# Orchestration In computing, orchestration is the execution of a defined workflow in a specified sequence. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Scaling Enterprise Testing on Kubernetes with Argo DevFeed: [Scaling Enterprise Testing on Kubernetes with Argo]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-09-18T14:01:33Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Testing](), [Kubernetes](), [scaling](), [Orchestration](), [Multitenancy](), [Open Source](), [Cloud Native Ecosystem](), [parallel](), [Latency]() Tags: [argo](), [ci-cd](), [cloud-native](), [cluster](), [cncf](), [enterprise](), [infrastructure](), [kubernetes](), [latency](), [open-source](), [parallel](), [scaling](), [software-engineering](), [testing]() ### AI overview A case study of Capital One's rebuilt enterprise testing platform, which uses Kubernetes and open-source Argo ecosystem projects to run large numbers of parallel tests across teams. It describes the limitations of the previous pipeline and the platform's focus on scalability, multi-tenancy, and stability. ### Source excerpt How we built a resilient, multi-tenant test platform on open-source workflows. Every engineer knows the quiet anxiety of watching a CI/CD pipeline run. You commit, you wait for the build and then you wait some more. Now imagine that at enterprise scale: thousands of test agents running at once, across dozens of teams, all validating business-critical services on the same shared infrastructure. That's the problem our platform exists to solve, and for years, the way we solved it was quietly holding us back. At Capital One, we're a technology company that happens to operate in one of the most tightly regulated industries there is. That combination raises the bar: our internal platforms strive to be fast and safe, to scale for many teams at once and to prevent any single workload from destabilizing the rest. So when our first-generation testing pipeline started hitting hard limits, we didn't just move it; we rebuilt it on Kubernetes using open-source, Cloud Native Computing Foundation (CNCF) projects from the Argo ecosystem. In this post, we'll walk through three things: Why we moved off our first-generation pipeline How Argo orchestrates a single test run, end to end The guardrails that keep the platform stable when thousands of tests arrive at once The headline isn't just "we moved to Kubernetes." It's that we learned to run massively parallel test workloads without letting any single burst threaten a shared, multi-tenant cluster. Why we moved off our first-generation pipeline Our original platform got the job done, but it was built from a highly complex set of managed services linked together by a proprietary orchestrator. Execution logic lived inside cloud-specific state-machine definitions, far away from the containers actually running the tests. Three pain points stood out: State-machine sprawl. Changing execution logic meant editing large, cloud-specific workflow definitions. The orchestration lived far away from the code it was orchestrating. Cold-start latency. ## Itential's Agentic Platform for Enterprise Network Automation and Orchestration DevFeed: [Itential's Agentic Platform for Enterprise Network Automation and Orchestration]() Original publisher: [Read original article]() Author: Roger Perkin Published: 2026-09-18T09:03:07Z Content type: article Language: en Sources: [Roger Perkin Network Automation Consultant]() Topics: [Automation](), [Orchestration](), [Network](), [Artificial Intelligence](), [Access Control](), [Security](), [Ansible](), [Terraform](), [Python](), [Cloud]() Tags: [access-control](), [agentic](), [automation](), [faq](), [infrastructure](), [network](), [orchestration](), [python](), [security](), [terraform]() ### AI overview Itential provides an enterprise platform for building and executing AI agents and deterministic workflows, including Ansible playbooks, Python scripts, and Terraform jobs. Its automation platform connects infrastructure, cloud environments, and IT systems through a unified engine. ### Source excerpt Itential is the Agentic Platform for Enterprise Operations. They make it easy to build and execute agents as well as deterministic workflows, such as Ansible playbooks, Python scripts or Terraform jobs. Itential Automation Platform The Itential Automation Platform is an enterprise-grade network automation and orchestration solution