Summary Bullets:

• Conversational AI is driving a major voice resurgence in customer service by improving the IP voice experience.

• Long call queues on account of human agents being thinly stretched across large call volumes have led to customer frustrations and poor CX, but modern versions of conversational AI are set to change that by taking away the frictions associated with voice.

It was believed that voice would diminish in value as digital channels started to emerge as an alternative to the voice channel. While messaging and chatbots offer cost and scale advantages, GlobalData’s research shows that voice continues to remain a vital channel for connecting with customers despite the frictions. Voice provides instant gratification and tends to be more straightforward to use as it does not involve navigating through messaging channels, websites and so and having to type messages. While the elderly demography tends to prefer voice due to greater familiarity, voice is seen to be popular even with the younger generation.

The modern versions of conversational AI are reinforcing the value of voice by taking away the frictions, leading to improved experience, at least for some use cases. Conversational AI is not new, but the older versions were highly programmed, rigid, and could only answer limited, non-interactive queries. Modern conversational AI relies on probabilistic models, making it highly interactive and capable of closely emulating human agents. This technology has given birth to virtual AI receptionists that handle calls, greet customers, carry out authentication, identify intent, and route calls. This eliminates long queues and grants customers 24/7 access. The value multiplies with real-time translation, allowing AI receptionists to speak multiple languages. This is a game-changer for diverse ethnic groups who do not speak English as a first language, especially when accessing critical local government public services where expressing needs in a native language is vital.

Driven by rapid tech advancements, conversational AI is expanding from basic receptionists into autonomous AI agents. These agents handle tasks like answering queries, booking appointments, and taking orders. To do this, they require robust technical foundations, integrating seamlessly with knowledge bases and CRMs (systems of record) to provide targeted responses. Linked to the right workflows, AI agents can check calendars to schedule medical visits or process restaurant orders. Combined with 24/7 availability and translation, this dramatically elevates the voice experience for customers. Furthermore, non-technical teams can easily build these bots in agent builder studios by simply describing the desired persona and goals, removing the need for complex coding. As 8×8 CEO Samuel Wilson noted at the company’s analyst summit this June, “The next billion voice numbers will go to AI agents.”

Despite the promise, architectural challenges remain. Latency is a primary hurdle; the traditional multi-step process (like speech-to-text) can create latency, leading to awkward pauses that disrupt conversational flow. Robust guardrails are also critical to stop AI from hallucinating false information, which carries severe financial and legal risks. Additionally, backend integrations can be clunky due to poorly documented APIs or failing connectors. Finally, the compute costs for generative (GenAI) and agentic AI remain high and can be prohibitive for many businesses. While agentic conversational AI is still in its developing stages, steady breakthroughs are addressing initial operational hurdles to gradually enhance the contact center voice experience. This ongoing progress will drive deeper adoption of the voice channel, counteracting previous assumptions that voice would decrease in value.

The quest for ever-more automation within customer contact solutions continues apace. There will always be scenarios where human-to-human conversations are the preferred and best option. But to enable companies to offer voice interaction at the scale that customers demand, AI will have to carry a significant share of the burden. GenAI- and agentic AI-powered conversational AI is fundamentally shifting the voice experience in contact centers and customer service.

The post A New Breed of Conversational AI is Leading to Voice Resurgence in Customer Service appeared first on IT Connection.

Chris Arroyo, a regional director at Datadog, said agencies need to start small, build confidence in AI models before moving them into the mission areas.

© Federal News Network

We must build and test these solutions today, so they are ready to deploy when tomorrow’s emergencies strike. Now is the time to prepare for the future.

© The Associated Press

The executive director for financial integrity at the Bureau of Fiscal Service says new data sources are stopping improper payments sooner.

© Federal News Network

The adversaries here are well-resourced nation-states with explicit intent and demonstrated capability to exploit exactly what they steal.

© Getty Images/Urupong

"We'll have about the resolution of Hubble, but we will have 100 times the size of the field of view," said Matthew Bolcar.

© NASA via AP/Joel Kowsky

Summary Bullets:

• As rapid advances in AI application development drive demand for more processing capacity, US-based cloud providers are investing heavily in building out facilities to support these deployments.
• But not everyone is on board with expansion plans, with public criticism stalling some development efforts, forcing hyperscalers to pivot to new locations, often in more remote areas.

