Camunda has highlighted how its agentic orchestration capabilities are enabling organizations to introduce AI at scale into their processes while preserving transparency, compliance, and control. Agentic trade exception management (available on Camunda Marketplace): Camunda’s platform allows clients to connect their preferred AI models, whether hosted in the cloud or internally via EY labs, and apply deterministic guardrails to ensure AI is only triggered when appropriate. This lets clients avoid rebuilding AI from scratch, instead focusing on governance, visibility, and scalable deployment – areas where Camunda’s orchestration brings immediate and measurable value. In one capital markets implementation, EY reduced manual effort by 86%, cut T+1 delays by 98%, and boosted analyst productivity from 6–10 to 41–64 cases per day – a 7x improvement. Agentic AI-assisted quality audit process (available on Camunda Marketplace): Cognizant has created and demonstrated workflows in Camunda that include mandatory human review steps – enabling AI to suggest actions, but requiring manual approval before those actions are executed. This balance allows organizations to benefit from AI-powered insights while also facilitating compliance with regional laws. For example, audit trails, escalation paths, and process visibility are all embedded into the BPMN model, assisting organizations in demonstrating full control over every agentic interaction. This led to significant time savings: the quality audit process was reduced from 138 minutes to just 7–10 minutes, increasing auditor productivity by 20–30%, and cutting costs by 30–50%. All activity is fully traceable via embedded audit trails and escalation paths in BPMN. Customer service agent (available on Camunda Marketplace): Replacing standard auto-responses, Incentro built an AI agent that uses a LLM to analyze queries and draft meaningful replies in real time. The agent accesses the company’s full FAQ and documentation set, enabling specific answers rather than generic acknowledgments. Camunda’s BPMN model structures the logic, with the agent dynamically choosing the best response path via an ad-hoc sub-process. When implementing these systems with Payter, Incentro was able to reduce handling time per inquiry from 24 to 12 minutes, with lead time cut by 58%, helping improve both customer NPS and agent satisfaction without increasing headcount. Compliance agent (available on Camunda Marketplace): BP3 shared how it is integrating agentic AI into decision-heavy workflows in regulated industries like BFSI, pharma, healthcare, and utilities. Its approach uses LLMs alongside DMN (Decision Model and Notation) tables to generate “accept, reject, or refer” outcomes. In ambiguous cases, decisions are escalated to a human, enabling the AI to learn from real-world feedback over time.
Tines workflow automation agents enable enterprises to apply the right level of automation with flexibility from manual to fully autonomous within a single platform and run entirely within the platform’s secure infrastructure
Tines announced autonomous AI capabilities within its workflow automation platform via the launch of agents. Agents mark a significant evolution in Tines’ platform, enabling customers to automate workflows with maximum control and flexibility, whether with deterministic logic, human-in-the-loop copilots, or full AI autonomy. Agents enable Tines customers to build intelligent, context-aware workflows that can act independently, suggest next steps, and collaborate with users in real time. The addition of agents allows customers to choose the right level of AI involvement for every workflow, ensuring organizations can implement AI automation that aligns with their specific security requirements, levels of complexity, and operational needs. Unlike traditional AI implementations that require external data sharing or compromise on security, Tines’ agents run entirely within the platform’s secure infrastructure. This ensures no customer data leaves the environment, is logged, or used for training, delivering the privacy and governance assurances that enterprise teams demand. Tines capabilities: Full-spectrum automation and orchestration: Apply the right level of automation with flexibility—from manual to fully autonomous—within a single platform. Enterprise-grade security: Built by security professionals, Tines keeps all automation and data within its own infrastructure. Seamless system integration: Connect any tool, LLM, or proprietary app to build, augment, and orchestrate intelligent workflows. Intuitive no-code interface: Easily design complex, mission-critical workflows with drag-and-drop tools and built-in collaboration features. User-friendly adoption: Deploy apps, chatbots, and integrations with popular tools such as Slack to boost usage and maximize ROI on AI initiatives.
CC Signals framework will allow creators to publish a document that specifies how AI models may and may not use their content
Creative Commons has previewed an upcoming framework designed to help creators manage how artificial intelligence models use their content. The framework, which is called CC Signals. The new CC Signals framework will allow creators to publish a document that specifies how AI models may and may not use their content. This document will “range in enforceability, legally binding in some cases and normative in others,” Creative Commons staffers noted. The reason is that the extent to which creators can limit AI models’ use of their works varies by jurisdiction. Creative Commons detailed that the framework will include four content usage stipulations. Each one sets forth a different set of requirements for how AI models may interact with a file. CC Signals will allow creators to apply up to two of the four stipulations to a given file. Using CC Signals, creators can also indicate that they expect compensation from AI developers who use their work. That compensation may take several forms. CC Signals will allow for monetary or in-kind contributions to a file’s creators, as well as to the broader “ecosystem from which you are benefiting.” According to Creative Commons, there will be support multiple definitions of open-source AI. Content creators may require that a neural network’s weights be available under a free license. They can also go further and mandate that the tooling with which the algorithm was developed be open source as well.