designed to connect multi-domain infrastructure, cloud environments, and IT systems ... ## A serverless, data-driven Git metrics dashboard using Amazon Quick Sight DevFeed: [A serverless, data-driven Git metrics dashboard using Amazon Quick Sight]() Original publisher: [Read original article]() Author: Saurabh Singhal Published: 2026-09-17T15:42:31Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [dashboards](), [GitHub](), [Amazon Web Services](), [GitLab](), [Amazon S3](), [AWS Step Functions](), [event driven](), [Orchestration](), [API](), [parallel](), [ai-coding]() Tags: [advanced-300](), [ai](), [ai-coding-tools](), [amazon-quick-sight](), [amazon-s3](), [automated](), [aws](), [aws-step-functions](), [concurrently](), [dashboard](), [dashboards](), [delivery](), [etl](), [event-driven](), [execution](), [github](), [gitlab](), [infrastructure](), [metrics](), [pipeline](), [real-time](), [technical-how-to]() ### AI overview This tutorial presents a serverless pipeline that collects Git metrics from GitHub and GitLab, processes repository activity through an event-driven workflow, stores results in Amazon S3, and visualizes them in interactive Amazon Quick Sight dashboards. It also describes change detection, incremental loads, and parallel processing for larger organizations. ### Source excerpt Learn how to build a fully serverless pipeline that automatically collects Git metrics from GitHub and GitLab and visualizes them in interactive Amazon Quick Sight dashboards, giving engineering teams near-real-time delivery analytics at low cost. ## A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore DevFeed: [A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Shridhar Navanageri Published: 2026-09-17T15:41:03Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [AI Platform](), [observability](), [Authentication](), [Orchestration](), [scaling](), [control-plane](), [tracing](), [Self-hosted]() Tags: [agentic](), [agentic-ai](), [amazon-bedrock-agentcore](), [authentication](), [control-plane](), [customer-solutions](), [infrastructure](), [observability](), [orchestration](), [product](), [systems]() ### AI overview Wood Mackenzie built APEX, a shared agentic platform on Amazon Bedrock AgentCore, to help teams move AI agents from experimentation into production. The platform centralizes runtime orchestration, identity, observability, connectivity, safety, and persistent state so teams can focus on product-specific business logic. ### Source excerpt Wood Mackenzie built APEX, a shared agentic AI platform on Amazon Bedrock AgentCore so every team can ship production agents without rebuilding runtime, identity, observability, and guardrails from scratch. Learn why they chose AgentCore, how APEX Studio operates it, and where multi-agent systems go next. ## Kubernetes Multi-Cluster Project Karmada Reaches CNCF Graduation DevFeed: [Kubernetes Multi-Cluster Project Karmada Reaches CNCF Graduation]() Original publisher: [Read original article]() Author: Claudio Masolo Published: 2026-09-17T10:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Kubernetes](), [Orchestration](), [Cloud](), [Computing]() Tags: [ai-architecture](), [cloud](), [cluster](), [clusters](), [cncf](), [devops](), [karmada-kubernetes-cncf](), [kubernetes](), [multi-cloud](), [news](), [orchestration](), [project](), [release]() ### AI overview The CNCF announced that Karmada, a multi-cluster and multi-cloud Kubernetes orchestration project, graduated to its highest maturity tier. The announcement coincided with Karmada v1.19, which improves multi-component scheduling for AI training jobs and makes priority-based scheduling available by default in Beta. ### Source excerpt The Cloud Native Computing Foundation (CNCF) announced on September 2026 that Karmada, a multi-cluster and multi-cloud Kubernetes orchestration project, has graduated. This multi-cluster and multi-cloud Kubernetes orchestration project reached CNCF's highest maturity tier. By Claudio Masolo ## Sunsetting netlab Vagrant/libvirt provider DevFeed: [Sunsetting netlab Vagrant/libvirt provider]() Original publisher: [Read original article]() Published: 2026-09-17T05:51:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [Vagrant](), [Infrastructure as code](), [Orchestration](), [Containerlab topology](), [Oracle-VM-VirtualBox]() Tags: [change](), [containers](), [infrastructure](), [infrastructure-as-code](), [netlab](), [orchestration](), [vagrant](), [virtualbox]() ### AI