AI is changing the cloud landscape, creating the near-term need for a vast increase in processing power and storage space. Hyperscalers are responding with substantial facility construction plans. Just this year alone, Amazon Web Services, Google, and Microsoft Azure are pouring a total of $500 to $700 billion into extensions of their data center footprints. Given AI’s dominance in enterprise technology investment plans, this is a logical track. However, not everyone is on board with these aggressive development plans -and AI plays a role in that resistance.

Fifty-two percent of adults are more worried than excited about AI, according to results from a Pew Research Center survey of 3,488 adults fielded earlier this year. Those queried had trepidations related to AI about everything from job displacement and interference with human creativity to unreliable or even malicious output.

AI anxiety is translating into opposition to in-region data center expansion. A Gallup poll of 1,000 US adults conducted earlier this year, found that 71% of those surveyed are totally opposed to the building of new data center facilities used to support AI applications in their region. By comparison, 53% object to construction of a new nuclear energy plant – a perennially unpopular build in the US for decades.

Participants in the telephone survey cited several concerns related to the new data center expansion, primarily focused on resource consumption, cost, and quality-of-life impacts. Fifty percent said excessive resource requirements associated with these builds in areas like water and energy consumption along with secondary effects such as loss of farmland, wildlife, and deforestation are behind their resistance to facilities’ expansion in their areas. Twenty-two cited concerns about property values and increased traffic. Another 20 percent noted that new data centers might bring higher utility costs and cost of living expenses.

Localities are hearing and responding to this resistance to data center expansion. Due to regional complaints, more than $100 billion in facility buildouts was stopped or disrupted in just one quarter. Over 550 local governments have suspended new facility construction or stopped issuing new permits.

Industry observers warn that impeding expansion could have unintended harmful consequences, including hindering the establishment of effective cyber defenses against hostile adversaries and creating barriers to the development and deployment of technological innovations. But cloud providers have been adept at circumventing obstacles to expansion, finding locations in more remote areas that are more hospitable to new facility construction.

Hyperscalers and other cloud providers are targeting more rural areas in the South and Midwest, and more remote locales in states like Oklahoma, Maine, and Virginia. Nearly half of all new data center builds are in the south, with states like Texas being hot spots.

The post Public Opposition to New Data Centers Disrupts but Doesn’t Derail US Facilities’ Expansion appeared first on IT Connection.

Summary Bullets:

• After a period of ‘token maxxing,’ organizations are looking to reign in and better control inference costs.

• Instead, enterprises are now embracing ‘value-maxxing,’ which focuses on outcomes.
Last week, Google announced several enhancements to Gemini Enterprise designed to help enterprises obtain greater and faster ROI on their AI projects. The improvements address one of the biggest frustrations expressed by organizations today, namely that the benefits promised by AI are taking too long to realize. Companies are clamoring for domain specific solutions in order to speed the deployment, reduce the integration complexity, and increase the value obtained from AI projects. Additionally, business leaders are eager for better tools to help them manage AI costs. They are looking for improved visibility on token use and costs, more proactive spending controls, and more flexible payment options.

On Tuesday, August 25th, Google announced Gemini Enterprise for Financial Services and Gemini Enterprise for Legal. The industry-specific solutions include out-of-the box AI capabilities such as specialized agents that provide shortcuts for directing workflows, data connectors, and sector-optimized models. Reusable packages of instructions teach AI agents to perform specialized tasks that are customized to meet company-specific requirements; connectors link agents to internal systems and data while maintaining access controls; pre-built agents are available to deploy out of the box; and software provider partnerships facilitate industry-specific customization and integration, while avoiding vendor lock-in. Though initially rolled out for the financial services and legal industries, Google plans to offer similar solutions for other industries, including healthcare, life sciences, and professional services.

The following day, August 26th, Google revealed expanded tools for managing AI spending. It announced that Google Antigravity, its AI agent development platform, and Android Studio, for building applications, will now be included in Gemini Enterprise subscriptions. Usage across Antigravity, the platform, and the app rolls up into a single view instead of separate license and billing siloes. Furthermore, Google is providing expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise. Customers can purchase a mix of per-seat subscriptions along with a new pay-as-you-go option, to help avoid hitting token caps in the middle of a job. Companies that commit to a minimum monthly spend will receive discounts on token costs. To better control spending, Google has rolled out new guardrails that enable administrators to set limits on AI spend by project, help estimate agent runtime costs, and identify anomalies in spending. Project level guardrails can pause an agent when API call limits are reached; a FinOps agent provides spending summaries in natural language.

