OpenAI announced what it’s calling the first “connector” for ChatGPT deep research, the company’s tool that searches across the web and other sources to compile thorough research reports on a topic. Now, ChatGPT deep research can link to GitHub (in beta), allowing developers to ask questions about a codebase and engineering documents. The connector will be available for ChatGPT Plus, Pro, and Team users over the next few days, with Enterprise and Edu support coming soon. The GitHub connector for ChatGPT deep research arrives as AI companies look to make their AI-powered chatbots more useful by building ways to link them to outside platforms and services. Anthropic, for example, recently debuted Integrations, which gives apps a pipeline into its AI chatbot Claude. In addition to answering questions about codebases, the new ChatGPT deep research GitHub connector lets ChatGPT users break down product specs into technical tasks and dependencies, summarize code structure and patterns, and understand how to implement new APIs using real code examples. The company also launched fine-tuning options for developers looking to customize its newer models for particular applications. Devs can now fine-tune OpenAI’s o4-mini “reasoning” model via a technique OpenAI calls reinforcement fine-tuning, which uses task-specific grading to improve the model’s performance. Fine-tuning has also rolled out for the company’s GPT-4.1 nano model.
kama.ai’s supports knowledge management with hybrid agents informed by Knowledge Graph AI, enterprise RAG tech and a Trusted Collection
kama.ai, a leader in responsible conversational AI solutions, announced the commercial release of the industry’s most trustworthy AI Agents powered by GenAI’s Sober Second Mind®, the latest addition to its Designed Experiential Intelligence® platform – Release 4. The new Hybrid AI Agents combine kama.ai’s classic knowledge base AI, guided by human values, with a new enterprise Retrieval Augmented Generation (RAG) process. This in turn is powered by a Trusted Collection feature set that produces the most reliable and accurate generative responses. The Trusted Collection features provide pre-integrated intentional document and collection management with enterprise document repositories like SharePoint, M-Files and AWS S3 Buckets. Designed Experiential Intelligence® Release 4 helps enterprise experts work faster with greater ease. It generates draft responses automatically for a Knowledge Manager or SME to review. This is needed for highly sensitive applications (like HR), or for high volume customer facing applications. User inquiries, feedback, and AI drafts all help improve the system. Together, consumers, clients, partners, and SMEs create a more efficient and effective human-AI ecosystem. kama.ai Release 4 also introduces a new API supporting 3rd party Hybrid AI Agent builders that can deliver 100% accurate and approved information curated for the enterprise.
Zencoder’s platform offers software teams access to third-party registries that host ready-to-use MCP connectors and MCP-powered pre-packaged AI agent integrations to enable them to build their own custom AI agents
Startup Zencoder, officially For Good AI Inc., introduced a cloud platform called Zen Agents that can be used to create coding-optimized AI agents. The new Zen Agents platform has two main components. The first is a catalog of open-source AI agents that can automate more than a half dozen programming tasks. The platform’s other component, in turn, is a tool that allows software teams to build their own custom AI agents. Developers can create an AI agent by entering a natural language description of the tasks it should perform. Zen Agents provides a collection of prepackaged AI agent integrations powered by MCP. The platform also offers access to third-party registries, or cloud services that host ready-to-use MCP connectors. The company says AI agents powered by its platform can create documentation that explains developers’ code, as well as generate new code in multiple programming languages. Software teams can also deploy AI agents that automatically test application updates for bugs. Zencoder has developed a technology it calls Repo Grokking to improve AI-generated code. It maps out the structure of an application’s code base, including details such as the programming best practices that the application’s developers follow. This information allows the AI models that power its platform to generate more relevant programming suggestions.
