vFunction, the pioneer of AI-driven architectural observability and modernization, is bringing its architectural context to any GenAI assistant, including native integrations with Amazon Q Developer and GitHub Copilot to guide developers through automated architectural modernization and GenAI-powered service transformation. vFunction’s GenAI is enriched with deep architectural knowledge that is aware of semantic structures like context, components, and logical domains, enabling code assistants to address system-wide architectural challenges with complete architectural awareness, rather than just isolated code modifications. By bringing architectural intelligence into developers’ workflows, vFunction accelerates application modernization, helping organizations move beyond lift-and-shift to fully maximize their cloud investments. “With these new advancements, teams can surface and resolve architectural debt, and transform their apps to cloud-native, with unprecedented speed through autonomous modernization,” said Amir Rapson, CTO and co-founder of vFunction. “From eliminating circular dependencies to refactoring ‘god classes’, developers can now simplify refactoring and modernization, accelerate delivery, and optimize architecture for the cloud.” One of the ways vFunction is addressing GenAI-based refactoring is with its new MCP server, connecting vFunction’s architectural observability engine with modern developer environments. It enables developers to query architectural issues, generate GenAI prompts, and kick off remediation—all from the command line. With optimized support for Amazon Q Developer and GitHub Copilot, developers can use their preferred assistants to resolve architectural issues using prompts enriched with real-time architectural data. This closes the divide between architects and developers, making the architectural vision executable within native workflows.
RegASK agentic AI architecture pairs domain-specific vertical LLM with specialized AI agents who perform distinct tasks, are coordinated by a ‘project manager’ agent and their outputs reviewed by an evaluator agent to deliver personalized insights for day-to-day compliance operations
RegASK, a provider of AI-driven regulatory intelligence for Consumer Goods and Life Sciences, has launched the industry’s first agentic AI architecture that pairs RegASK’s vertical large language model (V-LLM) with specialized AI agents to deliver personalized insights and streamline how teams find, understand, and act on regulatory information. These agents each perform a distinct task, such as document retrieval, translation, summarization, and assessment generation. These agents are coordinated by a dedicated ‘project manager’ agent that manages how tasks are assigned and performed across the system, enabling collaborative execution of multi-step workflows. An evaluator agent reviews outputs before they’re delivered to users, helping ensure accuracy and build trust in the results. Together, the enhanced agent network and embedded V-LLM power deeper automation, more tailored insights, and the ability to manage a wider range of day-to-day compliance operations. The launch also brings: A more powerful, embedded vertical language model: RegASK’s domain-specific LLM is now fully integrated into the platform and enhanced with additional structured attributes. The model gives agents deeper context to generate faster, more precise summaries, assessments, and search results, delivering insights that are directly aligned to users’ regulatory priorities. Redesigned user interface with streamlined regulatory change tracking: RegASK’s redesigned user experience significantly improves how regulatory teams identify and respond to critical updates. The new alerts module delivers customizable alert views, streamlined navigation, and faster access to essential regulatory details, enabling professionals to efficiently manage compliance workflows, mitigate risk proactively, and keep their organizations ahead in highly regulated environments.
Blok’s AI agents aim to eliminate friction points in software testing by simulating the behavior of human users and identifying their likes and dislikes using a combination of behavioral science and product data
A startup called Blok Intelligence Inc. has raised $7.5 million to transform the software testing process with AI agents that simulate the behavior of human users. Blok has developed AI agents that are grounded in a combination of behavioral science and product data to try to simulate how different types of people use software. That way, developers can identify the most useful features and uncover and eliminate any friction points in their applications. It’s aiming to transform the software testing process, which often takes weeks, and condense it into a matter of hours. According to the startup, the capabilities its AI agents provide are needed more than ever, given the surging popularity of “vibe coding,” which has led to a flood of new digital products, but the challenge is that many of these new applications aren’t giving people what they want. Blok gets around that with AI agents that behave like humans. It says they’re curious, imperfect and full of nuance, just like people are. By grounding them in the “messy realities” of human decision making, they’re better able to identify what humans will like and dislike about new software products. Co-founder and Chief Executive Tom Charman said he thinks static, one-size-fits-all software products are soon going to become obsolete, replaced by tools that are more adaptive and responsive to each user’s needs. But developers need help to understand what those needs are.