Google’s announcements directly address concerns many organizations have over the spend on AI inference. After a period of ‘token maxxing’ wherein greater token usage was associated with greater productivity, organizations are looking to rein in and better control inference costs. Despite declining token costs, overall consumption, and therefore spend, are skyrocketing. Thus, the industry is now embracing ‘value-maxxing,’ which focuses on outcomes. It seeks to identify and quantify results, whether they be improved performance, more insightful decisions, or greater efficiency.

Regardless of the jargon of the day, organizations are taking a more analytical and practical approach to cost, latency, and performance optimization. No longer is the fastest or most expensive model considered the best choice for all tasks; organizations are now recognizing that some workflows are served well enough by less intensive reasoning, and that the same level of accuracy is not required for all tasks. At the same time, many are considering open-source strategies, attracted to the potential of lower costs, ability to fine tune models, greater transparency, local deployment options, and the option of leveraging existing infrastructure investments. At the end of the day, the development of appropriate AI strategies relies heavily on a broader understanding of the business and its operating model; professionals that can combine this knowledge with technical AI expertise are invaluable.

The post Google’s New Tools Support ‘Value Maxxing’ to Address Organizations’ Growing Concern Over AI ROI and Tokenomics appeared first on IT Connection.

"Most of the AI today in the postal and logistics sectors is used for discrete tasks. It's basically assisting human workers," said Rick Schadelbauer.

© The Associated Press

"One of the big challenges in disaster prep and response is knowing in advance who's at greatest risk of being left behind," said Christopher Marcum.

© Federal News Network

Topics include growth hacking, game design, blockchain, AI, digital marketing, cybersecurity, copywriting, and big data.

The post Enhance Your Expertise Anytime with Unlimited Online Courses — Now $19.97 appeared first on TechRepublic.

Topics include growth hacking, game design, blockchain, AI, digital marketing, cybersecurity, copywriting, and big data.

The post Enhance Your Expertise Anytime with Unlimited Online Courses — Now $19.97 appeared first on TechRepublic.

The Pentagon wants more cost and pricing data transparency and to establish contract profit margins from vendors who provide commercial products and services.

© The Associated Press

CISA said agencies will be able to achieve priority logging capabilities that support continuous event monitoring, threat hunting, incident response and more.

© Amelia Brust/Federal News Network

Greg Garcia, the CTO for Inserso Corp. explains why the use of Commercial Solutions Opening is a good antidote to AI-generated bids.

© Getty Images/iStockphoto/chombosan

Summary Bullets:

• Zayo will build 8,000 miles of new long-haul fiber across key AI corridors, with Nvidia becoming its anchor customer.

• Nvidia’s extends beyond GPUs and compute, with partnerships spanning the optical and networking ecosystem underpinning AI infrastructure.

Until now, the AI Infrastructure race has predominately been focused on GPUs, data centers and access to reliable power, yet beneath all three sits a less visible, but increasingly critical, layer – connectivity. As AI workloads become larger, more distributed and dependent on moving large volumes of data between locations, fiber is emerging as a fundamental component of the AI Stack. Zayo’s recent announcement to build 8,000 miles of new long-haul fiber across key AI corridors, backed by Nvidia as its anchor customer, could mark the new beginning of a new investment cycle for terrestrial networks. The bigger question is whether AI-related companies could become the anchor customer needed for the next generation of fiber investment?

The significance of the Zayo – Nvidia agreement extends well-beyond the fiber being built. Under the model, Zayo will build, own and operate the network, while Nvidia provides the demand certainty needed to underpin this investment. This represents a potentially important shift in how long-haul networks are financed and deployed. Traditionally, service providers have built new routes ahead of demand, invested significant capital and then sought customers to fill the capacity. AI could begin to reverse that model. Securing an anchor customer before construction gives the provider greater visibility over future demand, reduces investment risk, and provides greater confidence that new routes will generate returns. If this model can be replicated with other AI infrastructure providers and hyperscalers, it could unlock future builds that might struggle to secure investment.

The AI chipmaker’s influence on the AI infrastructure ecosystem is extending well beyond GPUs and compute. Its partnership with Corning to expand US optical connectivity production, for example, highlights how AI demand is cascading into the fiber and optical supply chain. Corning plans to increase its US optical connectivity manufacturing by 10 times and fiber production capacity by more than 50%, highlighting the amount of infrastructure required to support AI factories.