Qodo launches CLI agent framework that enables developers to create, customize, and deploy their own AI coding agents.
Qodo, maker of an AI coding platform, today announced the release of Qodo Gen CLI, an agent framework that enables developers to create, customize, and deploy their own AI coding agents. With the framework, creating agents can be done by writing configuration files that add autonomous AI agents throughout the software development life cycle, according to the company’s announcement. Qodo was built to help developers add autonomous coding capabilities to their applications without requiring expertise in AI systems, which can lead to solutions that sync up with an organization’s requirements, the company said. With Qodo Gen CLI, developers can define custom agents and what tools they can access, specify actions that trigger the agents, what instructions guide their behavior and ultimately, what their outputs should be. Along with enabling custom agent creation, Qodo Gen CLI includes pre-built agents for code review, test coverage analysis, and release notes generation. These agents integrate seamlessly with existing development tools through GitHub Actions, GitLab CI, Jenkins, and other CI/CD systems. For advanced use cases, agents can be exposed as Model Context Protocol (MCP) servers, enabling integration with other AI tools and platforms.
OpenAI’s API platform allows developers to express intent, not just configure model flows through built-in capabilities for knowledge retrieval, web search, and function calling for supporting real-world agent workflows
Olivier Godement, Head of Product for OpenAI’s API platform, provided a behind-the-scenes look at how enterprise teams are adopting and deploying AI agents at scale. According to Godement, 2025 marks a real shift in how AI is being deployed at scale. With over a million monthly active developers now using OpenAI’s API platform globally, and token usage up 700% year over year, AI is moving beyond experimentation. Godement emphasized that current demand isn’t just about chatbots anymore. “AI use cases are moving from simple Q&A to actually use cases where the application, the agent, can do stuff for you.” This shift prompted OpenAI to launch two major developer-facing tools in March: the Responses API and the Agents SDK. Some enterprise use cases are already delivering measurable gains. Godement positioned the Responses API as a foundational evolution in developer tooling. Previously, developers manually orchestrated sequences of model calls. Now, that orchestration is handled internally. “The Responses API is probably the biggest new layer of abstraction we introduced since pretty much GPT-3.” It allows developers to express intent, not just configure model flows. “You care about returning a really good response to the customer… the Response API essentially handles that loop.” It also includes built-in capabilities for knowledge retrieval, web search, and function calling—tools that enterprises need for real-world agent workflows. Some enterprise use cases are already delivering measurable gains: Stripe, which uses agents to accelerate invoice handling, reporting “35% faster invoice resolution; ” Box, which launched knowledge assistants that enable “zero-touch ticket triage.” Other high-value use cases include customer support (including voice), internal governance, and knowledge assistants for navigating dense documentation. Godement offered a glimpse into the roadmap. OpenAI is actively working on: Multimodal agents that can interact via text, voice, images, and structured data; Long-term memory for retaining knowledge across sessions; Cross-cloud orchestration to support complex, distributed IT environments. What matters now is building a focused use case, empowering cross-functional teams, and being ready to iterate. The next phase of value creation lies not in novel demos—but in durable systems, shaped by real-world needs and the operational discipline to make them reliable.
Study finds running gen AI models on the phones instead of in the cloud consumed anywhere from 75% to 95% less power, with associated sharp decreases in water consumption and overall carbon footprint
One of the easiest ways to minimize AI’s environmental impact may be to move where the processing is done, per new academic research conducted in partnership with Qualcomm. Running AI on devices instead of in the cloud slashes power consumption of queries by about 90%, the study finds. The industry has long touted the benefits of running models locally on devices instead of in the cloud — not just in energy terms, but also potentially making them cheaper and more private. Researchers at the University of California, Riverside ran a series of experiments comparing the performance of various generative AI models, both in the cloud and on phones powered with Qualcomm chips. Running any of six different models on the phones consumed anywhere from 75% to 95% less power, with associated sharp decreases in water consumption and overall carbon footprint. Qualcomm is also developing an AI simulator and calculator that illustrates, for any given query and user location, what the responses would look like on-device versus the cloud, and how much less power and water they would use. One example — running a coding skills question on the Llama-2-7B model in California — was 94% more power efficient and 96% more water efficient on-device. For all six models in the study, the inference time on the phones, measured in seconds, was higher than in the cloud. Narrowing or eliminating that gap, particularly on the most powerful and popular models, will be crucial to accelerating on-device adoption. For many AI users, the data center in your pocket might be all you need.
