Sovos, announced the launch of Sovi™ AI, a first-of-its-kind suite of embedded AI and machine learning capabilities purpose-built for tax compliance. Sovi symbolizes smart power in action, a perfect reflection of Sovos’ embedded AI engine that drives a whole panorama of intelligent automation across the Sovos Tax Compliance Cloud platform. Sovi delivers unprecedented insight, automation, and reliability throughout every stage of compliance for e-invoicing, taxation and regulatory reporting. Sovi AI will integrate across analytics, automation, and regulatory workflows, enabling technical and non-technical teams to navigate complexity through natural language, visual interfaces, and intuitive guidance. Sovi AI capabilities are already operational across Sovos solutions, including advanced biometrics for face and liveness detection, image recognition, and secure authentication built into Sovos Trust solutions. The roadmap includes ambitious expansions such as AI compliance checks, Ask Sovi for embedded assistants, automated mapping tools for goods and services classification, and intelligent document agents for AP process automation. Sovi AI enables organizations to achieve: Enhanced Efficiency: Self-service analytics eliminate IT dependencies for finance and tax teams; Improved Accuracy: Biometric security and AI validations reduce errors, fraud, and compliance mismatches; Greater Clarity: Conversational AI and insightful dashboards uncover hidden issues and opportunities; Unlimited Scalability: Future-proof compliance capabilities regardless of country, volume, or complexity.
Amazon SageMaker HyperPod’s observability solution offers a comprehensive dashboard that provides insights into foundation model (FM) development tasks and cluster resources by consolidating health and performance data from various sources
Amazon SageMaker HyperPod offers a comprehensive dashboard that provides insights into foundation model (FM) development tasks and cluster resources. This unified observability solution automatically publishes key metrics to Amazon Managed Service for Prometheus and visualizes them in Amazon Managed Grafana dashboards. The dashboard consolidates health and performance data from various sources, including NVIDIA DCGM, instance-level Kubernetes node exporters, Elastic Fabric Adapter (EFA), integrated file systems, Kubernetes APIs, Kueue, and SageMaker HyperPod task operators. The solution also abstracts management of collector agents and scrapers across clusters, offering automatic scalability of collectors across nodes as the cluster grows. The dashboards feature intuitive navigation across metrics and visualizations, helping users diagnose problems and take action faster. These capabilities save teams valuable time and resources during FM development, helping accelerate time-to-market and reduce the cost of generative AI innovations. To enable SageMaker HyperPod observability, users need to enable AWS IAM Identity Center and create a user in the IAM Identity Center.
Amazon Web Services is launching a dedicated AI agent marketplace to enable startups to directly offer their AI agents to AWS customers while also letting enterprises to browse and install AI agents based on their requirements from a central location
Amazon Web Services (AWS) is launching an AI agent marketplace next week and Anthropic is one of its partners at the AWS Summit in New York City on July 15. The distribution of AI agents poses a challenge, as most companies offer them in silos. AWS appears to be taking a step to address this with its new move. The company’s dedicated agent marketplace will allow startups to directly offer their AI agents to AWS customers. The marketplace will also allow enterprise customers to browse, install, and look for AI agents based on their requirements from a single location. That could give Anthropic — and other AWS agent marketplace partners — a considerable boost. AWS’ marketplace would help Anthropic reach more customers, including those who may already use AI agents from its rivals, such as OpenAI. Anthropic’s involvement in the marketplace could also attract more developers to use its API to create more agents, and eventually increase its revenues. The marketplace model will allow startups to charge customers for agents. The structure is similar to how a marketplace might price SaaS offerings rather than bundling them into broader services.
Docker’s new capabilities enable developers to define agents, models, and tools as services in a single Compose file and share and deploy agentic stacks across environments without rewriting infrastructure code
Docker announced major new capabilities that make it dramatically easier for developers to build, run, and scale intelligent, agentic applications. Docker is extending Compose into the agent era, enabling developers to define intelligent agent architectures consisting of models and tools in the same simple YAML files they already use for microservices and take those agents to production. With the new Compose capabilities, developers can: Define agents, models, and tools as services in a single Compose file; Run agentic workloads locally or deploy seamlessly to cloud services like Google Cloud Run or Azure Container Apps; Integrate with Docker’s open source Model Context Protocol (MCP) Gateway for secure tool discovery and communication; Share, version, and deploy agentic stacks across environments without rewriting infrastructure code. Docker unveiled Docker Offload (Beta), a new capability that enables developers to offload AI and GPU-intensive workloads to the cloud without disrupting their existing workflows. With Docker Offload, developers can: Maintain local development speed while accessing cloud-scale compute and GPUs; Run large models and multi-agent systems in high-performance cloud environments; Choose where and when to offload workloads for privacy, cost, and performance optimization; Keep data and workloads within specific regions to meet sovereignty requirements and ensure data does not leave designated zones across the globe.