Over the last 12 months, Nvidia has made a series of partnerships to strengthen its position across the optical and networking ecosystem. Its partnership with Marvell covers custom AI infrastructure, while its relationship with Lumentum includes advanced optics and laser technology, capacity expansion, and research and development for AI infrastructure. It has also established a partnership with Coherent around advanced optics and optical networking. Together with the Zayo fiber agreement, these relationships point to a broader shift, with the company not only influencing the compute layer of AI infrastructure but also networks, optical components and fiber required to connect it.

This shift could create significant new opportunities for service providers and digital infrastructure providers. As AI workloads become more geographically distributed, demand for high-capacity, low-latency connectivity is likely to grow alongside demand for GPUs and data center capacity. The next phase of the AI infrastructure race may therefore be fought not only within the data centers, but between them. For the wholesale telecom market, the emergence of AI is creating new demand across the connectivity stack, from long-haul and metro fiber to dark fiber, wavelengths, and ethernet services connecting data centers, AI factories, and cloud infrastructure. This presents wholesale providers with an opportunity to monetize existing fiber assets while supporting new network investment in new high-capacity networks purpose-built to support the evolving requirements of AI.

While the Zayo – Nvidia partnership highlights how AI demand could influence not only how much fiber is deployed but also where it is built and how investment is justified. Nvidia may not be becoming a service provider, but its infrastructure requirements are increasingly shaping the connectivity ecosystem. If AI can justify an additional thousand miles of new fiber today, how much further could the network investment cycle go as AI capacity continues to scale?

The post AI Changing the Commercial Model for Fiber Build appeared first on IT Connection.

Summary Bullets:

• AI is drastically changing the fabric of the traditional data center, prompting fundamental changes in design and architecture.

• The biggest challenge is that AI infrastructure requires simultaneous scaling across multiple constrained layers: electricity, cooling, networking, chips, facilities, capital, and operations.

The rise of AI workloads is pushing data centers through a major architectural shift: from relatively general-purpose, virtualized compute environments toward high-density, network-intensive AI infrastructure. For example, rack density is rising sharply, because traditional data centers were not designed for the power and thermal profiles of dense AI server clusters. This means power distribution, floor loading, cable management, and thermal design are becoming central architectural considerations. Power availability has now become a core design constraint. Energy availability is starting to influence where data centers are built, with land and power constraints pushing some infrastructure development into new or remote regions.

Networking architecture is also increasingly important because AI workloads rely on fast, predictable, low-latency networking between servers, storage, models, and cloud regions. Meanwhile, storage architecture must support larger, faster data pipelines. Last but not least, modular AI infrastructure is becoming more attractive. Because demand for AI compute is growing quickly, operators are increasingly looking at modular, pre-engineered AI systems that can be added to existing data centers with less disruption. This helps bridge the gap between legacy data center environments and the need for AI-ready capacity.

It is also worth highlighting that edge and regional AI infrastructure are gaining importance with the rise of latency-sensitive AI applications. Regional inference hubs are emerging to reduce latency, improve resilience, and support data sovereignty requirements, and these hubs increase the need for reliable interconnection with centralized AI models and cloud regions.

All these trends are creating major challenges for companies scaling infrastructure to support high-density AI compute environments. The solution is no longer simply “adding more servers.” As explained above, high-density AI compute changes the whole infrastructure equation across power, cooling, networking, location, economics, and operational resilience.

Firstly, power is the primary bottleneck. Securing enough reliable electricity to support high-density GPU environments can be a major hurdle. Some data center projects in the US and Europe are being canceled because reliable grid connections are hard to find. Secondly, cooling systems must be redesigned. Many legacy facilities are ill-equipped for widespread AI deployment because they lack the infrastructure required for liquid cooling and other advanced cooling systems. New AI data centers need to be designed around advanced cooling from the start, while existing facilities may require retrofits to support AI workloads.

However, retrofitting existing facilities is expensive and disruptive. A large portion of the existing data center estate was built for general-purpose cloud, enterprise workloads, or colocation, not dense GPU clusters. Retrofitting these environments for AI often requires very costly upgrades. This is one reason neoclouds are gaining relevance: traditional cloud environments often cannot provide specialized AI compute quickly enough. Thirdly, site selection is becoming harder. AI growth is changing where data centers are built because energy availability, land constraints, latency requirements, and sustainability considerations increasingly determine site feasibility. Some infrastructure development is being pushed into unusual, sometimes remote regions. Moreover, legislative changes and increasingly, moratoriums like the one seen in New York (US), are hampering data center construction.

