “Increasingly developers are increasingly managing multiple agents on ever growing swaths of work, coordinating work across repositories, and evaluating AI-generated output. The challenge is no longer generating code. It’s how to turn agent-generated code into high-quality, shipped software – from plan to main ”

As AI adoption accelerates, developers are beginning to work in ways that were unthinkable just 6-12 months ago. Instead of implementing a feature themselves, they may direct multiple agents working in parallel, use different models for different tasks, and spend more time reviewing and coordinating work than producing it directly.

Also Read: Harness Acquires Codecov from Sentry to Strengthen Software Delivery Governance in the AI Era

GitKraken’s research found that the top uses of AI in software development today are:

• Generating and refactoring code
• Troubleshooting and debugging
• Conducting code reviews

These changes are creating a new set of challenges for engineering teams:

• Which models are best suited for different types of work?
• How many agents should be running simultaneously?
• How prepared is a repository for agent-driven development?
• How do teams maintain standards and context across parallel workstreams?
• How do organizations safely integrate and merge dramatically larger volumes of code?

GitKraken customers coined the term for this emerging challenge as Code Flow.

Code Flow describes how work moves between developers, coding agents, repositories, planning systems, reviews, branches, and production environments. It encompasses the coordination, visibility, governance, and integration required to transform AI-generated output into working software.

Historically, software development tools were built around a simple assumption: humans wrote code and Git tracked the result.

Today, developers may have multiple agents operating simultaneously across multiple repositories, creating significantly more branches, pull requests, commits, and merge activity than teams managed only a few years ago.

“The industry has spent years focused on helping developers write code faster,” Johnston said. “As AI adoption grows, organizations need better ways to understand what’s happening across their development systems, where work is getting stuck and how agents are performing. The challenge has become managing the exploding velocity and volume of work coding agents can create. That’s what Code Flow is about.”

GitKraken recognizes Code Flow will become an increasingly important discipline as organizations expand their use of AI agents and autonomous development systems – and demand positive returns from their investments in AI developer tooling.

Because in the agentic era, writing code is only part of the job.

Moving that code from plan to main, that is Code Flow.

Source: PRNewswire

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