Observed Signal · Sep 28, 2026 · Market Signal · Source: JetBrains · Impact: 2/5
Air Teams: Bring Your Best Agentic Workflows to the Whole Team – and Automate Repeatable Work
Agentic workflows used to live on one developer's laptop. With Air Teams, you configure a workflow once, and the whole team can run, validate, and improve it.
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Agentic AI Workflows for Platform Engineering
This is Part 1 of a technical series explaining how to build agentic AI workflows for platform engineering teams. The author argues that improving developer velocity requires encoding team standards into the workspace (not just better prompts): steering files, skills, and agent definitions that provide persistent, role-specific context for AI agents. The post outlines a layered workspace model (e.g., a .kiro/ directory) that injects non-negotiable rules into every AI interaction, describes specialised agents for tasks like infrastructure authoring and security review, and details tool integrations (ticket trackers, CI/CD, AWS). The assumed stack includes AWS (multi-account), Terraform, GitLab CI, and AWS Secrets Manager. The article provides immediate starter steps (create a steering file and AGENTS.md) and previews later parts covering detailed steering files and GitOps/Kubernetes tooling.
GitHub Actions Becomes Agent Runtime
GitHub has opened Agentic Workflows in public preview, letting developers write natural-language workflow definitions in Markdown that compile to standard GitHub Actions YAML. These agentic workflows run through existing runner groups, organization policies, sandboxes, firewalls, output validation, and threat detection. GitHub also removed the need for long-lived personal access tokens for these workflows: they can use the built-in GITHUB_TOKEN, bill AI credits to the organization, and have per-run token caps. The article argues this design places AI agents inside established CI/CD governance — identity, permissions, billing, review and logging — making platform teams and organizational controls central. Early recommended use cases are low-risk, reviewable tasks (triage, analysis, docs checks) rather than broad autonomous code changes.
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