The biggest challenge is that AI infrastructure requires simultaneous scaling across multiple constrained layers: electricity, cooling, networking, chips, facilities, capital, and operations. If any layer lags, be it grid access, power equipment, cooling, data center interconnect, GPU availability, or utilization economics, the entire AI compute environment becomes harder to scale. Scaling high-density AI compute is becoming as much an energy, real estate, cooling, and network engineering problem as it is a compute problem.

Neocloud platforms such as CoreWeave, Crusoe, and Lambda Labs are emerging to meet AI infrastructure demand with scalable alternatives tailored for AI developers and high-performance computing. Last but not least, server vendors including Cisco, Dell, HPE, and IBM are designing AI-ready servers with powerful GPUs, accelerators, and machine learning frameworks.

Vendors that can adapt to the need for faster deployment cycles in AI infrastructure environments will emerge victorious. Some are adapting by shifting from bespoke, slow infrastructure builds to pre-integrated, AI-native, modular, automated, and services-led deployment models that reduce time-to-capacity for GPU-heavy environments, while hyperscalers are packaging AI into full-stack services.

The post AI Requires a Reinvention of the Modern Data Center appeared first on IT Connection.

Summary Bullets:

• 8×8 AI Routing identifies the right expert anywhere in an organization that can resolve a customer’s inquiry, not just the contact center.

• While 8×8 AI Routing is marginally better than legacy systems it still merits a try out.

We’ve probably all found ourselves reciting this famous opening line to a classic song when trying to connect with someone in customer support: “Help! I need somebody. Help! Not just anybody. Help! You know I need someone. Help!”

Desperately in need of assistance, eventually you get connected with either a person or an AI agent. You breathe a sigh of relief but then it turns out they are ill-equipped to handle your inquiry. You need help from somebody, but not just anybody. Well, 8×8 claims that its recently introduced ‘8×8 AI Routing’ will take that sad song and make it better.

8×8 AI Routing identifies the right expert for a given interaction anywhere in an organization, not just the contact center. The technology scans three 8×8 platforms, identifying contact center agents on 8×8 Contact Center plus subject matter experts on 8×8 Engage and back-office employees on 8×8 Work. Each interaction is analyzed in real-time across several factors such as transcripts, historical patterns, and sentiment to match the customer with the right resource immediately. 8×8 claims that legacy skills-based routing systems, in contrast to 8×8 AI Routing, are static, relying on skills inventories that are entered manually and often out of date. Furthermore, 8×8 says, those systems often route to whoever is available rather than whoever is necessarily best suited to resolve a specific request.

While 8×8 AI Routing is better than manual, legacy systems the difference is marginal. Identifying the ‘right’ resource hinges on AI conducting an accurate assessment of skills. While AI in general is powerful it is far from perfect. AI is known to produce errors, and the chance that a customer could get routed to the ‘wrong’ resource is not insignificant. While 8×8 AI Routing does allow for a human in the loop, with administrators having the opportunity to provide final sign-off on the skills assessment, such intervention introduces a manual element into the process. And although organizations can roll out first with a small pilot and grow the system as they gain comfort, that adjustment represents yet another layer of manual manipulation. Bottom line, while 8×8 AI Routing is not radically different than legacy systems, it does have the potential to improve an organization’s customer experience and thus merits a test drive.

The post 8×8 AI Routing Takes a Sad Song and Makes It Better appeared first on IT Connection.

Summary Bullets:

• Enterprises are deploying AI agents leveraging Google Cloud’s solutions and achieving positive business outcomes.
• Google Cloud offers the full AI stack, and its sovereign cloud and cyber solutions are especially crucial for enterprise customers.

AI agents are no longer an idea. They are now being deployed by enterprises to improve internal workplace productivity and external customer experience. At Google Cloud Summit Sydney (held on June 25, 2026), more examples of agentic AI in operations were presented, moving from deterministic AI chatbots to more autonomous systems. Bunnings, a home improvement, gardening, and hardware products retailer in Australia, upgraded its Buddy AI chatbot that helped customers with product search to an AI agent that takes customers’ descriptions of their projects and fills the shopping carts with the products that they need. Bunnings indicated an uplift of conversion rates and basket sizes when customers engage with Buddy. Similarly, Woolworths supermarket has an agentic AI powered Olive assistant that is able to build shopping baskets from recipe photos and assist with proactive meal planning. These two examples demonstrate how AI agents trained with proprietary knowledge (e.g., Bunnings’s DIY catalog and Woolworths’ recipe catalog) can deliver greater customer outcomes.