ZeroEntropy is a RAG based AI search tool strictly for developers that grabs data, even across messy internal documents and grabbing the most relevant information first
Startup ZeroEntropy joins a growing wave of infrastructure companies hoping to use retrieval-augmented generation (RAG) to power search for the next generation of AI agents. ZeroEntropy offers an API that manages ingestion, indexing, re-ranking, and evaluation. What that means is that — unlike a search product for enterprise employees like Glean — ZeroEntropy is strictly a developer tool. It quickly grabs data, even across messy internal documents. Houir Alami likens her startup to a “Supabase for search,” referring to the popular open source database that automates much of the database management. At its core is its proprietary re-ranker called ze-rank-1, which the company claims currently outperforms similar models from Cohere and Salesforce on both public and private retrieval benchmarks. It makes sure that when an AI system looks for answers in a knowledge base, it grabs the most relevant information first. “Right now, most teams are either stitching together existing tools from the market or dumping their entire knowledge base into an LLM’s context window. The first approach is time-consuming to build and maintain,” CEO Ghita Houir Alami said. “The second approach can cause compounding errors. We’re building a developer-first search infrastructure — think of it like a Supabase for search — designed to make deploying accurate, fast retrieval systems easy and efficient.”
DigitalOcean’s managed AI platform offers one simple UI with integrations for storage, functions, and database to build AI agents that can reduce costs or streamline user experiences—without requiring deep AI expertise on their team
DigitalOcean Holdings, announced the general availability of its DigitalOcean GradientAI™ Platform, a managed AI platform that enables developers to combine their data with foundation models from Anthropic, Meta, Mistral and OpenAI to add customized GenAI agents to their applications. The DigitalOcean GradientAI Platform is a fully managed service where customers do not need to manage infrastructure and can deploy Generative AI capabilities in minutes to their applications. With the DigitalOcean GradientAI Platform, all tools and data are available through one simple UI with integrations for storage, functions, and database all powered by DigitalOcean’s GPU cloud. This empowers customers to build AI agents that can reduce costs or streamline user experiences—without requiring deep AI expertise on their team. The DigitalOcean GradientAI Platform is built with simplicity in mind to get GenAI-backed experiences into customer applications quickly. By leveraging retrieval augmented generation (RAG), customers can quickly and easily create GenAI agents for use within their applications. These agents offer powerful capabilities that can be enhanced through function routing to integrate with third-party APIs, and agent routing to connect with other GenAI Agents within the platform. Additionally, with Serverless LLM Inference, customers can integrate models from multiple providers via one API, with usage-based billing and no infrastructure to manage.
Contify’s agentic AI delivers trusted, decision-ready market and competitor insights by continuously analyzing unstructured updates from millions of verified external and internal sources and connecting information through Knowledge Graph
AI-native Market and Competitive Intelligence (M&CI) platform Contify, launched Athena, its proprietary Agentic AI insights engine. Athena eliminates manual M&CI work and delivers trusted, decision-ready market and competitor insights, with enterprise-grade accuracy. It enables organizations to make faster, more confident decisions and compete with greater agility. Unlike generic AI assistants like ChatGPT, Gemini, or Perplexity, which often hallucinate, respond from unverified web content, and even fabricate sources, making them unsuitable for enterprise use, Athena is built on a foundation of data integrity coupled with strict AI-usage guardrails. It continuously analyzes unstructured updates from millions of verified external and internal sources. It synthesizes what matters and connects information through a proprietary Knowledge Graph, which stores the organization’s context, to produce reliable insights. Mohit Bhakuni, CEO of Contify says “Intelligence professionals are often stretched thin, grappling with overwhelming data, frequent stakeholder requests, and generic AI assistant limitations. Athena transforms this by automating grunt work and providing rich, verified insights. It frees them to focus on strategic priorities and become trusted advisors their organizations rely on.”
New Liquid Foundation Models can be deployed on edge devices without the need for extended infrastructure of connected systems and are superior to transformer-based LLMs on cost, performance and operational efficiency
If you can simply run operations locally on a hardware device, that creates all kinds of efficiencies, including some related to energy consumption and fighting climate change. Enter the rise of new Liquid Foundation Models, which innovate from a traditional transformer-based LLM design, to something else. The new LFM models already boast superior performance to other transformer-based ones of comparable size such as Meta’s Llama 3.1-8B and Microsoft’s Phi-3.5 3.8B. The models are engineered to be competitive not only on raw performance benchmarks but also in terms of operational efficiency, making them ideal for a variety of use cases, from enterprise-level applications specifically in the fields of financial services, biotechnology, and consumer electronics, to deployment on edge devices. These post-transformer models can be used on devices, cars, drones, and planes, and applications to predictive finance and predictive healthcare. LFMs, he said, can do the job of a GPT, running locally on devices. If they’re running off-line on a device, you don’t need the extended infrastructure of connected systems. You don’t need a data center or cloud services, or any of that. In essence, these systems can be low-cost, high-performance, and that’s just one aspect of how people talk about applying a “Moore’s law” concept to AI. It means systems are getting cheaper, more versatile, and easier to manage – quickly.
