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Volume 342, Issue 5 · 6 articles
AWS previews the Well-Architected Agent, which recommends cost, security, performance and resilience fixes with code.
DevOps.com reports that AWS has released a preview of the AWS Well-Architected Agent, an AI agent that acts like a cloud architect by correlating metrics, resource configurations and application topology against best practices across more than 65 AWS services and an organization's stated business goals.
It highlights tradeoffs between cost, security, performance and resilience, and provides step-by-step remediation guidance, cost estimates and code deployable via CLI, infrastructure-as-code or runbook automation.
AWS Support VP Jill Fariss says the goal is to help short-staffed teams manage workloads at much greater scale.
Copado's Federico Larsen explains exposing DevOps platforms to AI agents via APIs, CLIs and MCP without losing controls.
Copado co-founder and CTO Federico Larsen tells DevOps.com's Alan Shimel that delivery platforms built around screens assume a human operator, which breaks down as developers delegate to coding agents in tools like Cursor and Claude Code, so Copado exposes capabilities through three layers: APIs, CLI commands, and packaged agent skills or MCP servers.
Broader agent access expands what teams must validate, including connected apps, IP ranges and agent behavior, with quality gates earlier in delivery.
Because agent output varies run to run, testing must check whether agents stay on task and follow requirements.
Futurum launches Futurum Media, combining Techstrong, Tech Field Day and Visible Impact under one umbrella.
In a DevOps.com post, Techstrong founder Alan Shimel announces Futurum Media, which brings Techstrong, Tech Field Day and Visible Impact together as a go-to-market execution business within The Futurum Group.
The new unit becomes the umbrella for Futurum's editorial properties and media services, backed by its research, buyer intelligence, testing and analyst expertise.
Shimel argues that as AI accelerates software and content production, technical depth, evidence, independent judgment and trusted practitioner communities matter more than ever.
Atlassian's Ming Wu says governed agent loops can automate SDLC work, but people must stay accountable for results.
In a DevOps.com interview with Alan Shimel, Ming Wu, head of engineering for Dev AI at Atlassian, argues that responsibility for agent-completed development tasks stays with people.
Wu describes governed agent loops, in which governance provides visibility, guardrails and enforcement while loops let agents process batches of work such as taking an issue, doing the work and reviewing a pull request.
Jira serves as the initial interface, with support for third-party coding agents and a shared context layer for priorities and business intent, as customers look to scale these workflows and prove real delivery gains.
A survey of 705 developers finds 42% say AI now writes at least half their code, shifting time to review and debugging.
A global survey of 705 developers and IT leaders found 42% report AI writes at least half their code, and only 21% spend more than half their week writing new code.
Developers report saving about 13 hours a week on coding, while spending more time reviewing (67%) and debugging (52%) AI output and about nine hours a week learning AI tools.
Half say accountability for AI-generated code sits with them personally and only 7% say ship decisions have been fully delegated to AI, while the article notes the rest of the delivery lifecycle has yet to catch up.
PeopleCert's new DevOps Institute book, The DevOps Standard, offers a vendor-neutral definition and operating model.
The lead contributor to 'The DevOps Standard,' a DevOps Institute book published by PeopleCert on October 1, 2026, explains its purpose on DevOps.com.
The book defines DevOps as a socio-technical system integrating people, process and technology for efficient, safe and reliable software delivery, and provides a vendor-neutral operating model to examine delivery across organizational boundaries, including AI-assisted work.
Using a retail inventory example, the author shows how teams can identify which capability needs attention and what evidence would demonstrate improvement beyond passing pipeline checks.
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