overview The netlab project is sunsetting its Vagrant/libvirt provider after the vagrant-libvirt plugin became effectively unmaintained and HashiCorp announced the closure of Vagrant Cloud by the end of 2026. Existing support and tests will remain for now, but no new libvirt features or device integration tests are planned; containerlab will become the primary orchestration path. ### Source excerpt When I started the netlab project, Vagrant was the go-to tool if you wanted to build a virtual environment described in a text configuration file (an idea popularized as infrastructure-as-code). It wasn't ideal for what we were doing, but a tool rarely does a great job when used far away from its intended use case. netlab initially supported Vagrant with VirtualBox, quickly adding support for KVM/libvirt through the vagrant-libvirt plugin. Life was good... until it wasn't. Read more ... ## Beyond the sync: Argo CD needs an Enterprise Control Plane DevFeed: [Beyond the sync: Argo CD needs an Enterprise Control Plane]() Original publisher: [Read original article]() Author: Eric Minick Sudarshan Purohit Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [argo-cd](), [GitOps](), [Orchestration](), [DevOps](), [Platform Engineering](), [Deployment](), [Artificial Intelligence]() Tags: [argo-cd](), [deployment](), [gitops](), [governance](), [orchestration](), [platform-engineering](), [verification]() ### AI overview This article argues that Argo CD's manifest synchronization is only one part of enterprise software delivery. It describes how scaling GitOps can create operational toil and Argo sprawl, and proposes an enterprise control plane with workflow orchestration, governance, and deployment verification. ### Source excerpt Scaling GitOps? Argo CD is great for syncing manifests, but enterprise delivery requires workflow orchestration, governance, and AI verification. | Blog ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## The Original Serverless Architecture is Still Here DevFeed: [The Original Serverless Architecture is Still Here]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-31T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [serverless architecture](), [Kubernetes](), [Orchestration](), [Docker](), [Cloud](), [Firebase](), [DynamoDB]() Tags: [amazon](), [cloud](), [containers](), [docker](), [dynamodb](), [engineering](), [firebase](), [infrastructure](), [kubernetes](), [news](), [orchestration](), [serverless](), [serverless-architecture]() ### AI overview This commentary compares Kubernetes-based architectures with serverless approaches. It explains that Kubernetes offers flexibility through containers, Helm, ingress controllers, monitoring tools, and service meshes, but requires substantial configuration and maintenance. Serverless platforms such as Firebase and Amazon Lambda abstract away server infrastructure so developers can focus on applications and stateless functions. ### Source excerpt By Kevin Dangoor This month, my colleague Dave Rosile and I went to GlueCon 2018 in sunny Denver, ... Read more ## Google and OpenAI take different approaches to reducing voice-agent latency DevFeed: [Google and OpenAI take different approaches to reducing voice-agent latency]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T21:50:15Z Content type: comparison Language: en Sources: [The New Stack]() Topics: [Latency](), [Google AI](), [OpenAI](), [API](), [Orchestration](), [real-time](), [Artificial Intelligence]() Tags: [ai](), [ai-agents](), [ai-engineering](), [api](), [comparison](), [google-ai](), [large-language-models](), [latency](), [openai](), [orchestration](), [real-time]() ### AI overview The article compares Google's Gemini 3.8 Live Extended Thinking with OpenAI's GPT-Live-1 for reducing latency in voice agents. Google keeps speech, reasoning, and asynchronous tool execution in one stateful session, while OpenAI uses a real-time conversation model alongside a backend reasoning model, shifting more orchestration to the application. ### Source excerpt Voice agents have a latency problem that shows up as soon as they have to do real work. Within five The post OpenAI's voice model doesn't think. That's the point. appeared first on The New Stack. ## AWS agents will suggest your new flights. Code decides what gets booked. DevFeed: [AWS agents will suggest your new flights. Code decides what gets booked.