TNG Technology Consulting’s adaptation of DeepSeek’s open-source model R1-0528 is 200% faster, scores at upwards of 90% of R1-0528’s intelligence benchmark scores, and generates answers with < 40% of R1-0528’s output token count
DeepSeek’s latest version of its hit open source model DeepSeek, R1-0528 is already being adapted and remixed by other AI labs and developers, thanks in large part to its permissive Apache 2.0 license. German firm TNG Technology Consulting GmbH released one such adaptation: DeepSeek-TNG R1T2 Chimera, the latest model in its Chimera large language model (LLM) family. R1T2 delivers a notable boost in efficiency and speed, scoring at upwards of 90% of R1-0528’s intelligence benchmark scores, while generating answers with less than 40% of R1-0528’s output token count. That means it produces shorter responses, translating directly into faster inference and lower compute costs. This gain is made possible by TNG’s Assembly-of-Experts (AoE) method — a technique for building LLMs by selectively merging the weight tensors (internal parameters) from multiple pre-trained models. R1T2 is constructed without further fine-tuning or retraining. It inherits the reasoning strength of R1-0528, the structured thought patterns of R1, and the concise, instruction-oriented behavior of V3-0324 — delivering a more efficient, yet capable model for enterprise and research use.
Dust helps enterprises build AI agents capable of taking real actions across business systems and secures sensitive information by separating data access rights from agent usage rights
AI platform Dust helps enterprises build AI agents capable of completing entire business workflows, has reached $6 million in annual revenue — a six-fold increase from $1 million just one year ago. The company’s rapid growth signals a shift in enterprise AI adoption from simple chatbots toward sophisticated systems that can take concrete actions across business applications. The startup has been selected as part of Anthropic’s “Powered by Claude” ecosystem, highlighting a new category of AI companies building specialized enterprise tools on top of frontier language models rather than developing their own AI systems from scratch. Instead of simply answering questions, Dust’s AI agents can automatically create GitHub issues, schedule calendar meetings, update customer records, and even push code reviews based on internal coding standards–all while maintaining enterprise-grade security protocols. The shift toward AI agents that can take real actions across business systems introduces new security complexities that didn’t exist with simple chatbot implementations. Dust addresses this through a “native permissioning layer” that separates data access rights from agent usage rights. The company implements enterprise-grade infrastructure with Anthropic’s Zero Data Retention policies, ensuring that sensitive business information processed by AI agents isn’t stored by the model provider.
Agent2.Ain’s AI agent can instantly turn complex research tasks into usable outputs in multi-formats like structured spreadsheets and presentation slides through a transparent, step-by-step breakdowns of how it searched, evaluated sources, and reached conclusions
Agent2.Ain has launched Super Agent, a powerful new AI tool designed to help users tackle complex research tasks and instantly turn them into usable outputs—like structured spreadsheets, presentation slides, and more. What sets Super Agent apart is its open process. Every step in its reasoning is visible—users can review, edit, or guide the workflow in real time. Behind the scenes, multiple AI models collaborate on each task. The system compares their outputs, refines them, and delivers a final version that reflects stronger reasoning from multiple angles. Super Agent fits into existing workflows with support for formats like Excel, PowerPoint, Docs, Markdown, and more. And when deeper context is needed, users can securely log in to enterprise tools within a virtual machine, allowing the agent to factor in private business data alongside public research. The Agent2.AI Super Agent is designed to take a prompt and deliver usable results across multiple formats and tools. Some examples of what it can do include: Deep Research: Transparent, step-by-step breakdowns of how the agent searched, evaluated sources, and reached its conclusions;
AI Sheets: Structured spreadsheets that organize research findings, metrics, and summaries. Exportable with one click; AI Slides: Presentation decks built from research or reports, complete with titles, visuals, and speaker notes; Other Outputs: From timelines and tables to emails and internal docs, the agent adapts its output based on what the user needs.