Continuous Delivery

Evidence (16)

• 5InfoQ·10/9/2026product_launch
  CircleCI Releases Machine Runner Orchestrator 1.0

  CircleCI has generally available its Machine Runner Orchestrator 1.0.0, which automates scaling of self-hosted machine runner virtual machines based on CI workload demand. The release is aimed at helping teams manage runner capacity more efficiently within their own infrastructure.

• 8GitHub Blog·10/6/2026framework_update
  GitHub Rebuilds Git Infrastructure for Agent-Scale Development

  GitHub says agentic software development is pushing its Git architecture beyond the needs of traditional human workflows, with far higher concurrent reads and writes in busy repositories. The company is rebuilding its Git infrastructure to improve commit latency, write throughput, merge handling, and repository operations for large teams and agents working in parallel.

• 7The New Stack·10/4/2026research
  AI Agents Turn CI into a Bottleneck

  The article argues that AI coding agents have sharply increased pull request and test volume, making continuous integration slower and more costly. It says faster runners and smarter test selection help, but the deeper issue is that CI only verifies a repository, not the full distributed system, so failures can still slip through between services.

• 4InfoQ·10/2/2026framework_update
  Managing Asynchronous APIs at Scale

  Ian Cooper describes practical approaches for operating asynchronous APIs in event-driven systems at scale. He covers the ABCs of messaging—Address, Binding, and Contract—and explains how discovery, governance, and provisioning can be handled with AsyncAPI, CloudEvents, schema registries, and automated CI/CD pipelines.

• 6InfoQ·10/1/2026research
  Uber’s SubmitQueue Keeps Monorepo Mainlines Green

  Dhruva Juloori describes how Uber keeps its large monorepos stable by using SubmitQueue to manage merges across 65,000+ monthly changes. The system combines binary speculation trees, conflict analysis, and machine learning to predict build outcomes and reduce CI load, cutting resource usage by 53% and PR landing time by 37%.

• 5The New Stack·9/10/2026open_source
  Using Executable Architecture to Stop AI-Driven Code Sprawl

  The article argues that AI code generators can create “comprehension debt” by producing code that works but violates architectural boundaries. It recommends enforcing architecture through executable tests in CI/CD, using tools such as pytest-archon, rather than relying on documentation and manual code review.

• 5Martin Fowler·9/1/2026research
  Fragments on AI prose, long-horizon agents, and CI with agents

  Martin Fowler highlights a tool for detecting common LLM prose patterns and notes research suggesting humans are poor at distinguishing AI-generated text from human writing. He also discusses NVIDIA’s AVO-based long-horizon agent work and Paul Stack’s argument that agent-driven development changes where verification should happen in CI pipelines.

• 4GitHub Blog·8/26/2026product_launch
  GitHub Copilot automates Dependabot pull request triage

  GitHub describes a Copilot app automation workflow that reviews open Dependabot pull requests, groups updates by risk, checks CI status, and produces a daily summary. The automation is designed to reduce repetitive maintenance work and let developers focus on higher-priority decisions or follow-up migration tasks.

• 4InfoQ·8/25/2026framework_update
  Continuous Delivery Practices for Foundational Platforms

  Ian Nowland explains why standard CI/CD approaches often fail for stateful core infrastructure and foundational platforms. He outlines practical methods such as progressive deployments, synthetic testing in production, and blast-radius reduction to improve release safety in complex systems.

• 6InfoQ·8/24/2026framework_update
  Microsoft Shifts AI Governance to Runtime Enforcement

  Microsoft described an AI governance architecture that spans nine governance domains and four functions: policy, control, visibility, and proof. The approach links governance policies to runtime enforcement, continuous evaluation, observability, identity, security, and audit evidence for AI applications and agents in production.

• 2InfoQ·8/24/2026product_launch
  Podcast on Brownfield Codebases, Mob Programming, and AI-Assisted Coding

  In this podcast, Asgaut Mjølne Söderbom and Ola Hast discuss how their software engineering practices have evolved beyond continuous deployment and pair engineering. They also describe experiments with Claude Code and why they view it as useful for many tasks, but not as a replacement for coding in brownfield codebases.

• 5InfoQ·8/8/2026research
  OpenAI Uses Always-On Agents to Keep ChatGPT Fast

  Martin Spier describes how agentic workflows at OpenAI have sharply increased the volume and pace of code changes. He says the company is using always-on AI agents to automate profiling, regression detection, and continuous optimization to preserve performance at global scale.

• 6The New Stack·8/5/2026framework_update
  Software companies shift toward internal dev tools

  The article argues that as AI generates more application code, software engineers will increasingly focus on building the platforms, guardrails, and feedback loops that control and support code generation. It frames this as an expansion of platform engineering, with internal developer platforms merging delivery infrastructure and agentic AI controls into one shared layer.

• 3InfoQ·8/4/2026framework_update
  Microservices Platforms and Team Topologies

  Chris Richardson explains how internal platforms and Team Topologies can speed up microservices delivery while reducing cognitive load on stream-aligned teams. He outlines six platform patterns spanning security, observability, build, and deployment, and highlights common pitfalls in platform engineering.

• 7The New Stack·7/27/2026research
  How Anthropic uses evals to guide product development

  Anthropic says evaluation suites have become the core artifact for building frontier AI products, replacing traditional product requirements documents for many teams. The company relies on representative eval sets, continuous testing, and hands-on model use to catch capability jumps, failures, and unintended behaviors as Claude evolves from chatbot to coding assistant.

• 6The New Stack·7/22/2026product_launch
  Harness launches AI Agent Development Lifecycle service for governed agent deployment

  Harness has launched its AI Agent Development Lifecycle (DLC) service to help teams ship AI agents through familiar governance, testing, and security pipelines. The company says the approach focuses on making the delivery pipeline deterministic with quality gates, eval scores, and full audit records, rather than trying to make agent behavior itself reproducible.