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T21:27:21Z Content type: news Language: en Sources: [The New Stack]() Topics: [AWS Step Functions](), [Amazon Bedrock AgentCore](), [Orchestration](), [Generative AI](), [Amazon Web Services]() Tags: [ai-agents](), [ai-engineering](), [airline](), [amazon-bedrock-agentcore](), [aws](), [aws-step-functions](), [cloud-services](), [generative-ai](), [orchestration]() ### AI overview AWS published a Step Functions pattern for airline rebooking in which Amazon Bedrock AgentCore agents propose itineraries and compensation messages, while deterministic workflow steps validate proposals before reservations change or payments are issued. ### Source excerpt AWS published a new Step Functions pattern this week that gives AI agents a role in airline rebooking while keeping The post AWS agents will suggest your new flights. Code decides what gets booked. appeared first on The New Stack. ## From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production DevFeed: [From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production]() Original publisher: [Read original article]() Author: Vishal Ganeriwala Published: 2026-09-15T16:55:59Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [AI Factory](), [NVIDIA DSX](), [datacenter](), [Orchestration](), [Inference](), [High-Performance Computing]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [capacity](), [compute](), [dsx](), [efficiency](), [inference](), [nvidia](), [orchestration](), [production]() ### AI overview The article describes how Emerald AI's Conductor platform responds to utility demand signals by adjusting flexible data-center workloads while keeping high-priority AI inference running. It also reports that Lambda's validation found a fixed power budget could support 24% more token throughput when managed intelligently. ### Source excerpt On a sweltering August evening in Silicon Valley, as the sun dropped and air conditioning loads spiked, Silicon Valley Power sent a signal to an AI factory to adjust its power consumption. Varun Sivaram was watching on Zoom with about forty others -- his team at Emerald AI in their San Francisco conference room, engineers [...] ## NVIDIA Adds CUDA-Q Logical for Fault-Tolerant Quantum Application Design DevFeed: [NVIDIA Adds CUDA-Q Logical for Fault-Tolerant Quantum Application Design]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T16:47:58Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [CUDA](), [Quantum Computing](), [Nvidia](), [Orchestration](), [Open Source](), [Algorithm]() Tags: [ai](), [algorithm](), [applications](), [cuda](), [enterprise](), [nvidia](), [open-source](), [orchestration](), [quantum]() ### AI overview NVIDIA added CUDA-Q Logical to its open-source CUDA-Q platform for designing applications on fault-tolerant quantum computers. Early-access reports say Fermilab reduced an algorithm design cycle from five months to three weeks, while Iceberg Quantum modeled a Diraq spin-qubit architecture using about 150,000 physical qubits for 1,000 logical qubits. ### Source excerpt NVIDIA has added CUDA-Q Logical to its open-source CUDA-Q platform, an orchestration layer for building applications that run on fault-tolerant quantum computers, and it arrives with two numbers that are interesting. Fermilab says the tool cut a fault-tolerant algorithm design cycle from five months to three weeks, and Iceberg Quantum used it to show that The post NVIDIA CUDA-Q Logical Debuts With a 7x Fermilab Speedup and a 10x Cut in Diraq's Qubit Estimate appeared first on StorageReview.com. ## Perplexity's new agent runs entirely on your GPU -- with one expensive catch DevFeed: [Perplexity's new agent runs entirely on your GPU -- with one expensive catch]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-14T18:21:44Z Content type: news Language: en Sources: [The New Stack]() Topics: [GPU](), [Nvidia](), [Windows](), [Orchestration](), [qwen](), [Cloud](), [browser](), [Ollama]() Tags: [ai-agents](), [ai-infrastructure](), [gpu](), [hardware](), [infrastructure](), [news](), [nvidia](), [ollama](), [orchestration](), [qwen](), [windows]() ### AI overview The article reports that Perplexity's Portable Computer, a local version of its Computer agent, is available in the Perplexity app for Windows on compatible Nvidia GeForce RTX and RTX PRO GPUs. It requires at least 24GB of VRAM