# workflow orchestration Published articles for workflow orchestration. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## LittleHorse adds agent controls and free serverless trial to its 'Business-as-Code' orchestration platform DevFeed: [LittleHorse adds agent controls and free serverless trial to its 'Business-as-Code' orchestration platform]() Original publisher: [Read original article]() Author: Paul Gillin Published: 2026-09-21T12:00:49Z Content type: release Language: en Sources: [SiliconANGLE]() Topics: [Orchestration](), [Artificial Intelligence](), [Cloudflare Workers](), [SDK](), [Apache Kafka]() Tags: [agent](), [agentic-orchestration](), [apache-kafka](), [apps](), [artificial-intelligence](), [business-as-code](), [business-process-management](), [deployment](), [javascript](), [littlehorse-enterprises-llc](), [littlehorse-saddle-command-center](), [microservices](), [news](), [orchestration](), [serverless](), [the-latest](), [top-story-6](), [workflow-orchestration]() ### AI overview LittleHorse announced version 1.3 of Saddle Command Center, adding no-code agent deployment, prebuilt task workers, a JavaScript SDK, agent controls, Kafka-compatible streaming, connectors, and a free serverless trial. The platform coordinates AI agents and conventional software through version-controlled, testable workflows. ### Source excerpt LittleHorse Enterprises LLC, a maker of workflow orchestration software spanning multiple applications, today announced version 1.3 of its Saddle Command Center, adding no-code agent deployment, prebuilt task workers, a JavaScript software development kit, and a free serverless trial to its platform for coordinating artificial intelligence agents and conventional software. The privately held company describes Saddle [...] The post LittleHorse adds agent controls and free serverless trial to its 'Business-as-Code' orchestration platform appeared first on SiliconANGLE. ## What to expect from theCUBE's analysis of Dreamforce: Tune in Sept. 25 DevFeed: [What to expect from theCUBE's analysis of Dreamforce: Tune in Sept. 25]() Original publisher: [Read original article]() Author: Victoria Gayton Published: 2026-09-18T13:57:37Z Content type: opinion Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [Automation](), [Model Context Protocol](), [business logic](), [Command-line interface]() Tags: [agentforce](), [agentforce-operations](), [agentic](), [agentic-ai](), [agentic-enterprise](), [ai](), [ai-agents](), [ai-security](), [artificial-intelligence](), [automation](), [cloud](), [command-line](), [connected-ai-workflows](), [cube-event-coverage](), [customer-360](), [customer-experience](), [data-360](), [data-360-headless](), [data-cloud](), [data-governance](), [digital-transformation](), [dreamforce](), [dreamforce-2026](), [dreamforce26eventpage](), [enterprise-ai](), [enterprise-applications](), [generative-ai](), [kishan-chetan](), [model-context-protocol](), [news](), [salesforce](), [salesforce-help-agent](), [thecube](), [tools](), [workflow-orchestration]() ### AI overview The article previews analysis of Dreamforce and examines how Salesforce is positioning connected AI workflows and the agentic enterprise. It focuses on integrating AI agents with business data, established workflows, application environments, permissions, and governance, including the use of APIs, Model Context Protocol tools, and a command-line interface. ### Source excerpt Enterprises are moving beyond standalone AI tools as they look for ways to weave agents into the work employees and customers already do. The shift toward connected AI workflows raises broader questions about how agents access data, interact with people and operate across established business processes. Salesforce Inc. is positioning the agentic enterprise as the [...] The post What to expect from theCUBE's analysis of Dreamforce: Tune in Sept. 25 appeared first on SiliconANGLE. ## Enhancing Cloud Usage Forecasting, Monitoring & Optimizing DevFeed: [Enhancing Cloud Usage Forecasting, Monitoring & Optimizing]() Original publisher: [Read original article]() Author: Anthony Tambasco Published: 2024-06-17T13:58:16Z Content type: article Language: en Sources: [Etsy Engineering | Code as Craft]() Topics: [finops](), [Google Cloud Platform (GCP)](), [migration](), [Monitoring](), [data-processing](), [Orchestration](), [engineering-culture]() Tags: [cloud](), [data-processing](), [efficiency](), [finops](), [forecasting](), [gcp](), [google-cloud](), [infrastructure](), [migration](), [monitoring](), [optimizing](), [workflow-orchestration]() ### AI overview Etsy describes how its FinOps team forecasts and monitors cloud spending after migrating from an on-premise data center to Google Cloud Platform. The approach uses cost categories and a trailing Cost Per Visit metric, while accounting for limitations involving crawlers, machine-learning training, data costs, development resources, and short-term spikes. ### Source excerpt In 2020, Etsy concluded its migration from an on-premise data center to the Google Cloud Platform (GCP). During this transition, a dedicated team of program managers ensured the migration's success. Post-migration, this team evolved into the Etsy FinOps team, dedicated to maximizing the organization's cloud value by fostering collaborations within and outside the organization, particularly with our Cloud Providers. Positioned within the Engineering organization under the Chief Architect, the FinOps team operates independently of any one Engineering org or function and optimizes globally rather than locally. This positioning, combined with Etsy's robust engineering culture focused on efficiency and craftsmanship, has fostered what we believe is a mature and successful FinOps practice at Etsy. Forecast Methodology A critical aspect of our FinOps approach is a strong forecasting methodology. A reliable forecast establishes an expected spending baseline against which we track actual spending, enabling us to identify deviations. We classify costs into distinct buckets: Core Infrastructure: Includes the costs of infrastructure and services essential for operating the Etsy.com website. Machine Learning & Product Enablement: Encompasses costs related to services supporting machine learning initiatives like search, recommendations, and advertisements. Data Enablement: Encompasses costs related to shared platforms for data collection, data processing and workflow orchestration. Dev: Encompasses non-production resources. The FinOps forecasting model relies on a trailing Cost Per Visit (CPV) metric. While CPV provides valuable insights into changes, it's not without limitations: A meaningful portion of web traffic to Etsy involves non-human activity, like web crawlers that's not accounted for in CPV. Some services have weaker correlations to user visits. Dev, data, and ML training costs lack direct correlations to visits and are susceptible to short-term spikes during POCs, exp