Franz’s Natural Language Query interface builds agentic AI that can understand user intent, can reason over complex data, and take meaningful action through built-in GraphRAG capabilities
Franz Inc., an early innovator in AI and supplier of Graph Database technology for Neuro-Symbolic AI Solutions, has announced AllegroGraph v8.4, with an Enhanced AI-powered Natural Language Query interface AllegroGraph’s advanced natural language queries drive Agentic AI solutions by enabling more intuitive, human-like interaction between users and intelligent systems—critical for agents that need to reason, plan, and act autonomously. Dr. Jans Aasman, CEO of Franz Inc. “This latest release makes it easier for enterprises to build intelligent agents that can understand user intent, reason over complex data, and take meaningful action—bringing us closer to truly autonomous, explainable AI systems.” AllegroGraph v8.4 has enhanced its Natural Language Query interface to allow users to ask questions in natural language and automatically converts them into SPARQL queries for precise Knowledge Graph interrogation. This AI-powered capability depends on the platform’s vector database which contains query examples that help the system learn and improve over time. With this feature, you have built-in GraphRAG capabilities for your agentic AI applications. In this release, AllegroGraph provides enhanced the collaborative workflow around these Natural Language Query examples with new metadata tracking. Additionally, a new tabular view option has been introduced that provides a more structured presentation of query metadata, making it easier to sort, filter, and compare query examples at a glance. This enhancement streamlines the process of maintaining high-quality training examples that drive improved natural language understanding. Other features include: Bridging Documents and Graphs; Security and Access Control; AI Symbolic Rule Generation; Knowledge Graph-as-a-Service; Enhanced Scalability and Performance; and Advanced Knowledge Graph Visualization.
OpenAI’s new PDF export capability enables users to download comprehensive research reports with fully preserved formatting, tables, images, and clickable citations
OpenAI launched a new PDF export capability for its Deep Research feature, enabling users to download comprehensive research reports with fully preserved formatting, tables, images, and clickable citations. PDF export directly addresses a practical pain point for professional users who need to share polished, verifiable research with colleagues and clients. The technical implementation of PDF export transforms Deep Research from an interesting capability into a practical business tool by addressing several critical requirements for enterprise adoption. First, it bridges the gap between cutting-edge AI and traditional business communication. Second, the preservation of citations as clickable links addresses the critical need for verifiability in professional contexts. Without verifiable sources, AI-generated research lacks credibility in high-stakes decision-making environments. Most importantly, the PDF export capability dramatically improves Deep Research’s shareability. AI-generated insights create value only when they can be effectively distributed to decision-makers. By enabling users to generate professional-looking documents directly from research sessions, OpenAI removes a significant barrier to broader organizational adoption. The feature’s implementation across both new and past reports also demonstrates technical foresight. Rather than requiring users to adapt to AI-native interfaces for sharing research findings, the PDF export recognizes that many organizations still require traditional document formats for effective information distribution.
Ally Bank’s AI platform can pick the right external LLM depending on the use case or combine answers from several LLMs; it removes PII, tracks all transactions and rehydrates PII for context
In an era where differentiation in banking is increasingly difficult, Ally Bank has emerged as a leader in creating exceptional digital banking experiences. Sathish Muthukrishnan, chief information and technology officer at Ally Financial said, “The intent behind launching our technology strategy was to ensure that technology will continue to be relevant in an all-digital bank, but more importantly, to create differentiation and drive significant business outcomes. We categorized our strategy into six different pillars. The first is security. Our second pillar was driving tremendous experiences. The third pillar is how I know my experience is working. That’s when data analytics came in. Measure what consumers do, but more importantly, measure what they don’t do. Our operational pillar involved migrating to cloud, driving automation and consistency in how we develop and deploy code. And then we needed to preserve our culture and take care of our talent. These pillars laid the foundation for our transformation. We now have about 75% of our applications running on the cloud and about 95% of the enterprise data in the cloud. This allows us to learn from consumer behaviors, understand what they’re expecting and create experiences in real time so consumers think they are our only customer. We had our cloud strategy and data in the cloud warehouse. At the beginning of 2022, we redefined our network. As we were thinking about AI, we launched our chat assistant, Ally Assist. We created Ally AI because we knew technology was fast-evolving, but there were concerns about sending data to external LLMs. To address this, we built an AI platform that could connect to external LLMs but with added security — it removes PII, tracks all transactions and rehydrates PII for context. Our platform can connect to multiple LLMs — from GPT to FLAN to Bedrock. We can pick the right LLM depending on the use case or combine answers from several LLMs. Our content creation LLM is different from what we use for code generation or risk assessment. We have different models for different use cases. My advantage is that the product team, UI/UX team and technology team are all part of the same technology organization. We rolled out savings buckets — your deposit account with multiple savings buckets that you can name yourself. If you start questioning why roadblocks exist and how to solve them, your brand becomes more relevant to consumers. You become their next best experience, deepening relationships.”