Enterprises deploying AI will appreciate the importance of data. To benefit from AI, it is necessary for enterprises to tap into corporate data to impart knowledge to AI agents. Google Cloud has the advantage in this area since enterprises have been adopting its products such as BigQuery to manage their data more effectively. Moreover, the company has other associated products such as Google Maps, Google Search, and Google Workspace that customers can leverage to enhance their AI capabilities. Transurban, an Australian road operations company and toll road operator, works with Google Cloud to transform its interaction with customers. While customer relationships are mainly transactional, Transurban now leverages Google Cloud’s solutions such as Gemini Enterprise, BigQuery, and Google Maps to power its Linkt app with the “Linkt AI” assistant, which proactively suggests optimal travel routes and toll options, dynamically adjusts schedules for prevailing weather, delivers timely account balance notifications, and offers discounted hotel and attraction bookings for upcoming road trips.

Data is the most valuable asset for enterprises particularly in the age of AI. Many companies across jurisdictions are increasingly concerned about security and sovereignty. Google Cloud offers a set of options for enterprises to meet their data and AI sovereignty requirements. It addresses not just the issue of data residency but also operational sovereignty and software sovereignty. Firstly, Google Cloud Data Boundary helps customers to meet data residency requirements through a set of controls, e.g., regions where data is stored, compliance programs, and external customer or partner managed encryption keys. This option allows enterprises to enjoy the benefits of hosting data in the public cloud for operational flexibility and high availability. Google Cloud is also offering support services with personnel meeting specific geographical locations as well as monitoring capabilities with real-time alerts when organization policy changes violate the defined compliance posture.

For customers that have a more stringent requirement on operational sovereignty, Google Cloud Dedicated addresses the need by enabling solutions to be operated by an independent local partner. The solution is hosted in a standalone, local instance of Google Cloud. The local partner maintains exclusive control over security-critical systems, identity management, authentication, etc. as well as controls over communication between Google and the Google Cloud Dedicated environment. For example, S3NS (a joint venture between Thales and Google Cloud that is headquartered in Paris, France) offers PREMI3NS services built on Google Cloud Dedicated for customers in Europe, now generally available in France. S3NS has achieved SecNumCloud 3.2 qualification from the French National Agency for the Security of Information Systems (ANSSI). Google Cloud Dedicated is also available in Germany (in preview).

For clients with the most stringent sovereignty requirements, Google Distributed Cloud (GDC) air-gapped allows complete isolation, without connectivity to an external network. The solution gives customers the flexibility to use general purpose compute and GPUs, and leverage open-source software. Google Cloud has also made its Gemini available in this air-gapped option, giving customers generative AI capabilities including automation, content generation, discovery and summarization. The GDC air-gapped solution is now deployed by many government agencies including those in Australia and Singapore within the Asia-Pacific region.

Besides sovereignty, Google Cloud has been bolstering its capability to offer stronger cyber defense. This includes the acquisition of Mandiant to add threat intelligence and incident response capabilities as well as Wiz for multi-cloud security defense. At the Google Cloud Summit, the company together with Wiz demonstrated how agentic AI can help to improve protection at scale and speed. Wiz is offering three AI agents with distinct roles: the Red Agent helps to uncover vulnerabilities and validate exploitable risks across web applications and APIs; the Blue Agent is the threat investigator that gathers evidence across cloud telemetry, runtime signals, and identity context to assess the severity of a threat and allow threats to be resolved more proactively; and the Green Agent is the investigation and remediation engine, identifying the root cause of a risk and the safest and most effective resolution. Wiz is known to provide security for cloud-native applications across major cloud environments including AWS, Microsoft Azure, Google Cloud, and Oracle. Following the completed acquisition on March 11, 2026, Wiz joins Google Cloud but operates independently to maintain its brand and key value proposition.