SAP Fioneer’s AI agent allows finance teams to generate complex reports using natural language by leveraging the suspense account analysis without the need to share data externally
SAP Fioneer has launched its AI Agent: an expert-built solution designed to intelligently enhance core operations of financial services institutions. By leveraging the suspense account analysis, finance teams can generate complex reports using natural language, significantly reducing manual effort, improving operational efficiency, and achieving considerable time savings. The Fioneer AI Agent delivers intelligence that is integrated into SAP Fioneer’s banking, insurance, and finance solutions, offering contextual, transparent, and actionable use cases without the need for custom development and heavy IT dependance. It empowers financial professionals to interact with data using natural language, eliminating reliance on IT teams and accelerating time to value. Designed for flexibility, the Fioneer AI Agent supports bring-your-own-LLM strategies as well as SAP BTP AI Core LLMs and will integrate with SAP Joule and other agents such as Microsoft Copilot. Integrated and aligned with the SAP strategy, it ensures full compliance with data privacy and auditability standards, making it a trusted solution for institutions seeking to scale AI responsibly and effectively. The first release of the Fioneer AI Agent lays the foundation for banks and insurers to automate processes, gain real-time insights, and make smarter decisions using natural language and without the need to share data externally. The Fioneer AI Agent is generally available now as an add-on for SAP Fioneer S/4HANA products in Banking, Insurance, and Finance.
Crusoe’s modular data centers enable rapid deployments with diverse power sources for edge inference by integrating all necessary infrastructure into a single, portable unit
Crusoe has launched Crusoe Spark™, a prefabricated modular AI factory designed to bring powerful, low-latency compute to the network’s edge. The modular data centers integrate all necessary infrastructure, including power, cooling, remote monitoring, fire suppression, and racks supporting the latest GPUs, into a single, portable unit. Crusoe Spark enables rapid deployments with diverse power sources for on-prem AI, edge inference, and AI capacity expansion needs, with units delivered as fast as three months. AI at the edge is transforming industries by enabling real-time decision-making and intelligence directly where data is generated, without the latency and bandwidth limitations of a remote cloud system. AI-optimized modular data centers integrate all necessary infrastructure—including power, cooling, remote monitoring, fire suppression, and racks that support the latest GPUs—into a single, portable unit. Crusoe Spark enables rapid deployments with diverse power sources for on-prem AI, edge inference, AI capacity expansion needs, with units delivered as fast as three months. AI at the edge is transforming industries by enabling real-time decision-making and intelligence directly where data is generated, without the latency and bandwidth limitations of a remote cloud system. This capability is critical for applications including autonomous vehicles needing instant reactions, real-time patient monitoring in healthcare, predictive maintenance in manufacturing, and smart city infrastructure optimizing traffic flow and public safety. This rapidly expanding market is driven by the explosive growth of IoT devices and the demand for immediate, localized AI insights.
Zerve and Arcee AI solution to enable users to automate AI model selection within their existing workflows by intelligently selecting between SLMs and LLMs based on input complexity, cost, domain relevance, and other variables
Zerve, the agent-driven operating system for Data & AI teams, announced a partnership with Arcee AI, a language model builder to bring model optimization and automation capabilities to the Zerve platform, enabling data science and AI professionals to build faster, smarter, and more efficient AI workflows at scale. Through the new partnership and integration, Zerve and Arcee AI enable users to automate AI model selection within their existing workflows using an OpenAI-compatible API, without incurring infrastructure overhead. Arcee Conductor enhances AI pipeline efficiency for users by intelligently selecting between SLMs and LLMs based on input complexity, cost, domain relevance, and other variables. This collaboration allows data science and AI engineering teams to: Optimize model usage by routing tasks to the most appropriate model, improving accuracy and runtime performance; Enhance automation by combining Conductor’s routing with the Zerve Agent’s dynamic workflow control; Maintain seamless integration through plug-and-play compatibility with existing Zerve environments; Cut costs by deploying lightweight, lower-cost models where applicable.
Anysphere’s new agent orchestration tools allow developers to send natural language prompts from a mobile or web-based browser directly to the background agents, instructing them to perform tasks like writing new features or fixing bugs
Well-funded AI startup Anysphere Inc. is expanding beyond its viral generative AI code editor and into “agentic AI” with the launch of new web and mobile browser-based orchestration tools for coding agents. With its new application, developers can send natural language prompts from a mobile or web-based browser directly to the background agents, instructing them to perform tasks like writing new features or fixing bugs. Using the web app, developers can also monitor fleets of agents that are busy working on different tasks, check their progress and register those that have been completed within the underlying codebase. Anysphere explained that developers can instruct its AI agents to complete tasks via the web app, and if they’re unable to do so, they can seamlessly switch to the IDE to take over and see what’s caused it to come unstuck. Each of its agents has its own shareable link, which developers can click on to see its progress.