NinjaTech AI general purpose AI agent handles entire workflows from start to finish autonomously and allows users to implement complex tasks such as coding and testing an entire application 3-5x faster than GPU-based solutions
NinjaTech AI, an agentic AI company announced: Super Agent: A revolutionary all-in-one General Purpose AI Agent with a dedicated virtual machine that plans, iterates, and executes entire workflows from start to finish in minutes. What sets Super Agent apart is its ability to handle entire workflows from start to finish. Unlike conventional AI tools limited by token limits or requiring constant hand-holding, Super Agent operates on its own dedicated computer in the same way humans do—running extensive data analysis, coding and validating full applications, conducting comprehensive research, building websites, and delivering high-quality results in the user’s preferred format. Each user gets their own isolated VM, ensuring complete data privacy and security. This enables Super Agent to download tools, write and execute code, create applications, analyze data, and build websites or dashboards autonomously—all within a secure environment that’s not shared with other users. Coming soon, Super Agent will also include a virtual smartphone capability, allowing it to interact with mobile applications on the user’s behalf. Central to Super Agent’s capabilities is NinjaTech AI’s strategic partnership with Cerebras Systems, pioneers in fast inference. This strategic collaboration utilizes Cerebras’ wafer-scale architecture, allowing users to implement complex tasks such as coding and testing an entire application 3-5x faster than GPU-based solutions.
KPMG survey finds AI agents are moving into production with 33% of organizations now deploying AI agents, a 3X increase from just 11% in the previous two quarters
Companies like Intuit, Capital One, LinkedIn, Stanford University and Highmark Health are quietly putting AI agents into production, tackling concrete problems, and seeing tangible returns. Here are the four biggest takeaways: 1) AI Agents are moving into production, faster than anyone realized A KPMG survey released on June 26, a day after our event, shows that 33% of organizations are now deploying AI agents, a surprising threefold increase from just 11% in the previous two quarters. Intuit, for instance, has deployed invoice generation and reminder agents in its QuickBooks software. Businesses using the feature are getting paid five days faster and are 10% more likely to be paid in full. Even non-developers are feeling the shift, building production-ready software features with power of tools like Claude Code. 2) The hyperscaler race has no clear winner as multi-cloud, multi-model reigns Enterprises want the flexibility to choose the best tool for the job, whether it’s a powerful proprietary model or a fine-tuned open-source alternative. This trend is creating a powerful but constrained ecosystem, where GPUs and the power needed to generate tokens are in limited supply. 3) Enterprises are focused on solving real problems, not chasing AGI Highmark Health Chief Data Officer Richard Clarke said it is using LLMs for practical applications like multilingual communication to better serve their diverse customer base, and streamlining medical claims. Similarly, Capital One is building teams of agents that mirror the functions of the company, with specific agents for tasks like risk evaluation and auditing, including helping their car dealership clients connect customers with the right loans. 4) The future of AI teams is small, nimble, and empowered Small team structure allows for rapid testing of product hypotheses and avoids the slowdown that plagues larger groups. As GitHub and Atlassian noted, engineers are now learning to manage fleets of agents. The skills required are evolving, with a greater emphasis on clear communication and strategic thinking to guide these autonomous systems. This nimbleness is supported by a growing acceptance of sandboxed development. The idea is to foster rapid innovation within a controlled environment to prove value quickly.
Akka’s agentic platform makes it easier to orchestrate teams of AI agents and manage their memory and streaming processes, with support for sequential, parallel, hierarchical and human-in-the-loop workflows
Distributed application development platform Akka, officially known as Lightbend is launching new Akka Agentic Platform that makes it easier to orchestrate teams of AI agents and manage their memory and streaming processes. Akka Agentic Platform features a range of new capabilities that aim to make the behavior of AI agents a bit more predictable. For instance, there’s an Akka Orchestration feature that helps developer teams to guide, moderate and control multi-agent systems, with support for sequential, parallel, hierarchical and human-in-the-loop workflows. Meanwhile, Akka Agents makes it possible to develop goal-directed AI agents and Model Context Protocol-based tools that can reason, act and analyze, and integrate with third-party agentic systems and applications. Another key component is Akka Memory, which allows developers to establish a durable, in-memory resource to aid with AI agent context, history retention and personalized behavior. It supports nanosecond writes and is designed to act as both long- and short-term memory, with replication features in case of any system failures. Finally, the platform includes an Akka Streaming capability for stream processing ambient, adaptive and real-time AI agents capable of continuously processing, aggregating and augmenting live data, video, audio and metrics. These data streams can be ingested from any source and fed into AI agents in real time, keeping them up to date with the world around them. The combination of these capabilities will bring “complete certainty” to AI agents, so they can achieve the required level of accuracy, safety, availability and recovery that’s needed to deploy them in production.