Google Cloud offers the full AI stack including applications and agents, AI models, data platforms, and infrastructure. It has also demonstrated strong momentum through broad customer references. However, the ability to drive AI adoption ultimately lies with its partner ecosystem and its willingness to support third-party products (including AI models) and help customers operate within a multi-cloud environment. Consulting partners such as Accenture and Mantel Group were featured at the Google Cloud event, and these partners play a crucial role in helping enterprises develop their business strategy around AI and implement solutions addressing data, security, governance, and other technology challenges.

The post Google Cloud Summit Sydney: Putting Agentic AI into Action appeared first on IT Connection.

Summary Bullets:

• Boomi is evolving from an iPaaS into an enterprise platform combining integration, automation, API management, data management, and AI agent governance.

• GlobalData recently attended Boomi’s World Tour London 2026, where agentic AI was discussed at length around announcements including Boomi Connect, Boomi Orchestrate, and Boomi Companion.

Although Boomi has historically been best known as an integration platform as a service, or iPaaS, the company is going to great lengths to emphasize that it has evolved into an enterprise platform which activates data and workflows for customers and combines integration, automation, API management, data management, and AI-agent governance. The Boomi platform acts as the connective and orchestration layer between an organization’s applications, data, and AI systems, but is increasingly moving towards management of AI agents to help data enhance business processes.

The company is investing significant resources in its Runtime environment, a hybrid platform that customers can install either locally or in a virtual private cloud. Integration is an area of major focus for the vendor, with plans to bring all business workloads into Runtime, including workflows, AI agents, and eventually small language models. Boomi has a partnership with Red Hat to help manage the deployment of open-weight and proprietary models.

Boomi is not tied to a particular ecosystem, in the vein of providers such as ServiceNow; rather, it offers an independent API control pane across all ecosystems. For example, it can connect Salesforce with SAP, synchronize customer records, automate an order-to-cash process, expose the process as an API, send transactions to suppliers through EDI, and now help AI agents replicate the same process.

The company is expanding not just beyond integration but across all platform services including investments in data readiness with Meta Hub, with strong traction among customers thanks to the growing popularity of data management systems. Currently in preview, the Knowledge Hub will also allow customers to bring unstructured data into the platform.

In the realm of agentic solutions, Boomi has recently announced new products including Boomi Connect, Boomi Orchestrate, and Boomi Companion:

• Boomi Connect offers the governance layer, sitting between the AI layer and the enterprise applications in the customer organization, with secure access, tool scoping, and observability, connecting and integrating MCP and governance by creating a single stack for CSOs. It establishes secure connections between AI tools (Claude, Copilot, and Gemini) and enterprise applications thanks to more than 1,000 MCP-enabled tools.

• Boomi Orchestrate brings IT, business applications and agents together to solve complex problems by creating blueprints allowing customer teams to build solutions. While agentic platforms are quickly becoming commoditized, Boomi has focused on delivering its proprietary offering in a way that meets customer requirements.

• Boomi Companion enables existing AI tools to design, build, test, deploy, and diagnose Boomi integrations in natural language. It transforms prompts into integrations in any language the AI agent supports and offers AI-assisted development with Claude Code, Cursor, and more via open-source Agent Skills standard.
Boomi is also launching Boomi Prompt, although the release date is yet to be determined. Boomi Prompt will form a layer of intelligent routing that is not limited to routing the LLM but will route, sort out, and separate deterministic from probabilistic workflows so users do not waste tokens. This promises, when it comes out, to be a strong portfolio addition to help customers mindful of “tokenomics”, the management of costs driven by AI consumption.

Agent Studio, introduced last May, is an agent management platform for customers who have been undertaking integration processes for 20 years and do not want to start over with agents and replace everything. These types of enterprises are looking for slightly augmented intelligence to leverage agents for fixing things that are not working. Rather than starting over, they favor an integration process for testing applications. Customers are used to deterministic workflows and the Agent Studio offers an integration workflow in an structured form.

Boomi is focusing on the right objectives. In terms of governance, the company is in a solid position to benefit from a significant opportunity in the next two years as token budgets get out of control to help people to get back into deterministic processes and bring non-deterministic workflows back under control.

The caveat is that the portfolio is becoming very broad. Integration remains Boomi’s most mature and recognizable capability, while advanced data engineering, enterprise knowledge and agent-management products are newer expansion areas. Many customers may still look at the Boomi platform primarily as an enterprise connectivity and orchestration platform, but its advancements in agentic AI management should not be overlooked.

The post Boomi Targets Agentic AI Governance, but Orchestration Remains Its Raison d’Etre appeared first on IT Connection.