and combines local models, orchestration, a browser, tool calling, and a proprietary SPACE sandbox. The article also discusses platform-specific engineering, external service connectors, and the boundary between local and cloud computing. ### Source excerpt Running an LLM on your PC is easy enough, but putting an agent to work there is a different story. The post Perplexity's new agent runs entirely on your GPU -- with one expensive catch appeared first on The New Stack. ## Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts DevFeed: [Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:23:21Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [Orchestration](), [GPU](), [Multi-tenancy](), [sglang](), [TensorRT](), [vllm](), [Security]() Tags: [ai](), [cache](), [concurrent](), [enterprise](), [gpu](), [inference](), [latency](), [nvme](), [orchestration](), [security]() ### AI overview Lightbits Labs is introducing Inferra, a KV cache orchestration engine for AI inference. It virtualizes GPU memory across DRAM and NVMe storage, preserving attention states for long-context and multi-session workloads. Lightbits claims up to 16 times more concurrent sessions, more than 100 times lower latency than recomputation, and context windows of up to 10 million tokens. Inferra supports vLLM, TensorRT, and SGLang and includes tiering, predictive prefetching, tenant isolation, and encrypted data transfer. ### Source excerpt Lightbits Labs, the company that invented NVMe over TCP, is moving into inference software with Inferra, a KV cache orchestration engine that makes its public debut tomorrow, September 15, at the AI Infra Summit in Santa Clara. The software virtualizes GPU memory across DRAM and NVMe storage tiers and turns the KV cache into a The post Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts appeared first on StorageReview.com. ## Configuring Management IP Addresses to Virtual Network Devices DevFeed: [Configuring Management IP Addresses to Virtual Network Devices]() Original publisher: [Read original article]() Published: 2026-09-14T05:29:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [networking](), [configuration](), [Orchestration](), [Vagrant](), [Cloud](), [ssh]() Tags: [cli](), [cloud](), [devices](), [dhcp](), [ip](), [ipv4](), [ipv6](), [management](), [netlab](), [network](), [networking](), [orchestration](), [server](), [ssh]() ### AI overview The article explains how to assign predictable management IP addresses to virtual network devices. It contrasts manual console-based setup with automated assignment through orchestration, containers, DHCPv4/DHCPv6, custom Vagrant boxes, and IPv6 mechanisms such as SLAAC. ### Source excerpt It goes without saying that if you want to configure (virtual) network devices with any semi-sane configuration mechanism1, the device must have a working IP address. Here's the time-honored method2 to assign an IP address to a virtual network device: Start the virtual machine (using a GUI)3 Open a new window: either a telnet session to the virtual console port or a full-blown virtual console (GUI) session. Manually configure the IP address, the SSH server, and the user credentials on the first interface. Read more ... ## Temporal raises $550M at a $12.55B valuation as demand grows for reliable AI infrastructure DevFeed: [Temporal raises $550M at a $12.55B valuation as demand grows for reliable AI infrastructure]() Original publisher: [Read original article]() Author: Allanah Hughes Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [AI Infrastructure](), [reliability](), [Orchestration](), [AI Platform]() Tags: [agents](), [ai](), [ai-infrastructure](), [announcements](), [funding](), [infrastructure](), [orchestration](), [outage](), [reliability](), [series](), [systems]() ### AI overview Temporal announces a $550 million Series E funding round at a $12.55 billion valuation. The company says the funding will support reliable infrastructure for long-running AI agents and applications, including orchestration and recovery across systems. ### Source excerpt AI is raising the bar for reliability. See why Temporal's $550M Series E, backed by Lightspeed and others, is built to meet that demand. ## GitHub Copilot's Project HydraFusion Promises Frontier Level Performance Through Multi-Model Routing DevFeed: [GitHub Copilot's Project HydraFusion Promises Frontier Level Performance Through Multi-Model