Zilliz Cloud is supporting powering database infrastructure of AI applications through sub-10ms latency, zero downtime and outages, 70% savings in infrastructure costs, 10% improvement in search accuracy and 8% faster responsesdelivers sub-10ms latency and cost savings for AI-first companies
Organizations implementing Zilliz Cloud are experiencing transformative performance improvements that directly impact their AI applications: 1) CX Genie doubled query performance after migrating to Zilliz Cloud, reducing latency to just 5–10ms across over 1 million embeddings. The team eliminated recurring downtime and improved global service reliability — critical for its always-on AI-powered customer support. Latency: 2× faster, now 5–10ms; Uptime: Zero daily downtime; Costs: 70% infrastructure savings. 2) Beatoven.ai, an AI-powered music creation platform, shortened generation time by 2–3 seconds per track after adopting Zilliz Cloud — improving creative workflows for its 1.5 million+ users. Tracks: Over 6 million AI-generated; Speed: 2–3s faster music creation; Costs: 6× reduction in operational spend. 3) Ivy.ai powers AI chatbots for higher education and government institutions. As data volumes surged by 200%, Zilliz Cloud enabled them to maintain consistent response times without a single outage. Data growth: +200%; Reliability: Zero outages; Consistency: Stable response times at scale 4) Dopple Labs uses Zilliz Cloud to store and retrieve long-term memory embeddings for Dopple.ai, its virtual AI companion. By improving context awareness across conversations, Dopple now offers more natural, personalized interactions. Context: Improved memory across sessions; Interactions: More personalized, human-like dialogue 5) EviMed, a medical AI platform, integrated Zilliz Cloud to manage 350M+ medical knowledge entries. They achieved better search accuracy and faster responses while cutting system costs. Accuracy: +10% in clinical search precision; Speed: +8% faster responses; Efficiency: 30% lower operational cost. The results reported by Zilliz Cloud customers show that database infrastructure is no longer just backend plumbing — it’s a core driver of AI performance, reliability, and cost-efficiency. The ability to deliver sub-10ms latency, reduce outages, and cut operational costs gives AI teams a powerful edge in a competitive market.
Anaconda supports enterprise open source, combining trusted distribution, simplified workflows, real-time insights, and governance controls in one place to deliver secure and production-ready enterprise Python
Anaconda announced the release of the Anaconda AI Platform, the only unified AI platform for open source that provides proven security and governance when leveraging open source for AI development, empowering enterprises to build reliable, innovative AI systems without sacrificing speed, value, or flexibility. As the only AI platform for open source, the Anaconda AI Platform combines trusted distribution, simplified workflows, real-time insights, and governance controls in one place to deliver secure and production-ready enterprise Python. The Anaconda AI Platform empowers organizations to leverage open source as a strategic business asset, providing the essential guardrails that enable responsible innovation while delivering documented ROI and enterprise-grade governance capabilities. The Anaconda AI Platform enables enterprises to build once and deploy anywhere safely and at scale. Anaconda saw a 119% ROI and $1.18M in benefits within three years, with improved operational efficiency (80% improvement worth $840,000, according to the Forrester study) and enterprise-powered security (Anaconda provided an 80% reduction in time spent on package security management and a 60% reduction in security breach risk, according to the Forrester study). The Anaconda AI Platform eliminates environment-specific barriers, enabling teams to create, innovate, and run AI applications across on-premise, sovereign cloud, private cloud, and public cloud on any device without reworking code for each target. The platform is now available on AWS Marketplace for seamless procurement and deployment. Additional features include: Trusted Distribution; Secure Governance; Actionable Insights
Parasoft’s agentic assistant automates generating API test scenarios using service definition files while also parameterizing for data looping