AWS launches Kiro: a ‘spec coding’ developer environment integrated with AI agents that will build in “specs” and use “hooks” that will understand the width and breadth of taking a prototype to production
AWS launched a preview of a new development environment named Kiro, integrated with AI agents for software engineers, which will help them turn ideas into production-ready code. Now in preview, Kiro helps provide speed and resilience to has become known as “vibe coding,” a new way to use development tools to tell an AI assistant what the developer wants built using conversational English and then working with it like a pair programmer or sitting back and letting it do most of the work. Amazon’s newest tool is an integrated development environment, or an IDE, which is a software development interface where software engineers spend most of their time building, coding, testing and compiling software. Traditionally, the experience of vibe coding might start from a blank template or an existing app where a coder prompts the AI to write something. Then they prompt it again to either write more or fix what it wrote. This chain of prompts eventually leads to a final product. Amazon said Kiro will change that with integrated AI agents that will build in “specs” and use “hooks” that will understand the width and breadth of taking a prototype to production. As a result, Amazon calls what Kiro’s new capability “spec coding.” The important thing about this approach is that the code and the agent’s process are completely documented top-to-bottom. Nothing is left out and the developer has a bird’s eye view of how the app or function will be built and is able to guide it from the requirements view before anything happens. Amazon said this eliminates the costly back-and-forth usually associated with vibe coding.
Wolters Kluwer unveils cloud-native and blockchain based bank confirmation platform that enables 100% bank statement retrieval from banks worldwide and entirely online experience and self-serve through a browser-based interface
Wolters Kluwer Tax and Accounting (TAA) unveiled the enhanced CCH Validate, next-generation, cloud-native bank confirmation platform engineered to redefine how audit professionals operate. Purpose-built for speed, security, and intelligence, this fully online solution empowers firms with instant, client-authorized data access and intuitive onboarding—delivering a smarter, faster, and more reliable audit experience. Leveraging advanced technologies—including blockchain for tamper-proof assurance—it enables 100% bank statement retrieval from banks worldwide. Key Features of the New CCH Validate: Entirely Online Experience: Users can self-serve through a browser-based interface and begin using the tool in minutes. Client-Driven Authorization: Clients securely and privately authorize data access, removing the need to wait for bank responses. Trial First: Firms can try the platform before committing. Cloud-Native Architecture: Built for scalability, security, and speed, with compliance to SOC 1, SOC 2, ISO 27001, and audit industry standards. Global Reach: Currently designed to support firms in the U.S., integration across platforms in Canada and the UK will soon follow. Security: Blockchain is leveraged to ensure requests are tamper-proof Audit Trail: Provides a comprehensive and immutable audit trail of the end-to-end process stored in the blockchain. Fraud Prevention: Eliminates the potential for fraud by eliminating human intermediaries in responding to requests.
Liquid AI’s platform offers developers curated catalog of small language models (SLMs) which includes compact models as small as 300MB with various quantization and checkpoint options to simplify deployment on edge devices
Liquid AI has launched LEAP aka the “Liquid Edge AI Platform,” a cross-platform SDK designed to make it easier for developers to integrate small language models (SLMs) directly into mobile applications. The SDK can be added to an iOS or Android project with just a few lines of code, and calling a local model is meant to feel as familiar as interacting with a traditional cloud API. Once integrated, developers can select a model from the built-in LEAP model library, which includes compact models as small as 300MB — lightweight enough for modern phones with as little as 4GB of RAM and up. The SDK handles local inference, memory optimization, and device compatibility, simplifying the typical edge deployment process. LEAP is OS- and model-agnostic by design. At launch, it supports both iOS and Android, and offers compatibility with Liquid AI’s own Liquid Foundation Models (LFMs) as well as many popular open-source small models. Developers can browse a curated model catalog with various quantization and checkpoint options, allowing them to tailor performance and memory footprint to the constraints of the target device. To complement LEAP, Liquid AI also released Apollo, a free iOS app that lets developers and users interact with LEAP-compatible models in a local, offline setting. Apollo is designed for low-friction experimentation — developers can “vibe check” a model’s tone, latency, or output behavior right on their phones before integrating it into a production app. The app runs entirely offline, preserving user privacy and reducing reliance on cloud compute.