Routing]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-13T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [GitHub Copilot](), [Model Routing](), [AI-assisted coding](), [Orchestration]() Tags: [agentic-coding](), [ai](), [ai-ml-data-engineering](), [benchmarks](), [development](), [frontier-model](), [github-copilot](), [github-hydrafusion](), [ml-data-engineering](), [model-routing](), [news](), [orchestration]() ### AI overview GitHub's Project HydraFusion research preview for Copilot orchestrates multiple models at runtime for coding tasks. Its single, cascade, and critique execution patterns aim to balance task quality, latency, and estimated cost. ### Source excerpt GitHub's Project HydraFusion is a research preview for GitHub Copilot that enhances coding intelligence through runtime model orchestration. It dynamically assembles execution plans using models from various providers. The system employs three execution patterns based on task complexity. Evaluations indicate that it achieves high task quality while significantly reducing operational costs. By Olimpiu Pop ## Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond DevFeed: [Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond]() Original publisher: [Read original article]() Author: Guillaume Laforge Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [Artificial Intelligence](), [Kotlin](), [AI Development](), [Android](), [Kotlin Multiplatform](), [multiplatform](), [Orchestration](), [Google](), [Persistence](), [Agent Skill](), [LiteRT](), [Cloud]() Tags: [agent-skill](), [agents](), [ai](), [ai-agent](), [ai-agents](), [android](), [building](), [database](), [development-kit](), [firebase](), [incident](), [kotlin](), [multiplatform](), [orchestration](), [persistence](), [production]() ### AI overview Google announces the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin, a production-ready toolkit for building multi-agent applications with Kotlin, Java, and Android. Built on Kotlin Multiplatform, it provides feature parity with the ADK 1.0 Core and adds Android-first extensions for on-device agents with LiteRT-LM and ML Kit, hybrid cloud workflows through Firebase AI Logic, and state persistence with Room and AppSearch. The release also includes type-safe, compile-time function calling through KSP and declarative agent skills. ### Source excerpt Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workflows and context compaction. Additionally, the release introduces a robust suite of Android-first extensions, allowing mobile developers to integrate local models via LiteRT-LM, cloud reasoning through Firebase AI, session persistence using Room, and semantic memory powered by AppSearch. ## Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations DevFeed: [Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations]() Original publisher: [Read original article]() Author: Meghana Ashok Published: 2026-09-11T18:26:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [ai observability](), [AWS IAM](), [incident](), [Orchestration](), [telemetry]() Tags: [advanced-300](), [agent](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [aws-identity-and-access-management-iam](), [best-practices](), [devops](), [incident](), [infrastructure-monitoring](), [logs](), [metrics](), [monitoring](), [orchestration](), [production](), [tracing]() ### AI overview The article describes monitoring production multi-agent systems with Amazon Bedrock AgentCore Evaluations for continuous quality assessment and AWS DevOps Agent for autonomous infrastructure incident investigation. ### Source excerpt Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Introducing automatic remediation policies with Cloudflare CASB DevFeed: [Introducing automatic remediation policies with Cloudflare CASB]() Original publisher: [Read original article]() Author: Abe Carryl Published: 2026-09-11T13:00:00Z Content type: release Language: en Sources: [Cloudflare Blog]() Topics: [Software as a service](), [Cloudflare One](), [Orchestration](), [Authorization]() Tags: [automation](), [casb](), [cloudflare](), [cloudflare-one](), [event-driven](), [oauth](), [orchestration](), [policy](), [reactive](), [saas](), [security](), [security-operations-center](), [zero-trust]() ### AI overview Cloudflare introduces CASB policies that automatically remediate SaaS security findings, including revoking risky file shares and sending