Parasoft has added Agentic AI capabilities to SOAtest, featuring API test planning and creation. Parasoft also has enhanced its Continuous Testing Platform (CTP), extending Test Impact Analysis (TIA) and code coverage collection to manual testers, further reducing technical barriers, accelerating feedback, and improving collaboration between development and quality. Parasoft SOAtest’s AI Assistant now utilizes agentic AI in API test-scenario generation, making it easier for testing teams with diverse skill sets to adopt API test automation. This release now enables a tester to, in natural language, request the AI to generate API test scenarios using service definition files. Going beyond simple test creation, the AI Assistant leverages AI agents to generate test data and parameterize the test scenario for data looping. Complex, multi-step workflows with dynamic data are handled in collaboration with the user, allowing less technical testers to build complicated tests without requiring scripts, advanced code-level skills, or in-depth domain knowledge. In addition to reducing technical burdens, Parasoft’s AI Assistant will help customers scale API testing and automate other in-product actions. As additional agents are introduced over time, it will produce even smarter test scenarios and workflow guidance. QA teams can leverage Parasoft CTP to collect and analyze code coverage from manual test runs, then publish that coverage into Parasoft DTP for deeper analysis. In CTP, the tester can easily create a manual test case, and with a few clicks can ensure code coverage is captured during their test runs. With this visibility, teams can fine-tune their manual testing efforts—eliminating redundancies, filling coverage gaps, and focusing on the highest-risk areas. Teams can now create, import, and manage manual tests directly in CTP, capture code coverage as those tests run, and utilize that data in test impact analysis to pinpoint exactly which manual regression tests need to be rerun to validate application changes. This trims retesting time and effort, reducing testing fatigue while strengthening collaboration between development and QA teams. This new capability also makes it easier to adapt manual regression testing for agile sprints, as it allows teams to only focus on impacted areas. With faster test cycles, QA teams can quickly validate changes and shorten feedback loops.
Pega launches agents for workflow and decisioning design that can instantly create out-of-the-box conversational agents from any workflow
Pegasystems unveiled Pega Predictable AI™ Agents that give enterprises extraordinary control and visibility as they design and deploy AI-optimized processes. Businesses can deploy Pega Predictable AI Agents with confidence, accelerating value while minimizing risk. Pega Predictable AI Agents allow enterprises to avoid the sinkhole of “AI black boxes” by thoughtfully integrating AI agents into the world’s leading enterprise platform for workflow automation. Instead of providing nothing more than prompt-based authoring tools, basic dashboards, and vague advice to use it wisely, Pega maximizes the value of AI while minimizing risk with the following Pega Predictable AI Agents: Design Agents: At the core of Pega Predictable AI Agents strategy is Pega Blueprint™, the industry’s first agents for workflow and decisioning design. Pega Blueprint leverages a collection of unique AI models and agents to generate workflows, next-best-action strategies, data structures, interfaces, user screens, security configuration, and more. It can also be invoked at runtime if a user needs to automate a process on the fly that isn’t already defined in the application. Conversation Agents: Leveraging the Pega Agent Experience™ API, Pega Blueprint can instantly create out-of-the-box conversational agents from any workflow. Automation Agents: Clients can incorporate these agents into their workflows as specific workflow steps, orchestrating agents both inside and outside of Pega to accelerate productivity in a transparent and reliable way. Knowledge Agents: Pega Blueprint leverages Pega Knowledge Buddy™ agents to create workflows that leverage industry best practices and to embed guidance inside other workflows. Coach Agents, such as Pega Coach, collaborate with humans involved in a workflow step to provide real-time, contextual guidance about the work.