webhooks after a finding is detected. ### Source excerpt Cloudflare CASB policies introduce a native automation engine built directly on the Cloudflare developer platform to remediate SaaS risks automatically. Security teams can now design event-driven logic to revoke risky file shares and send webhooks without manual intervention. ## How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory with PHP DevFeed: [How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory with PHP]() Original publisher: [Read original article]() Author: Amanda Lange, Matthew Setter Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Composer](), [Large Language Model](), [context](), [PHP](), [Tutorial](), [Orchestration](), [OpenAI]() Tags: [agent](), [ai](), [api](), [code](), [context](), [conversation-memory](), [developer-insights](), [environment-variables](), [how-to](), [llm](), [logging](), [ngrok](), [orchestration](), [php](), [phpstorm](), [voice-api]() ### AI overview This tutorial shows how to build a PHP service with Open Swoole that uses Twilio Conversation Memory to preserve caller context, preferences, and action history across separate inbound calls. It covers the required accounts, tools, environment variables, Composer setup, OpenAI integration, logging, and Memory Store configuration. ### Source excerpt In this tutorial you'll make a PHP service using Open Swoole that retains caller context, preferences and action history across multiple separate inbound calls. ## Building Pinterest's VLM Serving Stack on NVIDIA Dynamo DevFeed: [Building Pinterest's VLM Serving Stack on NVIDIA Dynamo]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-10T23:08:16Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [vlm](), [Inference](), [model-deployment](), [Orchestration](), [Latency](), [Caching](), [Open Source Models & Datasets]() Tags: [blackwell](), [cache](), [dynamo](), [embeddings](), [engineering](), [inference](), [latency](), [llm](), [multimodal](), [multimodal-ai](), [nvidia](), [nvidia-blackwell](), [open-source](), [orchestration](), [performance](), [pinterest](), [vllm](), [vlm](), [vlm-serving]() ### AI overview Pinterest describes its VLM serving stack built on NVIDIA Blackwell GPUs and NVIDIA Dynamo. The stack addresses multimodal inference demands such as image processing, variable prefill costs, KV-cache pressure, routing, and cache offloading. ### Source excerpt Lei Pan | Senior Software Engineer; Salina Wu | Senior Software Engineer; Cristian Lopez | Software Engineer I; Guangtong Bai | Staff Software Engineer; Soam Acharya | Principal Engineer; Saurabh Vishwas Joshi | Principal Engineer; Chia-Wei Chen | Staff Software Engineer; Ambud Sharma | Principal Engineer Why VLM Serving Matters at Pinterest Pinterest is a visual search and discovery platform, so its AI systems must reason over both language and visual content. Vision-language models (VLMs), which can interpret images, compare visual candidates, and respond naturally to user intent, are becoming the foundation for the next generation of Pinterest experiences: Pinterest Assistant, hybrid search, multimodal reranking, content understanding, signal generation, content safety, and more. This direction also reflects Pinterest's broader strategy to customize open-source models to meet its product & scale needs. Pinterest Assistant is a standout example. This multi-turn conversational experience covers both user language and visual content. Serving it requires low-latency VLM inference over rich multimodal context as well as reworking Qwen3-VL with proprietary multimodal embeddings to cut runtime cost while improving performance. Serving VLMs, however, introduces more challenges compared to text-only LLM workloads. Requests may carry multiple images, require extra vision encoder computation, incur larger and more variable prefill cost, and create higher KV cache pressure. To support this new class of models & product experiences, we built Pinterest's VLM serving stack on top of NVIDIA Blackwell GPUs and NVIDIA Dynamo. Blackwell GPUs incorporate many architectural innovations that are uniquely positioned for today's most demanding AI workloads -- including higher BF16/FP8 compute throughput, increased memory bandwidth, and larger HBM memory capacity -- that enable dramatically higher performance for inference. Dynamo provides a distributed inference orchestration layer that g [Next page]()